Elevator Interface Generation Using Rules and Acceptance Scoring
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Solution Overview
Problem
Elevator control and user interface systems require inefficient and labor-intensive graphical user interface design processes, involving numerous drafts and substantial human and computational resources, often necessitating skilled designers and administrators.
Innovation Solution
An elevator system with a drive controller and non-transitory computer-readable medium that retrieves rules for hardware, software, and operation components, generates graphical user interfaces based on templates, calculates an acceptance score, and publishes them for display, allowing for automated design and user feedback integration to optimize interface design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a traditional iterative design process with skilled designers is used to create graphical user interfaces, then the design quality and administrator satisfaction improve, but the time consumption and computational resources increase substantially
Solution Approach 1:
The system enables self-service by allowing administrators to automatically generate and iterate graphical user interface designs using machine learning algorithms. The automated design engine processes design templates and parameters without requiring skilled designers, reducing manual intervention while maintaining design quality through algorithmic optimization and automated evaluation.
Solution Approach 2:
The system changes design parameters automatically by adjusting visual elements, layout configurations, and interface characteristics based on administrator feedback and performance metrics. The machine learning model iteratively modifies these parameters to optimize design outcomes, replacing the traditional manual parameter adjustment process.
2Adaptability or versatility
If multiple drafts and iterations are performed to finalize the graphical user interface design, then the design suitability and user satisfaction improve, but the computational resources and manual effort increase substantially
Solution Approach 1:
The system implements feedback loops where administrator responses to generated designs are automatically captured and used to refine subsequent design iterations. The machine learning model learns from this feedback to improve design suitability over time, enabling rapid adaptation without proportional increases in manual effort or computational resources.
Solution Approach 2:
The system performs preliminary actions by pre-processing design templates, constraints, and administrator preferences before actual design generation. This preparatory work includes setting up design spaces, defining parameter ranges, and establishing evaluation criteria, which streamlines the subsequent iterative design process and reduces overall computational burden.
3Manufacturing precision
If skilled designers and administrators manually create and review graphical user interface designs, then the design quality improves, but the labor intensity and resource requirements increase
Solution Approach 1:
The system empowers administrators to independently generate and evaluate graphical user interface designs without requiring skilled designers. The automated design engine handles complex design tasks, while administrators simply provide feedback and select from generated options, dramatically simplifying the operational process while maintaining design quality.
Solution Approach 2:
The system replaces the mechanical process of manual design creation and review with an automated computational system. Machine learning algorithms generate multiple design variants, evaluate them against criteria, and present optimized options to administrators, substituting human manual labor with intelligent automated processes that maintain or improve design quality.
Data Source
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AI summary
An elevator system includes a drive controller configured to actuate a drive motor of an elevator drive assembly. A non-transitory computer-readable medium includes program instructions that cause a processor to retrieve a set of rules of the elevator system, generate at least one graphical user interface by applying the set of rules to at least one design template, calculate an acceptance score for the at least one graphical user interface based on a user profile of an administrator, and publish the at least one graphical user interface based at least partially on the acceptance score. The elevator system further includes a display device configured to display the graphical user interface after it is published and cause the drive controller to actuate the drive motor based on user input of a selection of at least one selectable option on the at least one graphical user interface.