Neural Network Video Customization for User Engagement

The patent introduces an intermediary system comprising behavior tracking modules, neural network models, and video generation modules that mediate between the user and the video content. This intermediary layer analyzes user behavior through tracking modules, processes data through trained neural networks, and generates customized videos, thereby enabling video customization without directly modifying the core video playback infrastructure.

The system is divided into distinct functional modules: behavior tracking module for collecting user data, neural network module for processing and analyzing behavior patterns, and video generation module for creating customized content. This segmentation allows each module to be developed, tested, and optimized independently, reducing overall system complexity while enabling sophisticated video customization capabilities.

The neural network models are trained in advance using historical user behavior data before actual video customization is needed. This preliminary training phase allows the system to pre-learn user preferences and behavior patterns, so that during actual video delivery, the system can quickly select optimized video variants without performing complex real-time analysis, thereby reducing computational energy consumption during production.

The system creates simplified copies or representations of user behavior patterns through the trained neural network models. Instead of processing raw behavioral data repeatedly, the system uses the trained model copies that capture essential user preferences, enabling fast video variant selection with minimal computational resources during actual video delivery.

The system extracts only the most relevant and significant behavior tracking features and metrics that have the highest correlation with user preferences and video engagement. By filtering out redundant or less important data points, the system maintains high customization accuracy while reducing the volume of data that needs to be processed, thereby minimizing processing time and computational overhead.

The neural network models dynamically adjust the weight and importance of different behavior tracking parameters based on their predictive value for user preferences. Parameters that show higher correlation with successful video engagement are given greater weight in the customization algorithm, allowing the system to focus processing resources on the most impactful data points and achieve accurate customization with reduced processing time.