Low-Code ML Integration for Filter and Sorter Recommendations
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Solution Overview
Problem
Low-code/no-code software development applications require technical expertise for integrating machine learning, limiting non-expert users' ability to design and implement complex functionalities.
Innovation Solution
A graphical user interface with a drag-and-drop canvas and configuration panel enables users to integrate machine learning models without manual coding, generating metadata for filter and sorter elements that can be parsed by the client application to provide recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional IDEs are used for software development, then programming functionality and control are improved, but the requirement for programmer expertise increases
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between the user and the IDE. The AI assistant translates natural language user intentions into programming code, allowing users without programming expertise to leverage full IDE functionality. The AI acts as a mediator that converts high-level user requirements into low-level code implementations, resolving the contradiction between maintaining programming control and reducing expertise requirements.
Solution Approach 2:
The AI assistant enables self-service programming by automatically generating, explaining, and debugging code based on user inputs. Users can obtain programming functionality through the AI's autonomous code generation and explanation capabilities, eliminating the need for users to manually learn programming syntax and IDE operations while still accessing advanced development features.
2Ease of operation
If AI code generation is used to reduce programming expertise requirements, then ease of operation is improved, but control over generated code decreases
Solution Approach 1:
The patent implements feedback mechanisms where the AI assistant provides explanations for generated code, allowing users to understand and verify the code's correctness. Users can request code explanations, review generated code before execution, and provide feedback to the AI for corrections. This feedback loop maintains user control while preserving the benefits of AI-generated code, addressing the contradiction between ease of operation and code control.
Solution Approach 2:
The AI assistant performs preliminary code generation and validation before the user executes the code. Users can review the generated code and make modifications if needed, ensuring control over the final implementation. The preliminary action of code generation followed by user review maintains both accessibility and control.
3Loss of information
If comprehensive code explanations are provided to non-programmers, then understanding is improved, but information overload increases
Solution Approach 1:
The patent applies local quality by providing explanations at different levels of detail based on user needs and context. The AI assistant can provide high-level conceptual explanations for non-programmers or more detailed technical explanations when requested. This localized adaptation of explanation depth ensures adequate understanding without overwhelming users with excessive information, resolving the contradiction between code understanding and information volume.
Data Source
AI summary
An application development graphical user interface which includes a drag-and-drop canvas area is provided. User interface elements are configured to be dragged and dropped into the canvas area to design a client application graphical user interface including a filter or a sorter. A configuration panel of the application development user interface providing options and fields for configuring sorter or filter configuration options and for configuring machine learning service configuration options. Machine learning service metadata is generated for the client application. The client application metadata for the client application is deployed and the client application configured present recommendations based on inputs to the filter or sorter using the machine learning service.