Elevator GUI Publishing with Acceptance Scoring for Setup Efficiency

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Solution Overview

Problem

Elevator control and user interface systems require complex and inefficient setup processes, involving substantial human effort and computing resources, especially when designing graphical user interfaces for modern elevator systems.

Innovation Solution

An elevator system that includes a drive controller and a non-transitory computer-readable medium with program instructions to generate graphical user interfaces based on predefined rules, calculate an acceptance score, and publish them for display, allowing for efficient user input processing and motor actuation, utilizing machine learning for improved administrator feedback integration.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If a graphical user interface is designed for modern elevator systems, then the usability and appeal of the interface is improved, but the complexity of the setup process and the amount of manual effort required increases

Engineering Contradiction:

Improveusability of user interfaceVSAvoidcomplexity of setup process

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system performs automatic generation of the graphical user interface using machine learning algorithms. The processor automatically creates the GUI based on learned patterns from administrator feedback, eliminating the need for manual design efforts and reducing setup complexity while maintaining high usability standards.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system incorporates administrator feedback loops where the processor calculates acceptance scores based on administrator responses and iteratively improves the GUI design. This feedback mechanism allows the system to learn from user preferences and automatically adjust the interface design, reducing manual intervention while improving usability.

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If a graphical user interface is designed with multiple variables and parameters, then the functionality and appeal of the interface is improved, but the amount of computing resources required increases

Engineering Contradiction:

Improvefunctionality of user interfaceVSAvoidcomputing resources consumed

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The machine learning model is trained in advance on a dataset of administrator preferences and feedback. This preliminary training phase allows the system to learn patterns and preferences beforehand, so that during actual GUI generation, the processor can quickly create optimized interfaces without requiring extensive real-time computing resources.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces traditional manual GUI design processes with machine learning-based automatic generation. The processor uses trained ML models to generate interfaces, substituting human designer efforts and iterative design processes with automated algorithms that require fewer computing resources during deployment.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Manufacturing precision

If the design process involves iterative review and approval of drafts, then the quality and acceptance of the user interface is improved, but the time and manual effort required increases

Engineering Contradiction:

Improvequality of user interface designVSAvoidtime for design process

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system performs self-evaluation of GUI designs by automatically calculating acceptance scores based on trained machine learning models and administrator feedback patterns. This self-service capability eliminates the need for multiple manual review cycles while maintaining high design quality through automated optimization.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The machine learning model is pre-trained on extensive datasets of successful GUI designs and administrator preferences. This preliminary preparation enables the system to generate high-quality interfaces in a single pass without requiring iterative reviews, as the model has already learned optimal design patterns during the training phase.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11332340B2Elevator control and user interface system

Publication Date: 2022.05.17 THYSSENKRUPP ELEVATOR INNOVATION AND OPERATIONS GMBH

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.