Spectator Switchboard Viewport Selection via Machine Learning
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Solution Overview
Problem
Remote spectators of live gaming events often find it inefficient and uninteresting to navigate through numerous content options, leading to disengagement due to the complexity and lack of relevant content alignment with their preferences.
Innovation Solution
A system that uses machine learning models to dynamically select and present viewports based on spectator profiles and live event data, providing a customized switchboard interface with predicted interesting actions, allowing efficient navigation and automatic recording of non-selected content for later viewing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If spectators manually navigate through multiple content options in live gaming events, then they can access various viewpoints, but the complexity of navigation and time required to find interesting content increases
Solution Approach 1:
The system automatically selects and presents relevant viewports to spectators based on their profile data and real-time game event analysis, eliminating the need for manual navigation. The machine learning model performs self-service by autonomously curating content that matches spectator preferences, thereby reducing navigation complexity while maintaining adaptability.
Solution Approach 2:
The patent introduces an intermediary system comprising a machine learning model and switchboard interface that mediates between the available game content and the spectator. This intermediary automatically processes multiple content options and presents only the most relevant viewports, simplifying the spectator's task while preserving access to diverse viewpoints.
2Loss of information
If spectators manually search through content to find interesting scenes, then they can locate desired viewports, but the time required to find relevant content increases
Solution Approach 1:
The system performs preliminary action by pre-processing game event data and spectator profile information before the spectator needs to view content. The machine learning model analyzes upcoming game events and proactively identifies interesting scenes, preparing customized viewport recommendations in advance. This eliminates the need for spectators to manually search through content, significantly reducing search time while ensuring relevant content is delivered.
3Adaptability or versatility
If a customized switchboard interface is generated using machine learning models, then viewport relevance to spectator preferences is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex system into distinct functional modules: a machine learning model for analyzing spectator profiles and game events, a switchboard interface for presenting viewports, and separate processing components for different types of data. This segmentation manages system complexity by organizing functions into independent, manageable units while maintaining high adaptability and personalization capabilities.
Data Source
AI summary
Methods and systems are provided for selecting viewports into a game. One example method includes identifying a plurality of virtual cameras for providing viewports into the game. The method includes accessing a playbook of a spectator user, and the playbook identifies performance of the spectator user in the game. The method includes accessing event data for the game as the game is played live or recorded. The method includes generating a switchboard interface for the spectator user including a plurality of viewports providing optional views into the game. The plurality of viewports is dynamically selected based on processing the event data and the playbook of the spectator user through a machine learning model. The machine learning model is configured to identify features from the event data and the playbook to identify attributes of the spectator user. The attributes of the spectator user are used to select the plurality of viewports into the game.