Gameplay Hash Detection for Real-Time Co-Player Identification
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Solution Overview
Problem
Current technologies lack dynamic solutions for detecting common gameplay among multiple players in real-time, especially in livestream and game clip distribution environments, making it difficult for users to identify and connect with co-players engaged in the same gameplay.
Innovation Solution
The method and system utilize image processing to generate hash values from designated portions of gameplay images, which are then compared to hash values stored in a central database to electronically detect users engaged in common gameplay, without requiring manual identification of gaming identities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual identification of gaming identities is used, then users can identify co-players, but the process requires active user knowledge and manual sharing of identities
Solution Approach 1:
The system automatically detects common gameplay and identifies co-players without requiring manual user input. The hash value comparison occurs autonomously between the user's gameplay data and the database, eliminating the need for users to actively search for or share identity information.
Solution Approach 2:
A central database serves as an intermediary between users and co-player identification. Instead of users directly exchanging identity information, the system mediates the process by storing hash values and performing automated comparisons to identify common gameplay sessions.
2Adaptability or versatility
If real-time detection of common gameplay is implemented, then dynamic community fostering is achieved, but system complexity increases
Solution Approach 1:
The system extracts only the essential identifying elements (hash values from gameplay images) rather than processing entire gameplay streams. This extraction approach enables real-time detection while keeping computational requirements manageable.
Solution Approach 2:
The system transforms gameplay images into hash values, changing the parameter representation from complex image data to simplified numerical identifiers. This parameter transformation enables efficient real-time comparison and detection without requiring complex image processing of full gameplay sessions.
3Measurement precision
If hash values from entire gameplay images are processed, then comprehensive gameplay analysis is achieved, but processing time and computational resources increase
Solution Approach 1:
The system extracts only relevant portions of gameplay images to generate hash values, rather than processing entire images. This selective extraction maintains detection accuracy for common gameplay elements while significantly reducing processing time and computational overhead.
Solution Approach 2:
The system applies different processing quality levels to different parts of the gameplay data. By focusing hash generation on key identifying elements rather than entire images, the system achieves sufficient detection precision while optimizing processing efficiency.
Data Source
AI summary
The method and system electronically detects users engaged in common gameplay using hash values generated based on image processing of designated portions of gameplay image capture. Portions are based on the game, common display area visible to all players. For each player running the executable application, these hash values are generated on an intermittent basis and the hash values are sent via a network connection to one or more hash databases. The method and system compares hash values to determine matches. Based on this detection, the method and system determines additional user(s) engaged in the common gameplay.