Machine Learning Module for CAD User Desirability Insights
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Solution Overview
Problem
Existing CAD systems lack the ability to predict desired outcomes and provide well-informed suggestions for user desirability, relying on limited 'yes' or 'no' indicators, which increases workload when knowledge changes and fails to simplify the design process.
Innovation Solution
A computer-implemented method and system that uses a machine learning module trained on records to learn user desirability based on CAD-identifiers, allowing continuous learning from user acceptance or decline of suggestions, thereby simplifying the design process by providing well-informed and limited suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning is used to assess outcome desirability, then prediction capability is improved, but the system complexity increases
Solution Approach 1:
The patent introduces a machine learning module as an intermediary component that bridges the gap between CAD knowledge models and desirability assessment. This module is trained on historical project data and user feedback, enabling it to predict outcome desirability without requiring complex integration of multiple assessment systems. The ML module acts as a specialized mediator that handles the complexity internally while presenting simple predictions to users.
2Device complexity
If limited 'yes' or 'no' indicators are used for desirability assessment, then the system simplicity is maintained, but the insight quality deteriorates
Solution Approach 1:
The patent transitions from binary yes/no indicators to a multi-dimensional assessment framework. The machine learning module generates predictions across multiple dimensions including desirability scores, confidence levels, and explanatory factors. This dimensional expansion provides richer insights while the system maintains simplicity through automated processing of these multiple dimensions without requiring complex user interpretation.
3Reliability
If knowledge changes require template and model updates, then knowledge accuracy is improved, but workload increases
Solution Approach 1:
The patent implements a feedback mechanism where user acceptance or decline of suggestions is continuously fed back to retrain the machine learning module. This creates a self-improving system that automatically adapts to knowledge changes through usage patterns rather than requiring manual template updates. The feedback loop enables the system to learn from actual user behavior, maintaining knowledge accuracy while eliminating the manual workload of updating templates and models.
4Manufacturing precision
If user modeling of different parameters is required, then design precision is improved, but the process complexity increases
Solution Approach 1:
The patent enables the machine learning module to automatically perform parameter modeling and optimization without requiring users to manually model different parameters. The system self-services by learning optimal parameter relationships from training data and automatically applying this knowledge to provide precise design suggestions. This eliminates the need for users to understand or perform complex parameter modeling while maintaining high design precision through data-driven insights.
Data Source
AI summary
The present invention pertains to a method, system and computer program product for providing insights on user desirability on database-stored computer-aided design (CAD) knowledge models, as well as use thereof for managing CAD-knowledge models of cooling installations.