Automated Screen Display Generation via NLP and Machine Learning
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Solution Overview
Problem
Software development is inefficient due to communication gaps between specialized roles, leading to inferior software delivery, and there is a need to automate processes to enhance efficiency.
Innovation Solution
A computerized method using natural language programming (NLP) with visual programming applications, converting user conversational input into machine-readable format, interpreting goals, generating functionality, and using machine learning methods like artificial neural networks (ANN) and support vector machines (SVM) for automated screen display generation and configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple specialized roles communicate through artifacts to develop software, then software delivery can be achieved, but communication gaps lead to inferior delivery and inefficiency
Solution Approach 1:
The patent introduces an automated intermediary system that receives natural language requirements and automatically generates software artifacts including UI screens, backend logic, and database schemas. This intermediary eliminates communication gaps by directly translating user requirements into implementable code without relying on manual artifact exchange between specialized roles.
Solution Approach 2:
The patent replaces the mechanical system of manual communication and artifact exchange between human roles with an automated AI-based system. The system uses natural language processing and code generation algorithms to automatically transform requirements into software components, substituting human-to-human communication with machine-based automation.
2Productivity
If manual processes are used for screen display generation and configuration, then content can be placed on screens, but the process is time-consuming and inefficient
Solution Approach 1:
The patent performs preliminary actions by pre-defining content templates, layout patterns, and configuration rules that can be automatically applied during screen generation. The system prepares reusable components and design patterns in advance, allowing rapid assembly of screen displays without manual configuration during the actual generation process.
Solution Approach 2:
The system enables self-service by automatically generating screen displays, positioning content elements, and configuring layouts based on the provided requirements. The AI system performs these tasks autonomously without requiring manual intervention for content placement, thereby eliminating time loss associated with manual configuration.
3Manufacturing precision
If automated machine learning methods are used for optimizing content location, then screen display optimization is improved, but system complexity increases
Solution Approach 1:
The patent optimizes content placement by dynamically adjusting parameters such as position coordinates, layout dimensions, and spacing metrics using machine learning algorithms. The system changes these parameters automatically to achieve optimal screen displays based on the analyzed requirements, providing precision without requiring complex manual intervention.
Data Source
AI summary
In one aspect, a computerized method for implementing automated screen display generation and configuration including the step of obtaining a set of content that is displayed on a computer screen. The method includes the step of determining a number of screens to display the set of content. The method includes the step of determining an optimized location for each content in the number of screens that display the set of content using one or more machine learning methods to determine optimized location for each content in the number of screens.