User-Experience Content Generation with Caption-Guided Reconstruction
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Solution Overview
Problem
Existing AI-based content generation methods struggle with producing creative content that aligns with users' intentions due to unrefined languages or mismatched user needs during training, leading to difficulties in generating content that reflects users' experiences and preferences accurately.
Innovation Solution
A platform server utilizing multiple learning models to process user input, including a first learning model for reconstructing content based on text, a second model for image captioning, and a third model for morphological analysis, to generate and refine content by integrating user feedback, thereby aligning with user experiences and intentions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If AI-based content generation models are trained with unrefined languages or mismatched information, then training data availability is improved, but content generation accuracy and alignment with user intentions deteriorate
Solution Approach 1:
The system performs preliminary text refinement and user need analysis before feeding data to the AI model. A text processing module refines unrefined languages, and a user need analysis module identifies mismatches, preparing high-quality training data in advance to improve both quantity and precision of content generation.
Solution Approach 2:
The patent introduces intermediary modules between raw data and the AI model, including a text refinement module that cleans unrefined languages and a user need analysis module that bridges user intentions with generated content. These intermediaries ensure training data quality while maintaining data availability.
2Manufacturing precision
If multiple learning models and processing modules are integrated to refine user input, then content generation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements multi-functional modules that perform multiple operations. The text processing module handles refinement, segmentation, and feature extraction. The user need analysis module simultaneously analyzes intentions, preferences, and requirements. This multi-functionality reduces the number of separate components needed.
Solution Approach 2:
The system merges multiple learning models (first learning model for text-to-content, second learning model for image captioning) into a unified platform. The text processing and user need analysis are combined in an integrated workflow, reducing system complexity while maintaining high accuracy.
3Reliability
If user feedback is incorporated to reconstruct sentences and refine content, then user intention alignment is improved, but processing time increases
Solution Approach 1:
The system implements a feedback loop where user feedback on generated content is collected and used to reconstruct sentences and refine future content generation. The feedback processing module analyzes user responses and adjusts the generation process, improving alignment with user intentions through iterative refinement.
Solution Approach 2:
The system performs preliminary analysis of user feedback and pre-processes reconstruction suggestions before full content regeneration. By preparing refinement options in advance based on feedback patterns, the system reduces the time required for complete re-generation while maintaining high alignment accuracy.
Data Source
AI summary
A platform server for generating content based on user experience includes: a memory including a first learning model trained to generate a reconstructed content, based on a text, and a processor to communicate with the memory and to control the first learning model to output at least one reconstructed content corresponding to the text when the text is input from a user terminal. The processor is configured to receive an input content and a first text matching the input content from the user terminal, generate a bag of words, based on the first text, determine caption data using a second text derived from the first text included in the bag of words and a predetermined sentence structure, input a sentence indicated by the caption data to the first learning model, generate at least one reconstructed content corresponding to the sentence, connect the at least one reconstructed content with the input content, and output the at least one reconstructed content and the input content to the user terminal.