Machine-Learning Sports Graphics for Personalized Interactive Displays
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for generating sports media content are inefficient and lack personalization, often relying on manual techniques that do not effectively integrate user data with real-time sports event data to provide relevant and engaging content.
Innovation Solution
Utilizing machine learning models and templates to generate sports content streams that include user-specific, interactive elements, such as sports information cards, based on real-time event data and user interactions, allowing for dynamic and personalized content generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual techniques are used to generate sports media content, then content can be created, but the process is inefficient and lacks personalization
Solution Approach 1:
The system automatically generates sports media content by having the computing system receive game data, detect trigger events, generate graphics through machine learning models, and assemble final content without manual intervention. The system serves itself by autonomously completing the entire content generation workflow from raw data to personalized media output.
Solution Approach 2:
The system changes parameters by using machine learning models to dynamically adjust content generation based on user data and real-time game data. Templates are selected and customized based on detected trigger events and user preferences, allowing the same base system to produce highly personalized content variations efficiently.
2Ease of operation
If generic sports content is provided to all users, then content delivery is simple, but user engagement is reduced due to lack of relevance
Solution Approach 1:
The system applies local quality by customizing content specifically for each user based on their data and preferences. Instead of uniform generic content, the machine learning models generate personalized graphics and select templates tailored to individual user interests while maintaining the same underlying delivery infrastructure.
Solution Approach 2:
The system performs preliminary action by pre-processing user data and game data, pre-selecting relevant templates and graphics before content assembly. This advance preparation enables personalized content generation without complicating the actual delivery process, as the customization work is completed beforehand.
3Use of energy by moving object
If manual content generation techniques are used, then resource consumption can be managed, but the accuracy and relevance of displayed information is limited
Solution Approach 1:
The system replaces manual mechanical content creation processes with automated machine learning models and algorithms. The computing system automatically detects trigger events, generates graphics, and assembles content, substituting human manual operations with computational processes that improve accuracy while managing resource usage through automation.
Data Source
AI summary
A method may include receiving data for a game, the data comprising at least tracking data or event data. The method may include determining an occurrence of a trigger event within the game based on the data for the game, and providing the data for the game and the trigger event to a machine learning (ML) model. The ML model may be trained to generate a graphic based on the data for the game and the occurrence of the trigger event. The method may include receiving, from the ML model, the graphic based on the data for the game and the occurrence of the trigger event; and generating, using a template, a visual element including the graphic for presentation within a user interface. The visual element may be associated with a marker, the marker representing a recommended position for an interactive element to be presented within the user interface.