Virtual Digital Twin Design for Consumer-Specific Product Variants
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Solution Overview
Problem
Current virtual twin product design systems lack the capability to automate product design development for different consumer groups based on crowd-sourced data, failing to iteratively generate unique versions of products that meet specific consumer needs.
Innovation Solution
A computer-implemented method that converts a digital twin model of a physical product into a virtual digital twin model, allowing user interactions within a virtual environment, collects user interaction and sentiment data, and inputs this data into a trained machine learning predictive model to generate unique secondary designs tailored to different user groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If virtual twin product design systems are used, then user engagement and data collection are improved, but the capability to automate product design development for different consumer groups is lacking
Solution Approach 1:
The system segments consumers into different groups based on their interaction data and sentiment analysis, then generates customized product designs for each segment. This is achieved by dividing the consumer base into distinct groups with similar preferences and behaviors, allowing automated generation of tailored design options for each segment rather than a one-size-fits-approach.
Solution Approach 2:
The system changes design parameters automatically based on consumer group characteristics. By analyzing interaction data and sentiment feedback, the system adjusts product design parameters (such as features, specifications, or configurations) to optimize designs for different consumer segments, enabling automated adaptation without manual intervention.
2Loss of information
If crowd-sourced user interactions are collected, then consumer feedback quality is improved, but the system fails to iteratively generate unique product versions
Solution Approach 1:
The system implements a feedback loop where consumer interaction data and sentiment analysis continuously inform iterative design generation. User feedback from virtual twin interactions is processed and fed back into the design system, which automatically generates improved design iterations based on the analyzed feedback, creating a continuous improvement cycle that leverages crowd-sourced data.
Solution Approach 2:
The system performs preliminary analysis of consumer interaction data and sentiment before generating design iterations. By pre-processing and understanding consumer preferences through sentiment analysis, the system prepares design generation in advance, enabling faster iterative cycles without losing valuable feedback information.
3Ease of operation
If virtual reality gamification is implemented, then user engagement is improved, but manual analysis of consumer feedback is required
Solution Approach 1:
The system replaces manual feedback analysis with automated machine learning algorithms. Instead of human analysts manually processing consumer feedback from virtual reality interactions, the system uses automated sentiment analysis and data processing algorithms to extract insights, eliminating the need for manual intervention while maintaining high user engagement through gamification.
Solution Approach 2:
The system performs self-service by automatically analyzing consumer feedback generated from virtual reality gamification. The feedback analysis system autonomously processes interaction data, extracts sentiment information, and generates design insights without requiring external manual analysis, allowing the system to serve itself in the feedback processing function.
Data Source
AI summary
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.