AI Generative Model Refinement via Feedback Loop
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Solution Overview
Problem
Existing generative models lack a scalable and adaptive feedback loop to refine digital component generation rules, leading to inefficiencies and resource wastage due to the disconnect between performance data and generation processes across varying locations and times.
Innovation Solution
Implement a feedback loop within an AI system that refines generative models using performance data, evaluation results, classification results, and user feedback to identify high-quality digital components, allowing for continuous training and adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If generative models generate digital components without feedback loops, then generation speed is maintained, but quality and adaptability deteriorate
Solution Approach 1:
The patent implements feedback loops that collect performance data, user feedback, and evaluation results from generated digital components, feed this information back into the generative model, and continuously refine the model's generation rules. This enables the system to learn from actual performance and improve quality over time without requiring complete system redesign.
Solution Approach 2:
The generative model performs self-refinement by automatically processing its own outputs through evaluation systems, learning from performance data and user feedback to update its generation rules. This self-service mechanism allows the model to improve its own quality without external intervention for each individual improvement step.
2Manufacturing precision
If multiple candidate digital components are generated and evaluated, then quality improves, but resource consumption increases
Solution Approach 1:
The system generates multiple candidate digital components and systematically evaluates them using performance data, user feedback, and evaluation results. The feedback from this evaluation process is used to refine the generative model, enabling more efficient resource allocation in future generation cycles by learning which generation parameters produce highest-quality outputs.
Solution Approach 2:
The system discards low-performing digital components after evaluation while recovering and reusing the valuable feedback information from both successful and unsuccessful candidates to improve future generation processes. This transforms what would be wasted computing resources into useful learning data.
3Adaptability or versatility
If generative models operate without continuous refinement, then system simplicity is maintained, but adaptability to varying locations and times deteriorates
Solution Approach 1:
The generative model transitions from static generation rules to dynamic, continuously evolving rules that adapt to changing conditions across different locations and times. The model processes feedback data to update its generation parameters in real-time, enabling it to respond to contextual variations while maintaining a manageable refinement process through automated learning mechanisms.
Data Source
AI summary
One example method includes receiving, by an artificial intelligence (AI) system, a query; generating, by the AI system and based on the query, a plurality of candidate digital components using a machine learning model; obtaining, by the AI system, user feedback associated with the plurality of candidate digital components, each user feedback indicating a user preference level of a corresponding candidate digital component; obtaining, by the AI system, performance data indicating an acceptance level of each candidate digital component of the plurality of candidate digital components; identifying, by the AI system and based on the user feedback and the performance data, a candidate digital component of the plurality of candidate digital components; generating, by the AI system and based on the candidate digital component, training data; and refining, by the AI system, the machine learning model using the training data.