Bidirectional Workflow Assistant for Natural Language Development
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Solution Overview
Problem
Existing development platforms require users to have knowledge and skills to effectively use their features, limiting accessibility and ease of use for non-technical users.
Innovation Solution
A bi-directional co-pilot for development platforms that interacts with users through natural language input, using machine learning models to translate between user requests and computer instructions, allowing users to create or edit workflows without extensive training, and providing bidirectional updates between graphical representations and natural language descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional development platforms are used, then users can create workflows with full functionality, but users require extensive knowledge and skills to use the platform effectively
Solution Approach 1:
The patent introduces an assistant as an intermediary between the user and the development platform. The assistant translates natural language user requests into platform-specific workflow instructions, shielding users from the complexity of the underlying platform while maintaining full functionality. This mediator handles the translation and coordination, allowing users to interact with the platform through simple language rather than requiring deep platform knowledge.
Solution Approach 2:
The patent replaces the traditional mechanical interaction model (drag-and-drop, manual configuration) with an intelligent language-based system. Instead of requiring users to manually manipulate complex interface elements, the system uses natural language processing and machine learning to interpret user intent and automatically generate the corresponding workflow configurations, substituting manual mechanical operations with automated intelligent processing.
2Adaptability or versatility
If development platforms provide comprehensive features, then functionality is enhanced, but accessibility to non-technical users is reduced
Solution Approach 1:
The assistant serves as an intermediary that adapts comprehensive platform features to non-technical users. It translates high-level user requirements into detailed platform-specific instructions, allowing users to access full platform functionality without needing to understand the underlying complexity. The assistant handles the adaptation between user intent and platform capabilities.
Solution Approach 2:
The system creates simplified representations of complex workflows through natural language descriptions. The assistant generates text-based models of workflow logic that are easier for non-technical users to understand and manipulate, while maintaining fidelity to the actual platform functionality. This copying approach allows users to work with simplified mental models that map to the full-featured platform.
3Productivity
If users manually create workflows, then precision is achieved, but time consumption increases
Solution Approach 1:
The assistant performs preliminary actions by pre-processing user natural language requests and pre-generating workflow configurations before final execution. It anticipates user needs and prepares workflow structures in advance, reducing the time required for manual configuration and refinement. The system proactively creates draft workflows that users can then review and adjust, rather than requiring users to build everything from scratch.
Solution Approach 2:
The system enables self-service workflow creation by automatically interpreting user requests and generating functional workflows without requiring manual step-by-step configuration. The assistant handles the entire workflow creation process autonomously, from understanding user intent to producing executable workflows, significantly reducing the time investment required from users while maintaining precision through iterative refinement.
Data Source
AI summary
In an embodiments, a process for providing a bidirectional assistant for development platforms includes receiving conversation data that indicates a user requested function, wherein the user requested function is associated with a workflow. The process includes determining, via a machine learning model, computer instructions corresponding to the user requested function. The process includes updating the workflow to include the computer instructions corresponding to the user requested function.