Dynamic Advertisement Content Segmentation and Context Adaptation
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Solution Overview
Problem
Current advertisement content delivery methods lack dynamic personalization and adaptability to viewer context, such as time, location, and viewer preferences, resulting in inefficient information transfer and reduced engagement.
Innovation Solution
A method and apparatus that segment advertisement images based on time to select relevant parts, determine association data based on viewer characteristics and context, and combine these elements to create personalized advertisement content, which can be edited in real-time based on displayed conditions, using input data, viewer images, and peripheral images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If advertisement content is delivered using fixed, non-personalized methods, then the device complexity is low, but the adaptability to viewer context and engagement is poor
Solution Approach 1:
The advertisement image is segmented into multiple parts based on time, allowing different portions of the advertisement to be displayed at different times. This segmentation enables the system to adapt to viewer context without requiring complete redesign of the entire advertisement, thus improving adaptability while managing complexity
Solution Approach 2:
The advertisement content delivery system dynamically selects and combines different parts of the advertisement image based on real-time context information such as viewer characteristics, time, and location. This dynamic adaptation allows the system to respond to changing viewer contexts, improving versatility while using automated algorithms to manage system complexity
2Productivity
If advertisement content is personalized and context-aware, then viewer engagement and information transfer efficiency improve, but the processing time and system complexity increase
Solution Approach 1:
The advertisement image is pre-segmented into multiple parts before deployment, and association data is prepared in advance. When the advertisement is delivered, the system simply needs to select and combine the appropriate pre-prepared parts based on context information, significantly reducing real-time processing time while maintaining personalized content delivery
Solution Approach 2:
The system uses template-based association data that can be rapidly instantiated and combined with selected image parts. This copying approach allows the system to generate personalized advertisement content efficiently by reusing pre-defined templates and data structures, improving information transfer efficiency without proportionally increasing processing time
3Measurement precision
If the advertisement system collects and processes extensive context information, then the personalization accuracy improves, but the data processing complexity and time increase
Solution Approach 1:
The system extracts only the most relevant context information (such as viewer characteristics, time, and location) needed for advertisement personalization, rather than processing all available data. This selective extraction approach improves personalization accuracy by focusing on key parameters while reducing overall data processing complexity
Solution Approach 2:
Different levels of context information processing are applied to different aspects of advertisement content. Critical personalization parameters receive more sophisticated processing, while less important aspects use simpler processing methods. This local quality approach optimizes the balance between precision and complexity for each specific function
Data Source
AI summary
A method of providing advertisement content is provided. The method includes selecting at least some parts from among a plurality of parts of an advertisement target which are generated by segmenting an image of the advertisement target based on time, determining association data associated with the advertisement target, based on one or more of characteristics of the selected parts and context information indicating a condition where the advertisement content is displayed, and combining the selected parts and the determined association data to create the advertisement content.