Procedure Content Processing for Easy Surgical Video Sharing
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Solution Overview
Problem
Not all segments of the population are equally skilled in using social media and content distribution, with younger and/or more technical individuals being more adept at sharing content compared to older/less technical individuals.
Innovation Solution
A computer-implemented method and system that enables users to select, edit, and process raw procedure content, such as medical procedures, by selecting sub-portions, adding overlay material, and sharing processed content privately, semi-publicly, or publicly, using cloud-based storage and machine learning for efficient content distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users manually edit and process surgical videos using traditional video editing tools, then content distribution capability is improved, but ease of operation deteriorates due to technical complexity
Solution Approach 1:
The system enables surgical videos to automatically generate captions, highlights, and shareable content without requiring manual editing by users. The AI-powered processing automatically identifies key moments, generates descriptions, and prepares content for distribution across multiple platforms, allowing non-technical users to benefit from advanced content creation capabilities.
Solution Approach 2:
Traditional manual video editing processes are replaced with automated AI-powered processing. The system uses machine learning algorithms to automatically analyze surgical videos, extract meaningful content, generate captions, and prepare formatted output for distribution, eliminating the need for users to manually manipulate video editing tools.
2Adaptability or versatility
If comprehensive video editing features are provided, then content processing capability is improved, but device complexity increases
Solution Approach 1:
The content processing system is divided into specialized AI modules that handle different tasks independently: one module generates captions, another identifies highlights, another formats content for different platforms, and another manages distribution. This modular architecture provides comprehensive processing capability while keeping each individual component manageable and specialized.
Solution Approach 2:
The system provides a unified AI-powered processing platform that handles multiple content creation tasks (captioning, highlight detection, formatting, distribution preparation) through a single integrated interface, eliminating the need for users to manage multiple separate tools while maintaining comprehensive processing capability.
3Productivity
If automated processing is implemented, then productivity is improved, but measurement precision may deteriorate due to loss of manual control
Solution Approach 1:
The system incorporates feedback mechanisms where AI-generated content (captions, highlights) is reviewed and validated against the original video content. The system provides users with the ability to review and adjust AI-generated content before final distribution, ensuring accuracy while maintaining the productivity benefits of automated processing.
Solution Approach 2:
The system performs preliminary AI-powered analysis and content generation, then presents the processed content to users for final review and approval before distribution. This approach allows automated processing to handle the bulk of content creation work while preserving manual oversight for quality assurance on critical elements.
Data Source
AI summary
A computer-implemented method, computer program product and computing system for enabling a user to select raw procedure content for processing; enabling the user to select one or more sub-portions of the raw procedure content for inclusion within processed procedure content; and processing the one or more sub-portions of the raw procedure content to generate the processed procedure content.