Engineering Schematic Generation Using Natural Language and Knowledge Graphs
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Solution Overview
Problem
Existing computer-aided design tools for engineering schematics require extensive user training and manual effort to translate high-level design concepts into detailed schematics, lacking the ability to understand natural language instructions and maintain schematics efficiently.
Innovation Solution
A system and method that uses natural language processing and knowledge graphs to interpret user inputs, generate, and modify engineering schematics, ensuring compliance with industry standards by employing a large language model and knowledge graph queries to automate the drawing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional computer-aided design tools are used to generate engineering schematics, then the schematics can be created with standard components and libraries, but the process requires extensive user training and manual effort
Solution Approach 1:
The patent replaces the mechanical interaction model (mouse clicks, menu navigation, drag-and-drop operations) with a natural language processing system. The language model interprets user intent expressed in natural language and automatically generates schematic representations, eliminating the need for users to learn complex software interfaces or perform manual component placement operations.
Solution Approach 2:
The system enables users to express design intent in their own natural language without requiring them to learn the tool's specific commands or interface conventions. The language model and knowledge graph work together to automatically translate user intent into proper schematic representations, allowing users to serve themselves without extensive training on the software's operational mechanics.
2Adaptability or versatility
If manual methods are used to translate high-level design concepts into detailed schematics, then flexibility in expression is maintained, but the process is time-consuming and iterative
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary layer between the user's natural language expression and the schematic generation process. The knowledge graph stores domain-specific concepts, relationships, and rules, enabling the system to interpret high-level design concepts and automatically translate them into detailed schematics while maintaining design flexibility and adhering to domain conventions.
Solution Approach 2:
The system performs preliminary processing by maintaining a knowledge graph that pre-encodes domain knowledge, component relationships, and schematic conventions. When a user provides high-level design concepts, the system can quickly retrieve and apply relevant pre-processing rules and relationships, eliminating the need for iterative manual adjustments and significantly accelerating the translation process.
3Ease of operation
If existing schematic tools are used, then standard components and libraries are available, but the ability to understand and interpret natural language instructions is lacking
Solution Approach 1:
The patent replaces traditional menu-driven or command-based interaction mechanisms with a natural language processing system. Users can interact with the schematic generation tool using conversational language, and the language model interprets these instructions to automatically generate and modify schematics, significantly simplifying the user interaction model while increasing automation capability.
4Ease of repair
If conventional tools are used for schematic maintenance and change management, then existing schematics can be modified, but complete understanding of the system schematic is a prerequisite
Solution Approach 1:
The knowledge graph serves as an intermediary that maintains the semantic relationships and contextual understanding of the schematic system. When users request modifications, the system queries the knowledge graph to understand the implications of changes, ensuring that maintenance operations are performed correctly without requiring users to have complete understanding of the entire system architecture.
Data Source
AI summary
A method for generating and modifying engineering schematics is provided. The method includes receiving a natural language input via a user interface, the input containing a user intent to perform operations on an engineering schematic. The method generates a structured representation of the input using a language model, identifying components, actions, and attributes. Knowledge graph queries are generated based on this representation, compatible with a standardized ontology. These queries are executed to retrieve component, connection, and attribute data from the knowledge graph. A system representation of the schematic is generated using the retrieved data and specified actions. The method then renders a visual representation of the engineering schematic based on this system representation and outputs it via the user interface. This approach allows for intuitive schematic modification using natural language processing and knowledge graph technologies.