Generative AI Causality Analysis for Process Optimization
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Solution Overview
Problem
Existing process optimization methods are inefficient due to the complexity of interactions among multiple variables, making it difficult to derive key factors and optimize processes effectively.
Innovation Solution
A method utilizing generative AI to analyze causality between variables, derive correlations, and generate hypotheses for process optimization, supported by logical grounds and simulation, with user-friendly interfaces for intuitive understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional optimization methods are used, then the process is simple to implement, but the optimization efficiency is low due to process complexity
Solution Approach 1:
The patent introduces generative AI as an intermediary system between the complex process data and the optimization goal. The AI model acts as a mediator that automatically explores causal relationships, performs simulations, and generates optimization hypotheses, thereby resolving the contradiction by handling process complexity through an intelligent intermediary rather than requiring simple traditional methods
Solution Approach 2:
The patent replaces traditional mechanical optimization approaches with an AI-based system that uses machine learning algorithms to explore causalities and generate optimization strategies. This substitution enables the system to handle complex multi-variable interactions that traditional mechanical methods cannot efficiently process, thereby improving optimization efficiency despite increased system complexity
2Productivity
If generative AI is used for causality exploration, then optimization efficiency improves, but the system complexity increases
Solution Approach 1:
The patent segments the optimization process into distinct functional modules: data receiving module, causality derivation module, simulation module, and hypothesis generation module. Each module performs a specific function, making the complex AI system more manageable and interpretable. This segmentation allows the system to handle complexity through modular organization while maintaining high optimization efficiency
Solution Approach 2:
The patent implements feedback mechanisms where the AI model continuously learns from simulation results and process data to improve its causality exploration accuracy. The system uses feedback loops to refine optimization hypotheses based on simulated outcomes, thereby managing system complexity through iterative learning and adaptation
3Measurement precision
If multiple variables are analyzed for causality, then the accuracy of optimization hypotheses improves, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing process data and pre-establishing the framework for causality analysis before conducting the actual optimization. The system prepares the AI model with initial training data and predefined analysis frameworks, which reduces the computational burden during actual optimization while maintaining high accuracy in causality detection
Solution Approach 2:
The patent applies partial action by focusing the AI analysis on the most critical variables and causal relationships rather than analyzing all possible variable interactions. The system identifies and prioritizes key causal pathways, performing detailed analysis only where needed, thereby reducing computational complexity while maintaining sufficient accuracy for effective optimization
Data Source
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AI summary
The present disclosure relates to a method and system for process optimization using generative AI, and more particularly, to a method and system for process optimization that allows the exploration or inference of causalities between multiple steps and variables involved in an arbitrary process through an artificial intelligence algorithm, and the derivation of hypotheses based thereon, and the simulation and verification thereof.