Iterative AI Content Generation Guided by User Intent Similarity
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Solution Overview
Problem
Existing AI systems struggle to accurately generate content that conforms to user intentions, lacking efficient methods for iterative improvement and refinement based on natural language inputs.
Innovation Solution
A device and method utilizing a neural network model to iteratively generate output content by compositing input content into a target area of base content, adjusting attributes, and refining based on similarity calculations with natural language inputs, employing models like NLU, NN, and GAN to enhance content generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing AI systems generate content based on natural language input, then content generation is achieved, but the content does not accurately conform to user intentions
Solution Approach 1:
The patent implements a feedback mechanism where the generated content is compared against the original natural language input using similarity calculation. The system calculates similarity between the input and generated content, and if the similarity does not meet a threshold condition, it iteratively regenerates content until the condition is satisfied. This closed-loop feedback process ensures the generated content accurately reflects user intentions.
Solution Approach 2:
The system performs preliminary analysis of the natural language input to extract user intention information before generating content. By understanding the user's intent in advance through NLU models, the system can guide the content generation process to produce results that are more likely to conform to user expectations, rather than generating content and then checking for alignment.
2Measurement precision
If iterative generation process is implemented to improve content accuracy, then content quality improves, but processing time increases
Solution Approach 1:
The system performs a limited number of iterative generations rather than exhaustive searching. It generates content, checks similarity, and only continues iteration if the similarity condition is not met. This partial action approach balances quality improvement with time efficiency by stopping iteration once acceptable similarity is achieved, rather than pursuing perfect alignment that would consume excessive time.
Solution Approach 2:
The system changes parameters during the generation process by adjusting the generation conditions based on similarity results. When similarity is insufficient, the system modifies generation parameters and attempts regeneration. This dynamic parameter adjustment allows the system to optimize between quality and time by adapting its generation strategy based on real-time feedback.
Data Source
AI summary
A method of improving output content through iterative generation is provided. The method includes receiving a natural language input, obtaining user intention information based on the natural language input by using a natural language understanding (NLU) model, setting a target area in base content based on a first user input, determining input content based on the user intention information or a second user input, generating output content related to the base content based on the input content, the target area, and the user intention information by using a neural network (NN) model, generating a caption for the output content by using an image captioning model, calculating similarity between text of the natural language input and the generated output content, and iterating generation of the output content based on the similarity.