CAD Markup Automation Using Machine Learning and Trace Correlation
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Solution Overview
Problem
Existing CAD drafting processes require manual interpretation of markups by drafters, leading to a slow, error-prone, and inefficient workflow due to the lack of direct integration of markup data into design files, and existing systems fail to accurately translate markups into CAD application objects.
Innovation Solution
Utilizing a CAD application trace feature combined with machine learning to identify and correlate markups with intended CAD application objects, allowing for automated or suggested updates while maintaining review and intervention by the drafter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual drafting process is used to incorporate markups into CAD drawings, then drafting accuracy can be maintained through expert review, but drafting time and productivity are significantly reduced
Solution Approach 1:
The patent introduces an intermediary system consisting of image processing algorithms and machine learning models that automatically interpret markup images and convert them into CAD commands. This intermediary layer bridges the gap between collaborative markups created by non-experts and the precise CAD drafting requirements, enabling automated conversion while maintaining accuracy through multiple validation stages including expert review feedback loops.
Solution Approach 2:
The patent replaces the mechanical manual process of interpreting and recreating markups in CAD software with an automated computational system. The system uses image recognition, optical character recognition (OCR), and machine learning models to automatically detect markup elements, interpret their meaning, and generate corresponding CAD commands, eliminating the need for manual tracing and recreation while maintaining drafting accuracy.
2Adaptability or versatility
If markup data from non-CAD applications is manually interpreted and converted, then integration of collaborative feedback is achieved, but the process becomes slow and error-prone
Solution Approach 1:
The patent enables markup data to self-convert into CAD commands through automated processing. The system captures markup images, automatically processes them through image recognition and OCR algorithms, extracts meaningful information, and generates CAD commands without requiring manual intervention. This self-service automation dramatically reduces the time required to incorporate collaborative feedback while maintaining adaptability across different markup sources and formats.
Solution Approach 2:
The patent transforms markup data from various non-CAD applications into standardized CAD commands by changing the parameter representation. The system converts diverse input formats (handwritten notes, printed text, graphical annotations) into unified CAD command parameters, enabling seamless integration of collaborative feedback from different sources while maintaining versatility in handling various markup types.
3Productivity
If automated markup recognition is implemented, then drafting efficiency is improved, but the complexity of the system increases
Solution Approach 1:
The patent segments the automated markup recognition system into distinct functional modules: image capture module, image preprocessing module, markup detection module, OCR processing module, command generation module, and validation module. Each module performs a specific function and can be independently developed, tested, and maintained. This segmentation reduces system complexity by breaking down the complex automation process into manageable, specialized components while maintaining high drafting efficiency.
Data Source
AI summary
A method and system provide for computer aided drawing (CAD) automation. A CAD drawing in a first format that is native to a CAD application is obtained. Markup for the CAD drawing that is in a second non-native format is acquired. A machine learning engine processes the markup using a machine learning model, to identify a markup type, identify markup properties, correlate, the markup to a CAD object needed to update the CAD drawing, and determine, CAD object properties and CAD application properties. The CAD object with the CAD object properties is provided in the CAD application using the CAD application properties. User input is accepted and the CAD drawing is updated based thereon. The machine learning model is updated based on the user input and utilized to process future markups.