AI Content Personalization Using Object-Level Prompt Generation
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Solution Overview
Problem
Existing digital content delivery systems fail to personalize content effectively based on user preferences and context, leading to reduced user engagement.
Innovation Solution
An electronic device equipped with AI models identifies primary and additional objects in content, obtains personalized user information, and generates customized content by modifying these objects using input prompts, leveraging generative AI to create personalized outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital content is delivered using existing systems, then content delivery is achieved, but user engagement is insufficient due to lack of personalization
Solution Approach 1:
The system changes content parameters by modifying objects in the content based on user profile parameters. The AI model adjusts attributes such as object characteristics, content style, and presentation format according to extracted user information, transforming generic content into personalized versions that match individual user preferences and context.
Solution Approach 2:
The system performs self-service by automatically extracting user information from provided content and using this information to generate personalized content outputs. The AI model autonomously processes user profiles, identifies relevant content elements, and creates customized content without requiring manual user input or intervention, enabling the system to serve itself in the personalization process.
2Reliability
If AI models are used to generate personalized content, then user engagement is enhanced, but computational complexity and processing requirements increase
Solution Approach 1:
The system segments the content processing task into distinct stages: extracting user information from content, analyzing user profiles, identifying relevant content objects, generating personalized content through AI models, and presenting the output. This segmentation allows each stage to be optimized independently and reduces the computational burden on any single processing component.
Solution Approach 2:
The system introduces an intermediary AI model layer that mediates between raw content input and personalized output. This intermediary component processes and transforms content based on user profiles, acting as a computational bridge that manages complexity by breaking down the personalization task into manageable processing steps through the AI model's structured approach.
Data Source
AI summary
According to an example embodiment, an electronic device may include: at least one processor, comprising processing circuitry; and a memory configured to store instructions, wherein at least one processor, individually and/or collectively, is configured to execute the instructions. The electronic device may identify a first object corresponding to a primary object and a second object corresponding to an additional object among a plurality of objects included in a first content. The electronic device may obtain personalized information related to a user from a second content. The electronic device may obtain at least one input prompt based on the first object, the second object, and the personalized information. The electronic device may provide, through a user interface, a third content which is output from a first artificial intelligence (AI) model by providing the at least one input prompt to the first AI model. The third content may include a third object resulting from changing of at least a part of the second object, and the first object.