Machine Learning Design Element Detection in 2D Documents
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Solution Overview
Problem
Existing methods for searching electronic documents, such as PDFs, are limited to text-based queries and cannot effectively identify and classify graphic elements based on their visual characteristics, making it difficult to efficiently search and review documents with primarily graphical content.
Innovation Solution
A computer-implemented method and system that uses a trained machine learning model to detect design elements in a design document, determine their locations and types, and augment the document with this information, enabling enhanced search capabilities based on visual characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If text-based search methods are used, then search simplicity is maintained, but the ability to identify and classify graphic elements is lost
Solution Approach 1:
The patent replaces traditional text-based mechanical search methods with machine learning-based visual detection. The system uses a trained machine learning model to automatically detect, locate, and classify design elements (such as walls, doors, windows) in graphical documents, enabling visual search capabilities while maintaining ease of use through automated processing
Solution Approach 2:
The patent introduces an intermediary layer between the user and the document content. The machine learning model acts as a mediator that translates visual graphic elements into detectable and classifiable data structures, allowing the system to bridge the gap between simple search operations and complex visual analysis
2Difficulty of detecting and measuring
If machine learning-based detection is applied, then graphic element classification capability is improved, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model on a comprehensive dataset of design elements before actual use. The model is trained to recognize various design elements (walls, doors, windows, etc.) and their characteristics, so that during document analysis, the system can quickly and accurately detect and classify elements without complex real-time computation
Solution Approach 2:
The patent uses a pre-trained machine learning model that has copied and internalized patterns from extensive training data. This pre-trained model serves as a reusable component that can be applied to multiple documents, reducing the need for complex custom training for each specific document type and thereby managing system complexity
3Measurement precision
If manual review of graphical documents is performed, then accuracy is maintained, but time consumption increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically detect, locate, and classify design elements in graphical documents without requiring manual intervention. The machine learning model independently analyzes the document content, providing accurate identification of design elements while significantly reducing the time needed compared to manual review processes
Data Source
AI summary
Systems and methods are disclosed for automatically detecting a design element in a design document. One method comprises receiving a design document and generating an enhanced design document based on the received design document. The enhanced design document may be generated by augmenting additional information to the design document using machine learning techniques. In response to receiving a user input, one or more design elements in the enhanced design document may be determined, and additional information associated with the determined one or more design elements may be displayed to the user.