Intelligent User Centric Design Platform Using Machine Learning
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Solution Overview
Problem
The manual Design Thinking process in software development is time-consuming and resource-intensive, requiring skilled resources, high personnel dependencies, and not leveraging analytics-driven data, which prolongs software design timelines and increases costs.
Innovation Solution
An intelligent user-centric design platform utilizing a supervised machine learning model, including a convoluted neural network and text classifier, to automatically generate and refine user-centric design diagrams based on user input, reducing the need for manual iterations and improving design accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual Design Thinking process is used, then design quality can be maintained through human expertise, but software development timeline is prolonged and resource dependencies increase
Solution Approach 1:
The system enables self-service by allowing the machine learning model to automatically generate UCD diagrams from user inputs and requirements without requiring manual intervention from skilled designers for every diagram creation task
Solution Approach 2:
The patent replaces the mechanical human expert review process with an automated machine learning system that uses natural language processing and image recognition to generate and validate UCD diagrams, substituting human mechanical analysis with algorithmic processing
2Manufacturing precision
If manual Design Thinking process is used, then design accuracy can be ensured through expert review, but resource dependencies and costs increase
Solution Approach 1:
The system performs self-validation through automated machine learning models that independently assess design requirements and generate UCD diagrams without requiring external expert review, eliminating resource dependencies while maintaining accuracy through algorithmic precision
Solution Approach 2:
The patent transforms the quality assurance mechanism from human expert judgment to automated parameter-based validation using machine learning models that evaluate designs against predefined criteria and requirements, changing the fundamental parameter of how accuracy is measured and ensured
3Productivity
If automated machine learning is used, then software development productivity is improved and timelines are reduced, but system complexity increases
Solution Approach 1:
The system segments the complex design process into distinct automated components: natural language processing for requirement extraction, image recognition for diagram generation, and machine learning models for validation, making the overall complexity manageable through modular architecture
Solution Approach 2:
The patent creates a universal machine learning system that can handle multiple design tasks and domains through a single integrated platform, reducing the need for separate specialized tools and processes while managing complexity through unified architecture
Data Source
AI summary
An intelligent user centric design platform is provided. In implementations, a method includes: receiving, by a computing device, software design input from a user, the software design input including software domain information; sending, by the computing device, questions to the user selected from a database of predetermined questions based on the domain information; receiving, by the computing device, answers to the questions from the user, the answers including text information regarding design requirements of the user; determining, by the computing device, a proposed user-centric design (UCD) diagram by matching the answers to a stored UCD diagram in a repository using a supervised machine learning model; and presenting, by the computing device, the proposed UCD diagram in a user interface, wherein the user interface enables acceptance of the proposed UCD diagram or rejection of the proposed UCD diagram.