Multi-level Prediction for CAD Workflow Logic Nodes
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Solution Overview
Problem
Conventional CAD applications lack the capability to reliably recreate sets of patterned logic operations based on past data for current design contexts in workflow logic, leading to cumbersome, time-consuming, and error-prone manual design processes.
Innovation Solution
The implementation of multi-level prediction technology in CAD applications, which enables the prediction of node sequences comprising multiple nodes for workflow logic, thereby increasing accuracy and efficiency by aggregating past workflow data and considering expanded contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-level prediction technology is implemented, then prediction accuracy and efficiency are improved, but device complexity increases
Solution Approach 1:
The prediction system is segmented into multiple levels (first-level nodes and second-level nodes), where each level handles specific prediction tasks. This segmentation allows the system to achieve high prediction accuracy through specialized sub-systems while managing complexity by dividing the overall prediction function into manageable, modular components.
Solution Approach 2:
The patent introduces a hierarchical dimension to the prediction system by adding second-level nodes that extend beyond the traditional single-level structure. This dimensional expansion enables multi-level predictions that capture complex patterns in workflow data, improving accuracy without requiring a complete redesign of the entire system.
2Productivity
If manual workflow logic design is used, then system complexity is reduced, but productivity and time efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically generating workflow logic nodes based on historical workflow data and identified patterns. Instead of requiring users to manually design each workflow step, the system pre-computes and suggests optimal workflow sequences, significantly reducing design time and improving productivity while maintaining system manageability.
3Productivity
If patterned logic operations are automatically recreated, then productivity is improved, but reliability of predictions may worsen without sufficient data
Solution Approach 1:
The system incorporates feedback mechanisms that continuously monitor workflow data and adjust prediction models accordingly. By analyzing actual workflow outcomes and comparing them with predicted patterns, the system refines its predictions over time, ensuring that automation reliability improves with accumulated data while maintaining high productivity through continuous learning and adaptation.
Data Source
AI summary
A computing system may include a logic construction engine configured to construct, via multi-level prediction, workflow logic to process a computer-aided design (CAD) model. The logic construction engine may do so by identifying a multi-node sequence inserted into the workflow logic, aggregating past workflow data specific to the multi-node sequence, determining a node prediction in the workflow logic for the multi-node sequence based on the aggregated past workflow data, and providing the node prediction as a suggested insertion for the workflow logic.