Digital Twin ROI Modeling for Contextual Product Validation
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Solution Overview
Problem
Conventional systems fail to quantify and contextualize decision-making metrics for purchasing products, limiting users' ability to make accurate purchase decisions, especially for products not yet owned, by not providing a quantitative value corresponding to user interactions.
Innovation Solution
A computer-implemented method using a convolutional neural network (CNN) model to analyze user interactions, generate digital twins or 3D physical products, and calculate ROI based on user performance metrics, recommending purchases when the ROI exceeds the product cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems are used for product purchase decisions, then users can access thousands of products, but users cannot accurately quantify and contextualize decision-making metrics for products not yet owned
Solution Approach 1:
The system creates digital twins (virtual copies) of physical products to simulate user interactions and measure interaction values. These digital twins replicate product functionality and user experience characteristics, enabling quantification of user interaction metrics without requiring actual product ownership or physical interaction with the real product.
Solution Approach 2:
The patent replaces physical product interaction with virtual simulation through digital twins. Instead of requiring users to physically interact with products to gather decision-making data, the system uses computational models and algorithms to simulate interactions and generate contextualized metrics, substituting mechanical/physical processes with information-based processes.
2Reliability
If digital twins are generated and analyzed, then accurate purchase decisions can be made, but system complexity increases significantly
Solution Approach 1:
The system implements a multi-functional platform that combines digital twin generation, interaction simulation, metric calculation, and recommendation generation within a single integrated architecture. This universal system handles multiple tasks (product modeling, user behavior simulation, ROI calculation, and decision support) that would otherwise require separate systems, managing complexity through consolidation rather than proliferation of components.
Solution Approach 2:
The digital twin serves as an intermediary between the user and the actual product. Instead of directly analyzing complex product characteristics and user needs, the system uses the digital twin as a mediator to simulate interactions and generate decision-making metrics, simplifying the overall system architecture by introducing this intermediate computational layer.
Data Source
AI summary
Embodiments determine at least one product interest based on user information, receive historical data from a plurality of users, train a convolutional neural network (CNN) model based on the historical data, determine a plurality of user task interactions related to the at least one product interest based on the trained CNN model, monitor the user task interactions for a user product related to the at least one product interest over a predetermined period of time, generate a digital twin of a comparative product to the user product based on the monitored user interactions, generate user performance metrics including a return on investment (ROI) of the generated digital twin, and generate a recommendation to purchase a product of the at least one product interest based on the generated user performance metrics including the ROI being greater than a cost of the product.