Profile-Based Prompt Engineering for Industrial Design Customization
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Solution Overview
Problem
Existing industrial design applications struggle with maintaining user-specific customization information due to storage constraints, requiring users to repeatedly select configuration options and lack centralized repositories for past customizations, especially for novice users unfamiliar with numerous parameters.
Innovation Solution
An industrial design application utilizing a Generative Artificial Intelligence (GAI) model, such as a Large Language Model (LLM) or Multi-Modal Model (MMM), generates user-specific customized designs based on learned preferences and industry standards, reducing the need for repeated user selections and storing extensive user preference data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If user-specific customization information is stored in a centralized repository, then user preferences can be maintained across projects, but storage costs and data management complexity increase
Solution Approach 1:
The patent extracts and stores only the essential user preference parameters (such as component brand preferences, configuration templates, and selection patterns) rather than storing complete design histories. This selective extraction reduces data volume while preserving the core information needed for personalization.
Solution Approach 2:
Instead of storing complete customization data, the system creates a simplified representation or copy of user preferences in a compact format. This allows the system to reference and apply preferences without storing the full historical data, reducing storage requirements while maintaining functionality.
2Manufacturing precision
If users manually select configuration options for each project, then customization accuracy is maintained, but time consumption and operational complexity increase
Solution Approach 1:
The system performs preliminary actions by automatically applying user's historical preference patterns and configuration templates to new projects before the user completes their selections. This pre-configures common parameters based on learned preferences, reducing the time users need to spend on manual selection while maintaining accuracy through user verification.
Solution Approach 2:
The system provides self-service functionality where it automatically retrieves and applies configuration settings based on the user's identity and historical data. This eliminates the need for users to manually re-select common options, as the system serves itself by retrieving appropriate configurations from stored preference patterns.
3Adaptability or versatility
If the system provides comprehensive configuration options, then design flexibility is improved, but user confusion and difficulty in selection increase
Solution Approach 1:
The system applies local quality by providing different levels of configuration detail at different stages. For novice users, the system presents simplified pre-configured options; for experienced users, it provides access to comprehensive parameters. This localized adaptation of interface complexity ensures ease of operation while maintaining design flexibility when needed.
Solution Approach 2:
The configuration interface dynamically adapts its complexity based on user interaction patterns and expertise level. The system learns from user behavior to adjust the number and type of options presented, making the interface easier to operate for common tasks while preserving access to comprehensive configurations for specialized requirements.
4Productivity
If a centralized repository stores all past customizations, then user-specific designs are readily available, but system complexity and maintenance difficulty increase
Solution Approach 1:
The patent segments the centralized repository into modular preference categories (component preferences, configuration templates, safety standards, etc.). This segmentation allows the system to manage and retrieve specific preference types independently, reducing overall system complexity while maintaining fast access to user-specific designs through targeted queries.
Data Source
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AI summary
The disclosure relates to an industrial design application that provides a customized user experience. In response to a user request for a design of an industrial system, the industrial design application selects generic base designs from a base design repository. Embodiments include a Generative Artificial Intelligence (GAI) model trained to generate user-customizations of the generic base designs. Once a user receives a customized base design, the user may make modifications to the customized base designs. Finalized designs may be provided to the GAI model for training on common selections made by users of the industrial design application.