Workflow Mapping Refined by Reinforcement Feedback
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Solution Overview
Problem
Conventional systems struggle to provide a complete and understandable graphical depiction of complex workflows due to the use of static models that fail to leverage user knowledge and preferences, often resulting in unsatisfactory outputs.
Innovation Solution
A generative model adjusted through reinforcement learning based on user feedback is employed to iteratively refine graphical workflow depictions, incorporating natural language processing and generative adversarial networks to extract and arrange entities and actions, allowing for dynamic adjustments based on user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a static generative model is used to create workflow depictions, then the system structure is simple, but the quality and user satisfaction of the output is poor
Solution Approach 1:
The patent implements feedback loops where user interactions with the workflow depiction (such as modifications, corrections, or preferences) are captured and fed back to the generative model. This allows the model to learn from user behavior and improve subsequent workflow depictions, directly addressing the quality issue while managing complexity through iterative refinement rather than complete system redesign.
Solution Approach 2:
The system transitions from a static generative model to a dynamic one that adapts based on user feedback. The model's parameters and structure can evolve over time, allowing it to improve workflow depiction quality. This dynamic approach resolves the contradiction by making the system flexible and responsive without requiring complete structural overhaul.
2Adaptability or versatility
If a static model is used, then the system is easy to operate, but it cannot adapt to user preferences and feedback
Solution Approach 1:
The generative model is designed to automatically learn and adapt from user feedback without requiring manual reconfiguration or complex user intervention. The system self-adjusts its parameters and behavior based on observed user preferences, maintaining ease of operation while achieving adaptability. Users simply interact naturally with the workflow depictions, and the system autonomously improves.
3Manufacturing precision
If reinforcement learning is implemented to improve workflow depictions, then the quality of output increases, but the training time and computational resources increase
Solution Approach 1:
The system implements reinforcement learning in a phased manner, starting with partial implementation on key components of the workflow generation process. Rather than training the entire system simultaneously, critical elements are optimized first, delivering quality improvements sooner. This incremental approach reduces initial training time while still achieving significant quality gains.
Solution Approach 2:
The system performs preliminary training and pre-computation of common workflow patterns before actual use. By pre-learning frequent scenarios and establishing baseline performance, the system reduces the computational burden and time required during interactive refinement phases. This preliminary action allows faster adaptation to specific user needs while maintaining high output quality.
Data Source
AI summary
Generating visual workflow representations by receiving data including text instructions, identifying actions in the instructions, generating a mapping of the actions according to a generative model, the mapping including an action sequence, providing the mapping to a user, receiving feedback from the user, altering the generative model according to the feedback, and generating a revised mapping according to the feedback.