AI User Task Generation with Tokenization and RL Feedback
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Solution Overview
Problem
Creating user tasks is challenging due to limited reuse potential and high time and cost in manual design, especially in small-scale projects, and existing technologies struggle to automate user experience (UX) tasks efficiently.
Innovation Solution
Utilizing generative AI and reinforcement learning to automatically split raw user tasks into tokens, generate new tasks from these tokens, and refine them using self-attention and reward models to enhance accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If user tasks are manually designed and created, then the quality and customization of user tasks can be ensured, but the time consumption and cost increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing raw user tasks into atomic subtasks and storing them in a reusable library. When new tasks are needed, the system combines these pre-prepared atomic tasks rather than creating everything from scratch, significantly reducing the time required for task design while maintaining quality through the structured framework.
Solution Approach 2:
The system creates copies of atomic subtasks from a library of previously processed user tasks. Instead of manually designing each new task, the system copies and recombines atomic tasks that have been proven effective, reducing time consumption while preserving quality through iterative refinement and validation.
2Adaptability or versatility
If user tasks are manually designed with high customization, then the relevance to specific user needs is improved, but the reuse potential across different projects decreases
Solution Approach 1:
The system segments user tasks into atomic subtasks that represent fundamental user actions. These atomic tasks are stored in a library and can be independently reused across different projects. By breaking down complex tasks into reusable atomic components, the system enables high reuse potential while managing complexity through modular organization and systematic recombination.
Solution Approach 2:
The system creates universal atomic tasks that can serve multiple functions across different user scenarios and projects. Each atomic task is designed to be context-independent and reusable, allowing the same atomic task to be applied in various combinations to address different user needs, thereby increasing adaptability and versatility without proportionally increasing design complexity.
3Extent of automation
If generative AI is used to automatically generate user tasks, then productivity and automation are improved, but the precision and relevance of generated tasks may deteriorate
Solution Approach 1:
The system implements feedback loops where generated tasks are evaluated against quality criteria and user interactions. The reinforcement learning component continuously learns from feedback about task effectiveness, refining the generation process to improve precision over time. This automated feedback mechanism ensures that while automation extent increases, task generation accuracy is maintained and continuously improved.
Solution Approach 2:
The system replaces manual mechanical task design with automated generative AI processes. Large language models and reinforcement learning algorithms automatically generate, refine, and optimize user tasks based on input specifications. This substitution of manual processes with intelligent automation maintains high precision through algorithmic consistency while dramatically improving productivity and automation extent.
Data Source
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AI summary
The present invention relates to a method of creating a new user task (3), the method comprises the following steps: - providing a raw user task (4) (high-level task, goal), - splitting the raw user task (4) into tokens (2), - creating the new user task (3) by a generative artificial intelligence unit (6) on the basis of the tokens (2).