Display Device Neural Network Advertisement Timing
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Solution Overview
Problem
Existing image display devices lack the ability to effectively adjust advertisement content output based on user preferences and viewing situations, leading to suboptimal advertisement delivery in terms of timing and display region attributes.
Innovation Solution
An image display device equipped with neural networks that determine recommended times and attributes for advertisement content output by analyzing user log data, adjusting the advertisement display region accordingly to enhance user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If advertisement content is output in batches predetermined by broadcasters, then the device complexity is low, but the adaptability to user situations is poor
Solution Approach 1:
The system performs preliminary analysis of user situations and advertisement suitability before actual advertisement output. The processor determines whether it is an appropriate time for advertisement output and identifies suitable display regions in advance, based on analyzed user situation data, before the advertisement content is actually displayed.
Solution Approach 2:
The processor acts as an intermediary between the advertisement content source and the display device. It receives advertisement content, analyzes user situation data, determines optimal output timing and display regions, and then controls the display to output advertisements only in determined appropriate regions and times, mediating between content delivery and user experience.
2Ease of operation
If advertisement content is output without considering user preferences, then the ease of operation is high, but the user interest and engagement are low
Solution Approach 1:
The system performs self-service by automatically analyzing user situation data and determining optimal advertisement output timing and display regions without manual intervention. The processor autonomously evaluates user preferences and behavior patterns to decide when and where to display advertisements, making the system both easy to operate and effective.
Solution Approach 2:
The system uses feedback from user situation data analysis to continuously improve advertisement output decisions. By monitoring user behavior patterns, preferences, and interactions with the display device, the processor adjusts advertisement timing and positioning based on accumulated feedback, enhancing effectiveness while maintaining ease of operation.
3Productivity
If advertisement content is displayed without optimizing timing, then the loss of time is minimal, but the advertisement effectiveness is reduced
Solution Approach 1:
The system performs preliminary analysis of user situation data and determines optimal advertisement timing in advance, before actual advertisement output. This preliminary determination ensures that when advertisements are displayed, they appear at the most effective moments based on pre-analyzed user context, maximizing effectiveness without adding significant time loss.
4Adaptability or versatility
If advertisement content is displayed in fixed regions, then the device complexity is low, but the adaptability to user preferences is poor
Solution Approach 1:
The system dynamically determines advertisement display regions based on real-time analysis of user situation data. Instead of using fixed display regions, the processor identifies and selects optimal dynamic display regions that adapt to current user preferences and context, making the advertisement system both adaptable and manageable.
Data Source
AI summary
According to an embodiment, an image display device includes: a display; a memory storing one or more instructions; and a processor executing the one or more instructions stored in the memory, wherein the processor executes the one or more instructions: to determine whether it is a recommended time for outputting advertisement content, from a user's log data, based on a first trained model using one or more neural networks; to determine a recommended attribute of an advertisement display region from the user's log data, based on a second trained model using the one or more neural networks, when it is determined that it is the recommended time for outputting the advertisement content; and to adjust an attribute of the advertisement display region based on the determined recommended attribute and control the display to output the advertisement content in the attribute-adjusted advertisement display region.