Neural Network Advertisement Replacement System
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Solution Overview
Problem
Conventional broadcast advertisements lack personalization and targeting effectiveness, as they are not tailored to the current state or preferences of individual users.
Innovation Solution
An electronic device that uses a learning model based on neural networks to acquire user state information from external devices and replace pre-determined advertisements with more relevant ones, considering user state and view history, to improve advertising effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional broadcast advertisements are output collectively, then the system is simple and easy to operate, but the advertisements lack personalization and targeting effectiveness
Solution Approach 1:
The patent segments the advertisement delivery system into multiple components: a determination unit that selects advertisements based on user information, a communication unit that transmits selected advertisements, and external devices that collect user data. This segmentation allows personalized advertisement delivery while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary determination unit that acts as a mediator between user data collection and advertisement delivery. This intermediary processes user information and selects appropriate advertisements, enabling personalized targeting without requiring direct complex interactions between all system components.
2Reliability
If user customized advertisements are output, then advertising effectiveness is improved, but the complexity of determining appropriate advertisements increases
Solution Approach 1:
The patent applies preliminary action by having the determination unit pre-select advertisements based on user information before transmission. User data is collected and processed in advance, and appropriate advertisements are determined beforehand, reducing the complexity of real-time decision-making and improving advertising effectiveness through targeted delivery.
Solution Approach 2:
The patent implements feedback mechanisms where user information is continuously collected from external devices and used to refine advertisement selection. The system learns from user responses and behavior patterns, improving advertising effectiveness over time while managing complexity through iterative optimization rather than complex upfront planning.
Data Source
AI summary
An electronic device and method for replacing and outputting an advertisement are provided. The electronic device includes: a memory storing at least one program; a communication unit configured to receive context data to be used to determine a state of a user, from at least one external device; and a processor configured to replace and output an advertisement by executing the at least one program, wherein the at least one program includes instructions to: acquire user state information indicating the state of the user from the received context data, based on a learning model using one or more neural networks; and perform control to replace a previously determined first advertisement with a second advertisement determined based on the user state information and to output the second advertisement.