Parallel Reality Displays for Dynamic Crowd Partitioning
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Solution Overview
Problem
Sports stadiums face challenges in providing a personalized and engaging experience for fans, as conventional display systems do not effectively tailor content to individual viewers, leading to suboptimal fan engagement and revenue generation.
Innovation Solution
Implementing a cognitive learning-based system that utilizes neural networks and sensor data to dynamically partition crowds and deliver personalized content to sections of the audience through parallel reality displays, analyzing demographics, audio inputs, and crowd reactions to provide tailored experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional display systems are used in sports stadiums, then the system complexity is low, but the fan engagement and personalization capability are insufficient
Solution Approach 1:
The display system segments the audience into different partitions based on demographics, behavior, and preferences. Each partition receives customized content independently, enabling personalized fan experiences while managing complexity through modular content delivery to specific zones rather than the entire stadium.
Solution Approach 2:
The system applies local quality by providing different content, timing, and style to different spatial zones (partitions) of the stadium. Each partition receives content optimized for its specific audience characteristics, achieving high adaptability without requiring complete system redesign.
2Measurement precision
If data collecting devices and neural networks are deployed throughout the stadium, then the crowd classification accuracy is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-configuring viewing events, seating layouts, and relative viewing angles for each seat before the event occurs. This preparation reduces real-time processing complexity while maintaining high classification accuracy through pre-established data structures and models.
Solution Approach 2:
The neural network acts as an intermediary that processes raw data from multiple collecting devices and transforms it into actionable crowd partitions. This intermediary layer simplifies the complexity by providing a unified interface between diverse data sources and the content delivery system.
3Productivity
If personalized content is delivered to each partition simultaneously, then the fan engagement increases, but the computational power and processing speed requirements increase
Solution Approach 1:
The content delivery is segmented by partition, with each partition receiving independently optimized content. This segmentation allows parallel processing of content delivery to multiple partitions simultaneously, improving overall productivity while distributing computational load across different content streams rather than requiring monolithic processing power.
Data Source
AI summary
In an approach for dynamically adjusting parallel reality (PR) displays, a processor configures a viewing event. A processor receives data from data collecting devices located throughout a location of the viewing event. A processor classifies a crowd of the viewing event into at least two partitions using a learning-based neural network that ingests the data. A processor selects content to be displayed to each of the at least two partitions. A processor enables a PR display to simultaneously display the content to each of the at least two partitions.