GUI Rule Conformance With ML-Driven Usability Adjustment
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Solution Overview
Problem
Web page designers often adjust graphical user interface (GUI) attributes at their discretion, leading to potential usability issues and resource consumption due to inefficient design processes, which can slow navigation and interpretation by users.
Innovation Solution
A GUI design management server uses a machine learning model to generate proposed GUIs that conform to design rules and improve usability, reducing resource consumption by optimizing attribute settings based on anticipated user demographics and design guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If web page designers adjust GUI attributes at their discretion, then design flexibility is improved, but usability and resource consumption are worsened
Solution Approach 1:
The system changes design parameters by using a machine learning model to automatically adjust GUI attributes based on design rules and user demographics. The model generates proposed GUIs with optimized attribute settings that balance design flexibility with usability requirements, resolving the contradiction between designer discretion and usability reliability.
2Reliability
If designers iterate on multiple GUI designs to achieve useability, then usability is improved, but resource consumption and time are worsened
Solution Approach 1:
The system performs preliminary action by using the machine learning model to generate multiple proposed GUI designs that already conform to usability guidelines before user review. This eliminates the need for designers to manually iterate through multiple versions, significantly reducing time and resource consumption while maintaining high usability standards.
Solution Approach 2:
The system implements feedback by evaluating proposed GUIs against design rules and usability criteria, then using this information to refine and generate improved designs. The machine learning model learns from design rule conformance and user demographics to continuously improve GUI proposals, resolving the contradiction between achieving usability and maintaining design efficiency.
3Speed
If GUIs are designed without adherence to design rules, then design speed is improved, but conformance to user expectations is worsened
Solution Approach 1:
The system applies self-service by enabling the machine learning model to automatically ensure design rule conformance without requiring manual designer intervention. The model independently evaluates and adjusts GUI attributes to meet accessibility guidelines and user expectations, maintaining high design speed while ensuring navigability and ease of operation.
Data Source
AI summary
Some implementations described herein provide apparatuses and techniques related to graphical user interface design conformance and useability. The apparatuses and techniques include a graphical user interface design management server including a graphical user interface design conformance and a measure of useability application. The graphical user interface design management server may receive one or more attribute changes related to a design of a graphical user interface. The graphical user interface design management server may then access a storage device containing graphical user interface design rules and determine a degree of conformance of a graphical user interface generated using the attribute changes to the graphical user interface design rules. Further, and using machine learning techniques, the graphical user interface design management server may determine one or more additional changes to the attributes that improve the measure of useability of the graphical user interface for anticipated users of the graphical user interface.