Machine Vision Metadata for Game Event Video Sharing
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Solution Overview
Problem
Existing systems fail to efficiently identify and describe game events and objects in electronic gaming videos for improved social media sharing, leading to reduced visibility and engagement.
Innovation Solution
A system utilizing machine learning and machine vision to detect and classify game events and objects, suggesting precise and community-friendly descriptions based on social media metrics and game-specific terminology for enhanced sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual description and tagging of game videos is used, then users can share content on social media, but the process is time-consuming and lacks precision in identifying game events and objects
Solution Approach 1:
The patent replaces manual mechanical tagging and description processes with an automated machine learning system that uses computer vision and natural language processing to automatically identify game events, objects, and generate descriptions, thereby eliminating the time-consuming manual effort while improving accuracy
Solution Approach 2:
The system enables videos to automatically generate their own descriptions and tags through AI-powered analysis of game footage, allowing the content to describe itself without human intervention and freeing users from the manual tagging task
2Ease of operation
If generic descriptions are used for game videos, then sharing is simple, but visibility and engagement on social media platforms are reduced
Solution Approach 1:
The system analyzes social media performance data from comparison videos to learn which descriptors and tagging strategies generate higher engagement, then applies this feedback to automatically optimize descriptions for new videos, creating a continuous improvement loop that maintains simplicity while enhancing visibility
Solution Approach 2:
The system dynamically adjusts description parameters such as hashtag selection, title formulation, and tag prioritization based on learned social media performance patterns, transforming generic descriptions into optimized content that maximizes engagement without requiring manual intervention
3Measurement precision
If extensive manual analysis of comparison videos and social media metrics is performed, then optimal descriptors can be identified, but the complexity and resource requirements increase significantly
Solution Approach 1:
The patent creates a universal machine learning model that performs multiple functions simultaneously: analyzing comparison videos, evaluating social media metrics, identifying game events and objects, and generating optimized descriptors, thereby achieving high precision through a single integrated system rather than multiple separate complex processes
Data Source
AI summary
A method of assisting video information sharing includes, at a server computer, obtaining a comparison video including a plurality of frames from a social media platform and determining a presence of at least one event in the comparison video. The method further includes obtaining social media metrics for the comparison video from the social media platform and evaluating a description of the comparison video. The method further includes identifying at least one descriptor in the description correlated to the at least event in the comparison video and recording the descriptor in an application module.