Virtual Influencer Spectator Routing via Gameplay Prediction
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Solution Overview
Problem
Current gaming technologies lack effective methods to predict and direct spectators to interesting gaming activities in real-time, leading to suboptimal spectator experiences in cloud gaming environments.
Innovation Solution
Implementing a system that uses machine learning models to analyze gameplay metadata and spectator characteristics to predict future gameplay activity and direct spectators to relevant game sessions, with virtual influencers providing narration and recommendations to enhance the spectator experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If spectators view gameplay sessions without prediction and direction, then they can access any game session, but they experience suboptimal spectator experience and miss engaging moments
Solution Approach 1:
The system performs preliminary analysis of gameplay metadata to predict future gameplay activity before spectators arrive. Machine learning models pre-process gameplay data to identify sessions likely to contain engaging moments, allowing spectators to be directed to appropriate sessions without having to search or wait for interesting content to occur naturally.
Solution Approach 2:
The virtual influencer acts as an intermediary between the gameplay sessions and spectators. The virtual influencer analyzes gameplay metadata, predicts engaging content, and directs spectators to appropriate sessions through social media channels. This intermediary layer filters and recommends content, improving spectator experience while reducing the time spectators spend searching for engaging gameplay.
2Reliability
If the system directs spectators to specific game sessions based on prediction, then spectator satisfaction increases, but the system complexity increases
Solution Approach 1:
The system uses machine learning models that automatically analyze gameplay metadata and predict future gameplay activity without requiring manual intervention. The virtual influencer autonomously directs spectators to engaging sessions based on algorithmic predictions, reducing the need for complex manual curation systems while maintaining high spectator satisfaction.
Solution Approach 2:
The patent replaces manual content curation and spectator guidance with automated machine learning-based prediction systems. Instead of human editors manually selecting and directing spectators to interesting gameplay sessions, the system uses AI models to automatically analyze gameplay data and make predictions, simplifying the overall system architecture while improving reliability.
3Measurement precision
If the system monitors and analyzes all gameplay activity in real-time, then prediction accuracy improves, but computational resources increase
Solution Approach 1:
The system extracts only the most relevant features from gameplay metadata for analysis, rather than processing all raw gameplay data. The machine learning models focus on specific predictive features that are most strongly correlated with engaging gameplay moments, reducing computational resource consumption while maintaining or improving prediction accuracy through selective feature extraction.
Data Source
AI summary
A method is provided, including the following operations: monitoring gameplay activity in a plurality of game sessions, wherein the monitoring includes, for each game session, analyzing the gameplay activity in said game session to predict future gameplay activity in said game session; identifying one or more characteristics of a plurality of spectators interacting with a virtual character, wherein each of the spectators interacting with the virtual character accesses, over a network, a channel that is attributed to the virtual character; through the channel, providing access to the spectators to spectate a selected game session that is one of the plurality of game sessions, selected based on the predicted future gameplay activity and based on the characteristics of the plurality of spectators.