Electronic Arts Inc. (20240316469). DETECTING HIGH-SKILLED ENTITIES IN LOW-LEVEL MATCHES IN ONLINE GAMES simplified abstract
Contents
DETECTING HIGH-SKILLED ENTITIES IN LOW-LEVEL MATCHES IN ONLINE GAMES
Organization Name
Inventor(s)
Laura Greige of Boston MA (US)
Fernando De Mesentier Silva of San Francisco CA (US)
Alexander Sulimanov of San Francisco CA (US)
DETECTING HIGH-SKILLED ENTITIES IN LOW-LEVEL MATCHES IN ONLINE GAMES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240316469 titled 'DETECTING HIGH-SKILLED ENTITIES IN LOW-LEVEL MATCHES IN ONLINE GAMES
Simplified Explanation:
This patent application describes a system that can detect highly skilled players in low-level matches of an online game. The system identifies eligible entities, analyzes their gameplay data, detects anomalies in their performance, and matches anomalous entities with other skilled players.
- The system detects high-skilled entities in low-level matches of an online game.
- It identifies eligible entities and analyzes their gameplay data.
- Anomalies in performance are detected using anomaly detection techniques.
- Anomalous entities are matched with other skilled players by the matchmaking algorithm.
Potential Applications:
This technology could be used in online gaming platforms to ensure fair and balanced matches by identifying highly skilled players in low-level games.
Problems Solved:
This technology addresses the issue of mismatched skill levels in online gaming, providing a more enjoyable experience for all players involved.
Benefits:
The system improves the overall gaming experience by creating more balanced matches and reducing frustration caused by unfair matchups.
Commercial Applications:
Potential commercial applications include online gaming platforms, esports organizations, and game developers looking to enhance the matchmaking experience for their players.
Questions about the Technology:
1. How does this system impact the competitiveness of online gaming?
This system enhances the competitiveness by ensuring fair matchups and challenging games for all players involved.
2. What data is used to determine anomalies in player performance?
Gameplay data associated with the entities is analyzed to detect anomalies in player performance.
Original Abstract Submitted
a high-skilled-low-level detection system may detect high-skilled entities in low-level matches of an online gaming. the system may identify a plurality of entities that are within a first category of entities eligible to be matched by a matchmaking algorithm. the system may then determine respective feature sets based at least in part on gameplay data associated with the plurality of entities and perform anomaly detection on the respective feature sets. the system may then determine, based on the anomaly detection, an anomalous entity of the plurality of entities and cause the matchmaking algorithm to match the anomalous entity with other entities that are in a second category of entities.
