Neural Network Sketch Interpretation for GUI Design
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Solution Overview
Problem
Current technologies face challenges in designing and implementing graphical user interfaces (GUIs) and distributed processing systems without requiring specialized programming skills, particularly for complex calculations and large data sets, which limits user-friendly interaction and collaboration in scientific and technical applications.
Innovation Solution
A system that employs a neural network to interpret sketch inputs and generate GUI instructions, allowing users to create GUIs and job flow definitions without extensive programming knowledge, by recognizing visual tokens and converting them into executable commands and data structures for distributed processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a dedicated GUI programming interface is used to create graphical user interfaces, then the GUI functionality is achieved, but the complexity of operation increases and requires specialized programming skills
Solution Approach 1:
The system captures images of hand-drawn sketches and creates digital reproductions of the GUI design. The neural network processes these sketch images to generate executable GUI code, allowing users to create interfaces by drawing simple sketches rather than learning complex programming interfaces. This copying approach transforms the physical sketch into a digital GUI implementation.
Solution Approach 2:
The system replaces the mechanical process of manual GUI programming with an automated neural network-based interpretation system. Instead of requiring users to manually code GUI elements using dedicated programming interfaces, the system uses image recognition and natural language processing to automatically translate sketches into functional GUI code, eliminating the need for specialized programming knowledge.
2Productivity
If specialized programming skills are required to write code for distributed processing, then complex calculations with large data sets can be performed, but the ease of operation decreases
Solution Approach 1:
The system enables users to perform complex distributed processing tasks without requiring them to learn specialized programming skills. By allowing users to define processing requirements through simple sketches and natural language descriptions, the system automatically generates and executes the appropriate distributed processing code, making advanced computational capabilities accessible to non-programmers.
Solution Approach 2:
The neural network acts as an intermediary between the user's simple sketch input and the complex distributed processing system. It translates high-level user intentions expressed through sketches into detailed programming instructions that the distributed processing system can execute, bridging the gap between user capability and system complexity.
3Ease of operation
If spreadsheet data structures with formula tables are used for analysis routines, then user-friendliness is improved, but the ability to accommodate complex distributed processing decreases
Solution Approach 1:
The system dynamically adapts the representation of analysis routines based on the requirements. Users can start with simple spreadsheet-like formula definitions and the system dynamically transforms these into optimized distributed processing implementations. The system adjusts the level of abstraction and processing architecture automatically, maintaining user-friendliness while enabling complex distributed execution.
Data Source
AI summary
An apparatus includes a processor to employ a neural network to interpret sketch input to identify an object token that represents a command to display either details of an object or a list of objects on a specified page of a GUI. In response to identifying the object token, the processor is caused to generate GUI instructions to perform the command, and employ the neural network to further interpret the sketch input to identify text specifying a page of the GUI on which to perform the command In response to identifying the text specifying the page, the processor is caused to incorporate an indication of the page into the GUI instructions, augment a job flow definition with the GUI instructions, and store the job flow definition within a federated area in support of providing the GUI when the job flow of the job flow definition is performed.