Process Flow Diagram Prediction Using Semantic Vector Embeddings
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Solution Overview
Problem
Conventional process flow diagram generation is complex and lacks techniques for predicting process flow diagram elements, accuracy verification, and searching similar diagrams, making it difficult for users to create accurate diagrams, especially for complicated processes with many activities.
Innovation Solution
A system that converts process flow diagram elements and context into semantic vectors, using a machine-learning model to predict and analyze elements, allowing for quicker and more accurate generation of process flow diagrams by identifying context, encoding features, and utilizing a process flow diagram embedding to make predictions and searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods are used to generate process flow diagrams, then users can create diagrams, but the process is complex and time-consuming, especially for complicated processes with many activities
Solution Approach 1:
The system performs preliminary actions by automatically generating process flow diagrams from process data before users need to manually create them. The machine learning model predicts diagram elements and structures in advance, reducing the time and effort required for diagram creation while maintaining accuracy even for complex processes
Solution Approach 2:
The system enables self-service by allowing the machine learning model to autonomously generate and complete process flow diagrams without requiring extensive user intervention. The model can predict missing elements, suggest improvements, and generate complete diagrams independently, making the process accessible to users regardless of their expertise level
2Manufacturing precision
If users manually create process flow diagrams without prediction capabilities, then they have full control, but accuracy verification and element prediction are unavailable, reducing diagram accuracy
Solution Approach 1:
The system implements feedback by using the machine learning model to predict process flow diagram elements and verify diagram accuracy. The model analyzes existing diagrams, predicts missing or incorrect elements, and provides feedback to users for improvement, thereby enhancing diagram accuracy while reducing the manual effort required for verification
Solution Approach 2:
The system replaces manual mechanical processes with automated machine learning-based prediction and verification. Instead of users manually checking each element for accuracy, the ML model automatically predicts correct elements and verifies diagram quality, substituting human cognitive effort with automated intelligent analysis
3Adaptability or versatility
If no semantic vector encoding is used, then the system is simpler, but it cannot perform predictions or searches on process flow diagram elements and contexts
Solution Approach 1:
The system applies parameter changes by converting process flow diagram elements and their contexts into semantic vectors. This transformation encodes structural and contextual information into numerical representations, enabling the machine learning model to perform predictions and searches. The parameter change from graphical elements to semantic vectors unlocks advanced analytical capabilities while managing complexity through efficient data representation
Data Source
AI summary
One embodiment provides a method, including: receiving a process flow diagram element of a process flow diagram; identifying a context of the process flow diagram element, wherein the identifying a context comprises identifying (i) categories of elements connected to the process flow diagram element, (ii) swimlanes within the process flow diagram, and (iii) text included in the process flow diagram; encoding features of the process flow diagram element into a semantic vector, wherein the features are identified from the context of the process flow diagram element; and predicting, utilizing a process flow diagram model, a process flow diagram element for the process flow diagram based upon the at least one process flow diagram element, wherein the process flow diagram model receives and analyzes the features of the at least one process flow diagram and outputs the predicted process flow diagram element.