Predictive Data Segmentation for Motor Racing Networks
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Solution Overview
Problem
Current systems fail to provide determinative and predictive information effectively in motor racing, leading to confusion and lack of engagement for fans and teams, despite the abundance of raw data, due to overwhelming and unstructured data presentation, especially on mobile devices and second-screen interfaces.
Innovation Solution
A system that processes and displays derived and predictive data through a network of nodes, including race cars, pit boxes, and fan devices, using processors to calculate probabilities of race events and transmit this information to displays, enhancing user engagement and understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If more raw data is provided and presented in visually stimulating ways, then the entertainment value and data availability are improved, but the data becomes overwhelming and confusing to users
Solution Approach 1:
The patent segments the overwhelming raw data into structured categories including real-time race data, historical data, predictive analytics, and contextual information. Each category is further divided into specific metrics (e.g., driver performance, car telemetry, weather conditions) that can be independently analyzed and displayed, transforming the data deluge into organized, digestible information units.
Solution Approach 2:
The patent introduces multiple intermediary layers between raw data and user presentation, including data processing systems that filter and validate information, analytical engines that derive meaning from raw metrics, and presentation systems that translate complex data into intuitive visualizations. These intermediaries act as mediators that preserve data integrity while making information accessible and comprehensible to end users.
2Productivity
If real-time predictive analytics are calculated and transmitted to multiple devices, then user engagement and decision-making capability are improved, but network bandwidth and system resources are consumed
Solution Approach 1:
The patent implements local quality by customizing data transmission and processing based on specific user needs, device capabilities, and contextual factors. Different user roles (fans, teams, broadcasters) receive tailored data sets with varying levels of detail and frequency. Mobile devices receive optimized subsets of data compared to desktop systems, and data transmission frequency adjusts based on race intensity and user engagement levels, reducing unnecessary network traffic while maintaining engagement.
Solution Approach 2:
The patent applies partial action by transmitting only the most critical predictive analytics and race information in real-time, rather than complete data sets. Less critical information is updated at lower frequencies or provided on-demand. This selective transmission approach maintains user engagement with essential information while significantly reducing network bandwidth consumption compared to transmitting all available data continuously.
3Measurement precision
If complex predictive computations are performed to determine race outcomes, then the accuracy of predictions is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing historical performance data, statistical models, and predictive algorithms before race events. These pre-processed data structures and computational models are ready for rapid deployment during races, eliminating the need to build analytical frameworks from scratch in real-time. This allows complex predictive computations to execute faster while maintaining high accuracy.
Solution Approach 2:
The patent implements dynamic computational strategies that adjust processing intensity based on race conditions and user needs. During stable race phases, computations run at lower intensity with longer intervals. During critical moments (overtakes, pit stops, cautions), computational frequency and depth increase automatically. This dynamic approach optimizes the balance between prediction accuracy and processing time, allocating computational resources efficiently based on actual race dynamics.
Data Source
AI summary
Networks, systems and displays for providing derived data and predictive information for use in multivariable component systems and activities; and in particular for use in motor racing such as in NASCAR®, Indy Car, Grand-Am (sports car racing), and/or Formula 1® racing. More particularly, there are systems equipment and networks for the monitoring and collecting of raw data regarding races, both real time and historic. This raw data is then analyzed to provide derived data, predictive data, virtual data, and combinations and variations of this data, which depending upon the nature of this data may be packaged, distributed, displayed and used in various setting and applications.