ML Score Generation for Dynamic Content in iGaming
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Solution Overview
Problem
Existing digital interactive platforms in the sports and iGaming industry lack dynamic and engaging content, leading to user boredom and decreased interest due to repetitive pre-stored sports-related content, without utilizing machine learning models to enhance user experience.
Innovation Solution
A computer-implemented system and method that generates a score for a machine learning model to optimize user experience in digital interactive platforms. This system includes subsystems for video content generation, media content selection, feedback obtaining, score generation, and media content generation, using feedback from users to dynamically select and generate engaging animated and video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-stored sports related contents are played in digital interactive platforms, then the platform can operate with existing content, but user boredom and decreased interest occur due to repetition
Solution Approach 1:
The system dynamically selects and generates media contents based on real-time user feedback and machine learning models, transitioning from static pre-stored content to dynamic adaptive content delivery. The media content selection subsystem continuously adjusts content choices based on user actions and feedback loops.
Solution Approach 2:
The feedback obtaining subsystem captures user responses to media contents and feeds this information back to the machine learning model and content selection subsystem. This closed-loop feedback mechanism enables the system to learn from user preferences and adjust content delivery accordingly, preventing repetition and boredom.
2Productivity
If machine learning models are integrated to provide dynamic content, then user engagement increases, but system complexity and computational requirements increase
Solution Approach 1:
The system is divided into specialized subsystems: video content generating subsystem, media content selection subsystem, feedback obtaining subsystem, score generation subsystem, and media content generating subsystem. Each subsystem handles specific functions, making the complex ML integration manageable and modular.
Solution Approach 2:
The score generation subsystem acts as an intermediary between the feedback obtaining subsystem and the media content selection subsystem. It processes user feedback into actionable scores that guide content selection, simplifying the interaction between complex components.
3Ease of operation
If repetitive pre-stored content is used, then content delivery is simple, but user interest decreases over time
Solution Approach 1:
The system changes key parameters of content delivery by using machine learning models to dynamically select media contents based on user feedback. Instead of delivering fixed pre-stored content, the system adapts content parameters (selection, timing, type) based on real-time user behavior and preferences.
Data Source
AI summary
A computer-implemented system for generating a score for a machine learning (ML) model to optimize user experience in digital interactive platforms, is disclosed. The computer-implemented system is configured to: (a) select animated media contents and second video contents to be played dynamically corresponding to user actions performed by users, in video contents; (b) obtain feedbacks from user devices of the users on the animated media contents and the second video contents played corresponding to the user actions performed by the users; (c) generate the score for the ML-model based on the feedbacks obtained from the user devices of the users on the animated media contents and the second video contents; and (d) generate second animated media contents and third video contents based on the score generated for the machine learning model, to optimize the user experience in the digital interactive platforms.