Neural Network Development Interface with Coordinate Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI, ML, and DL technologies face challenges such as complexity, data processing inefficiencies, limited accessibility, and lack of explainability, which hinder their widespread adoption and effective utilization.
Innovation Solution
The system provides a user-friendly, diagramming-based approach for building AI, ML, and DL models, allowing customization at the artificial neuron level, with features like a coordinate-based mapping system, real-time visualization, and automated logic generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If traditional AI/ML/DL technologies are used, then processing power and data analysis capability are improved, but device complexity and difficulty of operation increase
Solution Approach 1:
The patent introduces a cloud-based platform as an intermediary that hosts pre-trained neural network models. Users interact with simplified API interfaces rather than directly managing complex model architectures, training processes, or hardware resources. The platform handles computational complexity in the background while providing user-friendly access points for data upload, model selection, and result retrieval.
Solution Approach 2:
The system provides access to pre-trained neural network models that have been previously developed and optimized. Instead of requiring users to train models from scratch, the platform offers copies of existing models that can be immediately deployed for various applications. This allows users to leverage sophisticated processing capabilities without reproducing the complex training processes.
2Measurement precision
If specialized AI/ML knowledge is required, then model performance and accuracy are improved, but ease of operation deteriorates
Solution Approach 1:
The platform enables users to perform AI analysis tasks independently through automated processes. Users can upload data, select from pre-configured model types, and receive predictions without needing to understand underlying algorithms, adjust hyperparameters, or manage training processes. The system handles complexity automatically while delivering accurate results.
Solution Approach 2:
The system allows users to specify high-level parameters such as data type, desired output format, and basic model preferences, while the platform automatically adjusts the numerous technical parameters required for accurate modeling. This abstraction layer enables users to obtain precise predictions without managing the complexity of model configuration.
3Adaptability or versatility
If comprehensive customization options are provided, then adaptability to specific needs is improved, but device complexity increases
Solution Approach 1:
The customization interface is divided into distinct modular sections, each allowing users to adjust specific aspects of model behavior independently. Users can customize data processing parameters, model architecture selections, output formats, and evaluation metrics as separate controllable elements. This segmentation makes the customization process manageable while maintaining comprehensive adaptability.
Solution Approach 2:
The platform provides a universal interface that handles multiple customization needs through a single cohesive system. Rather than requiring separate tools for different customization tasks, the platform integrates model selection, parameter adjustment, data configuration, and result interpretation into one unified environment, reducing overall system complexity.
Data Source
AI summary
The subject matter as disclosed herein provides a system and method for improving neural network development efficiency through a visual development interface and customization framework. The system implements a coordinate-based mapping system that enables precise component tracking through spatial identification based on layer position and placement order, which reduces computational overhead. The interface provides drag-and-drop model creation capabilities alongside granular neuron-level customization. Users can modify activation functions, weight initializations, and connectivity patterns for individual neurons. The system supports creation of heterogeneous neural networks with non-uniform architectures and enables implementation of constant node networks and amalgamated configurations. Real-time visualization capabilities provide both high-level architectural views and detailed component-level information. Automated logic generation translates visual representations into optimized backend code. The system delivers measurable technical benefits including improved processing efficiency, reduced resource requirements, and accelerated development processes through automated batch operations and immediate optimization capabilities.