Machine Learning Engine Refinement for Component-Based User Interface Generation
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Solution Overview
Problem
Many entities lack the expertise in coding and design to create customized user interfaces that effectively combine functionality and aesthetics, leading to a need for automated solutions that can generate user interfaces without manual design or coding efforts.
Innovation Solution
A component-based system that uses machine learning to analyze existing user interfaces, identify and group elements into components, and automatically generate new interfaces based on input data, constraints, and user interaction, allowing for customization without requiring extensive design or coding knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated machine learning systems are used to generate user interfaces, then the need for manual design and coding expertise is reduced, but the quality and customization of the generated interfaces may deteriorate
Solution Approach 1:
The system implements feedback loops where user interactions with generated interfaces are collected and used to retrain and refine the machine learning models. This continuous feedback mechanism allows the system to learn from actual usage patterns and improve interface quality over time while maintaining automated generation capabilities.
Solution Approach 2:
The system performs preliminary analysis of existing high-quality interfaces to extract design patterns, component structures, and best practices before generating new interfaces. This preliminary learning phase enables the automated system to incorporate proven design qualities into generated interfaces without requiring manual intervention for each new interface.
2Productivity
If generic templates are used for user interfaces, then development time is reduced, but customization and user experience quality deteriorate
Solution Approach 1:
The system segments user interfaces into reusable component blocks with standardized interfaces but customizable content and behavior. This segmentation allows templates to provide structural efficiency while enabling customization of individual components through configuration parameters and user-specific data binding.
Solution Approach 2:
The system uses parameter-based customization where template definitions include configurable parameters that can be adjusted to generate different interface variants. This allows the same template framework to produce highly customized interfaces by changing parameters such as layout configurations, component properties, and data binding relationships.
3Manufacturing precision
If skilled designers manually create customized interfaces, then interface quality and branding alignment are improved, but time consumption and resource requirements increase
Solution Approach 1:
The system copies and adapts proven design patterns and component structures from existing high-quality interfaces and branding guidelines. By learning from existing successful interfaces through machine learning, the system can replicate effective design solutions and apply them to new interfaces automatically, maintaining design quality without manual copying.
Solution Approach 2:
The system creates universal template frameworks that can serve multiple purposes: maintaining branding consistency across different interfaces, ensuring design quality through proven patterns, and enabling rapid generation. These multi-functional templates reduce the need for skilled designers to manually create each interface while preserving quality standards.
Data Source
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AI summary
Techniques are disclosed relating to refining, based on user feedback, one or more machine learning engines for automatically generating component-based user interfaces. In various embodiments, a computer system stores template information that defines a plurality of component types and one or more display parameters identified for one or more user interfaces. The computer system may receive a request to generate a user interface, where the request specifies a data set to be displayed. Further, the computer system may automatically generate a user interface, where the generating is performed by one or more machine learning engines that use the template information and the data set as inputs. The computer system may then provide the user interface to one or more users, receive user feedback associated with the user interface, and train at least one of the one or more machine learning engines based on the user feedback.