Automated Image Analysis for Integration Process Flow Modeling
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Solution Overview
Problem
Current systems require users to manually model integration process flows using graphical visual interfaces, duplicating hand-drawn illustrations, which is time-consuming and inefficient, especially in complex business environments involving multiple trading partners and geographically dispersed entities.
Innovation Solution
The system automatically generates an integration process flow model based on a captured image of a hand-drawn or manually-formed illustration using shape recognition methods and neural networks, identifying visual elements and their connections to create a visual model that can be executed without requiring users to access or write code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users manually model integration process flows using graphical visual interfaces, then the process flow can be accurately represented, but the time consumption and efficiency deteriorate
Solution Approach 1:
The system captures an image of the hand-drawn process flow illustration and automatically generates a digital process flow model by recognizing visual elements in the image, eliminating the need for users to manually recreate the diagram in a graphical interface while preserving the original design intent
Solution Approach 2:
The patent replaces the manual mechanical process of dragging and dropping visual elements with an automated image recognition system that uses neural networks and machine learning algorithms to identify and convert hand-drawn elements into structured process flow data
2Ease of manufacture
If users manually duplicate hand-drawn illustrations using graphical interfaces, then the process flow model can be created, but the complexity and time required increase
Solution Approach 1:
The system performs self-service by automatically analyzing the captured image of the hand-drawn illustration, identifying visual elements, and generating the process flow model without requiring user intervention in the modeling process, thereby simplifying the user's task while managing complexity internally
3Productivity
If automated image recognition is used to generate process flow models, then time consumption is reduced, but the precision of visual element identification may deteriorate
Solution Approach 1:
The system employs feedback mechanisms where the neural network continuously refines its identification of visual elements based on training data and correction loops, allowing rapid processing while maintaining high accuracy through iterative improvement and validation against expected process flow patterns
Data Source
AI summary
An information handling system operating an image analysis integration flow creation system may comprise a network interface device receiving a captured image of an illustrated integration process flow chart connecting process step illustrations in a user-specified pattern, and a processor determining a process flow plot connecting visual element placeholders corresponding to the process step illustrations according to the user-specified pattern. The processor may identify an image shape within each process step illustration and image shape-identifying parameters for that image shape, apply a neural network to determine a type of integration process visual element represented by each process step illustration, based on the image shape-identifying parameters, and generate an integration process flow model displayed in a GUI by inserting the type of integration process visual element represented by each process step illustration into the visual element placeholder corresponding to that process step illustration.