Interactive Media Model Augmentation for Real-Time User Adaptation
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Solution Overview
Problem
Existing machine-learning models for generating media are not flexible enough to generate interactive content tailored to specific users or user groups, lacking the ability to dynamically adapt and respond to user inputs in real time.
Innovation Solution
Augmented machine-learning models that utilize interaction data to generate and modify media in real time, incorporating user-specific context and interaction data to create interactive media elements, such as images, audio, and video, through neural networks and other models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional machine-learning models are used for media generation, then the system is simpler and easier to implement, but the model lacks flexibility and cannot dynamically adapt to user interactions in real time
Solution Approach 1:
The system segments the media generation process into multiple independent components: a base machine-learning model for general media generation, an interaction data processing module for capturing user inputs, and an augmentation module for real-time adaptation. This segmentation allows each component to specialize in one function, improving overall flexibility without proportionally increasing complexity.
Solution Approach 2:
The patent implements dynamic adaptation by continuously updating the training dataset with new interaction data during runtime. The model transitions from a static pre-trained state to a dynamic system that adapts its parameters based on real-time user interactions, enabling flexible response to different users while maintaining a manageable complexity through incremental updates rather than complete retraining.
2Manufacturing precision
If interaction data is collected and processed in real time, then the media generation becomes more personalized and contextually relevant, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing interaction data and maintaining an augmented training dataset in advance. User interaction patterns are captured and pre-organized into structured formats during idle periods, so that when real-time personalization is needed, the model can quickly retrieve and apply pre-processed data rather than processing raw interactions from scratch, reducing latency while maintaining personalization accuracy.
Solution Approach 2:
The patent implements continuous learning where the model processes interaction data incrementally without stopping the media generation service. The training dataset is continuously updated with new interactions in the background, allowing the system to maintain personalized output while avoiding complete retraining cycles that would cause significant downtime or performance degradation.
3Reliability
If the training dataset is continuously updated with new interaction data, then the model improves its performance and adaptability, but the data management and model maintenance become more complex
Solution Approach 1:
The system implements feedback loops where user interactions are continuously monitored, processed, and fed back into the training dataset. This automated feedback mechanism ensures the model continuously improves its performance based on actual user behavior while reducing manual data management complexity through systematic, rule-based data collection and integration processes.
Solution Approach 2:
The patent enables the system to self-manage its training dataset by automatically collecting interaction data from various sources, preprocessing it according to defined schemas, and integrating it into the training corpus without extensive manual intervention. This self-service approach to data management improves model performance through continuous learning while keeping operational complexity manageable through automation.
Data Source
AI summary
A computing device may receive interaction data characterizing an interaction between a client device and a media element. The computing device may generate a training dataset using the interaction dataset and characteristics associated with the client device. A machine-learning model may execute using a feature vector derived from the training dataset. The machine-learning model defines a context associated with the interaction with the media element and generates an interaction response based on the context. The interaction response may be presented by the client device. Upon receiving subsequent interaction data associated with the interaction response may cause the machine-learning model to define a new context associated with the subsequent interaction data and the interaction response. The computing device may update the update the training dataset using the new context. The updated training dataset can cause a subsequent execution of the machine-learning model to generate modified media elements.