Deep Learning Architecture Diagram Conversion
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Solution Overview
Problem
Current techniques for understanding technical architecture diagrams are time-consuming and resource-intensive, requiring manual correlation with design and interface specification documents, often leading to misunderstandings and resource wastage.
Innovation Solution
A deep learning and natural language processing-based platform that processes technical architecture diagrams to identify hierarchical objects, perform optical character recognition, and extract data, allowing for the generation of interactive diagrams that consolidate functionalities, attributes, and icons, thereby reducing human and computational resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual correlation with design and interface specification documents is used, then detailed information can be obtained, but time consumption and resource usage increase significantly
Solution Approach 1:
The patent replaces manual mechanical correlation processes with automated optical character recognition (OCR) and natural language processing (NLP) systems. The OCR engine extracts text from diagram elements, while NLP algorithms automatically correlate extracted information with design and interface specification documents, eliminating the need for manual information gathering and significantly reducing time consumption while maintaining information completeness.
Solution Approach 2:
The patent introduces an intermediary processing layer between the technical architecture diagram and the final output. This intermediary layer includes an OCR engine that extracts text from diagram elements and an NLP processor that correlates extracted information with specification documents. This intermediary automation layer acts as a mediator that handles the correlation task automatically, reducing both time consumption and potential for human error.
2Reliability
If manual correlation and modification are performed, then accuracy can be maintained through human judgment, but resource wastage and errors increase
Solution Approach 1:
The patent substitutes manual human judgment with automated NLP-based correlation systems. The NLP processor analyzes extracted text from the diagram and automatically correlates it with design and interface specification documents using natural language understanding algorithms. This automated system maintains accuracy by systematically applying correlation rules while eliminating resource wastage associated with manual review and modification processes.
Solution Approach 2:
The patent enables the system to perform self-service correlation and validation without requiring external human intervention. The automated pipeline extracts information from the diagram, correlates it with specification documents, and generates the technical architecture description independently. This self-service capability reduces resource wastage by eliminating redundant manual verification steps while maintaining reliability through automated consistency checks.
3Ease of operation
If static diagrams are used, then simplicity is maintained, but interactivity and information accessibility are limited
Solution Approach 1:
The patent creates a digital copy of the technical architecture diagram information in an interactive format. The system extracts text from diagram elements using OCR and reconstructs the architecture information as an interactive digital representation that can be queried and explored. This digital copy maintains the essential information while providing enhanced accessibility through programmatic interfaces, allowing users to query specific components and relationships without increasing the complexity of the original diagram structure.
Solution Approach 2:
The patent transitions the technical architecture representation from a two-dimensional static visual format to a multi-dimensional interactive data structure. The extracted information is organized into a structured format that enables querying from multiple dimensions (components, relationships, attributes), providing enhanced information accessibility while keeping the core diagram structure simple and unchanged.
Data Source
AI summary
A device may receive input data identifying a technical architecture diagram, a design document, an interface specification document, and technical architecture icons, and may process the input data identifying the technical architecture diagram, with a model, to determine hierarchical objects from the technical architecture diagram. The device may perform OCR and NLP of the hierarchical objects to determine blocks of data, and may compare the blocks of data and the input data identifying the design document to identify functionalities of applications. The device may compare the blocks of data and the input data identifying the interface specification document to identify attributes, and may compare the blocks of data and the input data identifying the technical architecture icons to identify icons. The device may consolidate the blocks of data, the functionalities, the attributes, and the icons into a final document, and may perform actions based on the final document.