AI Advisor Integrating Hardware Constraints into CAD Design
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Solution Overview
Problem
Current CAD tools lack integration of hardware constraints, leading to designs that may exceed physical limits of devices, potentially causing damage or failure, and expert knowledge is often inconsistent and not widely available.
Innovation Solution
An AI-based advisor system that extracts data from graphical designs and compares it to reference knowledge graphs to identify hardware constraints, providing real-time recommendations to ensure safe operation by incorporating expert knowledge and industry standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If CAD tools are used for design, then design creation is enabled, but hardware constraints are not considered leading to potential device failure
Solution Approach 1:
The patent introduces an AI-based advisor as an intermediary component between the CAD tool and the design process. This advisor queries reference knowledge graphs containing hardware constraint information and provides recommendations to the user, thereby mediating the interaction between design creation and hardware limitation awareness without fundamentally altering the core CAD functionality.
Solution Approach 2:
The system performs preliminary action by querying reference knowledge graphs before finalizing the design to identify potential hardware constraint violations. The AI advisor proactively provides warnings and recommendations during the design process, preventing potential failures before they occur rather than detecting them after deployment.
2Measurement precision
If expert knowledge is consulted for hardware constraints, then accurate constraint information is obtained, but the process is inefficient and knowledge is not consistently stored
Solution Approach 1:
The patent creates a digital copy of expert knowledge in the form of reference knowledge graphs that are queried by the AI advisor. Instead of relying on actual expert consultation, the system copies and stores expert knowledge about hardware constraints in a structured format that can be efficiently accessed and compared against design parameters.
Solution Approach 2:
The AI advisor enables the system to serve itself by automatically querying reference knowledge graphs and generating recommendations without requiring human expert intervention. The system performs self-diagnosis and self-guidance through automated comparison of design parameters against stored hardware constraint knowledge.
3Loss of information
If datasheets are used for hardware limitations, then some constraint information is available, but the format is convoluted and incomplete for dynamic operation
Solution Approach 1:
The patent extracts relevant hardware constraint information from complex datasheets and presents it in a simplified, structured format within the reference knowledge graphs. The AI advisor then extracts and compares only the necessary constraint parameters against the design, filtering out irrelevant information and presenting it in an easily operable format.
Solution Approach 2:
The system transforms the convoluted datasheet format into standardized parameter representations within the knowledge graphs. By changing the data structure from unstructured text to structured parameter tuples, the system makes the information easier to process, compare, and extract automatically during the design process.
Data Source
AI summary
A system and method for computer aided design includes constructing, by an engineering software tool for a current project, a design of a circuit or a subsystem of an industrial system comprising a plurality of hardware elements. A project knowledge graph is constructed for the current project representing an ontology for a set of elements and element relationships, wherein the set of elements include the plurality of hardware elements. A feature extraction module extracts features of the project knowledge graph related to the plurality of hardware elements. An AI-based advisor runs integrated with the engineering tool during a current project and queries one or more reference knowledge graphs for common features extracted by the feature extraction module, and responsive to identifying additional information related to hardware constraints, generates and displays recommendations to the user for the design.