AI Validation of Industrial Design Configurations in Real Time
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Solution Overview
Problem
Existing industrial design applications do not provide real-time feedback or suggestions when users make suboptimal or uncommon configuration selections, leading to increased costs, longer lead times, and difficulty in maintenance due to varying user experience levels and the resource-intensive maintenance of databases with common preferences.
Innovation Solution
An industrial design application utilizing a Generative Artificial Intelligence (GAI) model to validate and suggest alternate selections, updating base designs to align with common user choices, and providing real-time feedback on design selections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If engineers make independent design selections without guidance, then design freedom and flexibility are improved, but design quality and optimality deteriorate due to varying experience levels
Solution Approach 1:
The system implements feedback by training a GAI model on historical design submissions from multiple users to learn common preferences. When a user makes a design selection, the model provides real-time feedback indicating whether the selection aligns with common industry practices. This feedback mechanism maintains design freedom while improving design quality by guiding users toward proven configurations without restricting their ability to make independent choices.
Solution Approach 2:
The system enables self-service by allowing the GAI model to autonomously analyze user selections and generate guidance recommendations without requiring manual review by experts. The model independently determines whether a selection is uncommon and automatically provides suggestions, reducing the need for human intervention while maintaining high-quality design guidance.
2Reliability
If a database of common preferences is maintained to guide users, then design quality is improved, but system complexity and maintenance costs worsen
Solution Approach 1:
The system replaces the traditional mechanical database approach with a GAI model-based solution. Instead of maintaining a static database of common preferences that requires manual updates and synchronization, the patent uses a trained neural network model that automatically learns from historical data and provides guidance through intelligent inference. This substitution eliminates the need for complex database maintenance while preserving design quality.
Solution Approach 2:
The system changes the fundamental parameter of how common preferences are stored and accessed. Rather than maintaining a structured database with explicit preference rules, the patent transforms the knowledge into the weights and parameters of a trained GAI model. This parameter transformation allows the system to provide guidance through pattern recognition rather than rule-based lookup, significantly reducing maintenance complexity.
3Adaptability or versatility
If uncommon design selections are made, then design customization is improved, but lead time and costs worsen due to manufacturing and inventory issues
Solution Approach 1:
The system applies preliminary anti-action by proactively identifying uncommon design selections before they are finalized and submitted for manufacturing. The GAI model detects deviations from common preferences during the design phase and provides guidance to prevent the selection of configurations that would cause manufacturing delays. This early intervention prevents problems rather than reacting to them later in the process.
Solution Approach 2:
The system performs preliminary action by guiding users toward common, well-supported configurations before the design is committed to manufacturing. The GAI model analyzes selections in real-time and suggests alternatives that have proven successful in similar applications, ensuring that the final design uses components and configurations that are readily available in inventory and can be manufactured efficiently.
4Productivity
If design selections are not validated, then design process speed is improved, but error detection and quality assurance worsen
Solution Approach 1:
The system implements rapid feedback by integrating the GAI model directly into the design workflow. When a user makes a selection, the model immediately evaluates it against learned patterns from historical data and provides instant guidance. This real-time feedback maintains design process speed by not requiring manual review steps while simultaneously improving error detection through automated analysis of each selection.
Solution Approach 2:
The system enables self-service validation where the GAI model autonomously performs quality checks on user selections without requiring external validation. The model independently determines whether selections are appropriate based on historical performance data and provides self-contained guidance, eliminating the need for separate review processes while maintaining high detection accuracy.
Data Source
AI summary
The present disclosure describes systems and methods for reviewing user selections in an industrial design application. Embodiments include leveraging a Generative Artificial Intelligence (GAI) model to review the selections. The GAI model is trained to recognize common selections of configuration options among users of the industrial design application. The disclosure describes generating prompts requesting GAI model validation of industrial design selections, including requesting alternate selection suggestions for irregular selections. The GAI model may respond with one or more alternate selection suggestions, which may be included in a notification displayed to the user.