Live Event Prediction Overlay System
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Solution Overview
Problem
Existing technologies fail to enhance the visual presentation of live-action sporting events with personalized and dynamic auxiliary information, such as real-time statistics and predictions, that cater to individual viewer preferences, leading to a suboptimal viewing experience.
Innovation Solution
A computing system that integrates multiple content sources, machine learning models, and user preference data to provide real-time statistical data and predictions as overlays or companion applications during live sporting events, allowing for personalized and dynamic enhancements of the viewing experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional broadcasting is used for live-action sporting events, then the broadcast content is simple and easy to transmit, but the viewing experience is not enhanced with auxiliary information
Solution Approach 1:
The system segments the broadcasting workflow into distinct modules: event data reception unit, auxiliary information generation unit, and integration unit. This allows complex processing to occur in separate stages, managing system complexity while enabling enhanced viewing experiences through multiple independent functional components
Solution Approach 2:
The patent introduces an intermediary processing system that sits between the traditional broadcast feed and the viewer's display. This intermediary generates and integrates auxiliary information (statistics, predictions, highlights) without disrupting the original broadcast signal, thereby enhancing the viewing experience while maintaining system compatibility
2Loss of information
If auxiliary information is added to enhance viewing experience, then the information completeness improves, but the information processing complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-generating auxiliary information such as team statistics, player profiles, and prediction models before the actual event occurs. This pre-processing approach ensures information completeness is achieved without overwhelming the real-time processing system, as much of the computational work is completed in advance
Solution Approach 2:
The system implements self-service mechanisms where the auxiliary information generation automatically pulls data from established databases and algorithms without requiring manual intervention. The prediction models and statistical engines operate autonomously, reducing processing complexity while maintaining comprehensive information delivery
3Productivity
If real-time predictions and statistics are provided, then the viewer engagement improves, but the data processing requirements increase
Solution Approach 1:
The system employs periodic action by updating auxiliary information and predictions at specific intervals rather than continuously processing every moment of the event. Statistics are refreshed at the end of plays or quarters, and predictions are generated at key decision points, reducing data processing energy requirements while maintaining high viewer engagement through timely information delivery
Data Source
AI summary
Live-action event data is received during a live-action event from an event reporting computing system via a computer network interface. The live-action event data is provided to a machine-learning prediction machine previously trained with previously-completed event data to output a prediction for an upcoming aspect of the live-action event. The prediction is sent to a client computing system via the computer network interface prior to commencement of the upcoming aspect to enhance a live-action event experience provided by the client computing system.