AI Model Update for Dynamic Ad Targeting
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Solution Overview
Problem
Existing electronic apparatuses fail to provide personalized and dynamic advertisement content to users, as they rely on static artificial intelligence models that do not account for real-time context and user feedback.
Innovation Solution
An electronic apparatus that updates its artificial intelligence model based on user context information, such as profile and usage history, and feedback information, to dynamically identify and display relevant advertisement content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a static artificial intelligence model is used for advertisement targeting, then the device complexity is reduced and ease of operation is improved, but the adaptability to user preferences and the relevance of advertisement content deteriorate
Solution Approach 1:
The patent implements dynamic updating of the artificial intelligence model by continuously incorporating user feedback information and retraining the model with new data. This transforms the static model into a dynamic system that adapts to changing user preferences while maintaining operational simplicity through automated update processes.
Solution Approach 2:
The system collects user feedback information regarding displayed advertisement content and feeds this back into the artificial intelligence model for continuous retraining. This feedback mechanism enables the model to learn from user interactions and improve its targeting accuracy over time without requiring manual intervention.
2Adaptability or versatility
If the artificial intelligence model is updated continuously with user feedback, then the adaptability to user preferences is improved and advertisement relevance is enhanced, but the device complexity and computational resources required increase
Solution Approach 1:
The artificial intelligence model performs self-updating by automatically collecting user feedback, retraining with new data, and deploying updated versions without requiring manual intervention. This self-service approach handles the complexity of continuous model updates internally while presenting a simple interface to users.
Solution Approach 2:
The system performs preliminary actions by pre-processing user feedback information and preparing training data before model retraining. This preliminary preparation simplifies the overall update process and reduces the computational burden during actual model updates by organizing data in advance.
3Speed
If the same data is input into the artificial intelligence model repeatedly, then the ease of operation is maintained and processing speed is preserved, but the advertisement content becomes repetitive and loses relevance to user preferences
Solution Approach 1:
The system maintains continuous useful action by constantly collecting fresh user feedback and continuously retraining the model with new data. This ensures the model never stagnates with repeated processing of the same data but continuously evolves to maintain relevance while preserving processing efficiency through incremental updates.
Data Source
AI summary
An electronic apparatus comprises: a display; a memory that stores a trained first artificial intelligence model; and at least one processor that acquires viewing group information of a user by inputting context information including profile information of the user and use history information of the electronic apparatus into the first artificial intelligence model, controls the display to display advertisement content identified on a basis of the obtained viewing group information, acquires feedback information of the user related to the displayed advertisement content, and updates the first artificial intelligence model to a second artificial intelligence model retrained on the basis of the input context information and the obtained feedback information.