18088080. CLASSIFICATION-BASED PRODUCT DESIGN USING VIRTUAL DIGITAL TWIN MODELS simplified abstract (International Business Machines Corporation)
Contents
CLASSIFICATION-BASED PRODUCT DESIGN USING VIRTUAL DIGITAL TWIN MODELS
Organization Name
International Business Machines Corporation
Inventor(s)
Sarbajit K. Rakshit of Kolkata (IN)
Sathya Santhar of Ramapuram (IN)
Sridevi Kannan of Katupakkam (IN)
CLASSIFICATION-BASED PRODUCT DESIGN USING VIRTUAL DIGITAL TWIN MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18088080 titled 'CLASSIFICATION-BASED PRODUCT DESIGN USING VIRTUAL DIGITAL TWIN MODELS
Simplified Explanation: The patent application describes a system and method for automatically generating product designs based on user interactions with a virtual digital twin model of a physical product.
- Converting a digital twin model of a physical product to a virtual digital twin model for user interactions.
- Collecting user interaction data and sentiment data from virtual interactions with the virtual digital twin model.
- Using machine learning predictive models to generate different secondary designs of the physical product based on user data.
Key Features and Innovation:
- Utilizes digital twin technology to create virtual models for user interactions.
- Incorporates user sentiment data to inform the design generation process.
- Employs machine learning predictive models to create customized product designs for different user groups.
Potential Applications: This technology can be applied in industries such as product design, manufacturing, and virtual reality simulations.
Problems Solved: Addresses the need for personalized product designs based on user preferences and interactions.
Benefits:
- Enables the automatic generation of product designs tailored to different user groups.
- Enhances user engagement and satisfaction with the design process.
- Streamlines the product development cycle by leveraging user data for design optimization.
Commercial Applications: The technology can be used in industries such as consumer goods, automotive, and architecture for creating customized product designs and improving user experiences.
Questions about the Technology: 1. How does the system ensure the accuracy and relevance of user interaction data in generating product designs? 2. What are the potential limitations or challenges in implementing this technology in real-world design processes?
Frequently Updated Research: Stay updated on advancements in machine learning algorithms for predictive modeling in product design applications.
Original Abstract Submitted
A system and method of automatically generating product designs is provided. In embodiments, methods include converting a digital twin model of a physical product having a primary design to a virtual digital twin model enabling user interactions with features of the virtual digital twin model in a virtual environment; collecting user interaction data generated from virtual interactions of users with the features of the virtual digital twin model in the virtual environment; generating sentiment data indicating a sentiment of the users associated with the virtual interactions of the users with the features of the virtual digital twin model; and inputting the user interaction data, the sentiment data, and different groups of the users into a trained machine learning (ML) predictive model, thereby generating, as an output of the ML predictive model, a different secondary design of the physical product for each of the different groups of users.
