Voice-Driven Application Prototyping Using Machine Learning
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Solution Overview
Problem
The process of developing computing applications is time-consuming and expensive, requiring significant manual effort for software development teams due to the need for manual creation of dummy user interfaces and repetitive implementation tasks, despite much of the work being boilerplate that machines can't understand.
Innovation Solution
A computer-implemented method and system for generating an application prototype using machine learning techniques, which captures events from a profiled application, analyzes them to create domain and user interface knowledge graphs, and modifies these graphs based on natural language utterances to produce an application prototype.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to create dummy user interfaces and develop boilerplate code, then developers can understand and control the application development process, but the development time and cost increase significantly
Solution Approach 1:
The system enables self-service by automatically generating application prototypes through voice commands. The machine learning model autonomously converts natural language input into functional prototypes without requiring manual coding, allowing the system to serve itself in the development process.
Solution Approach 2:
The patent replaces the mechanical system of manual coding with a voice-driven automated system. Developers speak natural language commands instead of writing code manually, and the system automatically translates these commands into application prototypes, substituting the mechanical typing and coding process with speech recognition and automated generation.
2Extent of automation
If machines are used to automate application development, then development time is reduced, but machines currently cannot understand application intentions and produce functional code
Solution Approach 1:
The patent introduces an intermediary layer between human intent and machine execution. The voice command system acts as a mediator that translates natural language into structured instructions that the machine learning model can process, bridging the gap between human understanding and machine automation.
Solution Approach 2:
The system changes the parameter of input method from traditional code-writing to voice commands. This parameter change enables machines to better understand application intentions by processing natural language speech patterns, which carry semantic meaning and intent more effectively than manual coding actions.
3Productivity
If voice-driven automated prototyping is implemented, then development time and cost are reduced, but the system complexity and machine learning requirements increase
Solution Approach 1:
The patent segments the complex development process into distinct voice-activated commands and automated generation steps. By breaking down the prototyping process into discrete, voice-controlled operations, the system manages complexity through modular organization of functions.
Data Source
AI summary
A method includes capturing an event, analyzing the event to generate graphs, receiving a natural language utterance, identifying an entity and a command, modifying the graphs; and emitting an application prototype. An application prototyping server includes a processor; and a memory storing instructions that, when executed by the processor, cause the server to capture an event, analyze the captured event to generate graphs, receive a natural language utterance, identify an entity and a command, modify the graphs; and emit an application prototype. A non-transitory computer readable medium containing program instructions that when executed, cause a computer to: capture an event, analyze the captured event to generate graphs, receive a natural language utterance, identify an entity and a command, modify the graphs; and emit an application prototype.