Digital Twin Product Recommendation Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies fail to effectively correlate and integrate digital and physical experiences, leading to inefficient product recommendations in product purchasing systems.
Innovation Solution
The method generates product recommendations using digital twin models of users, geographical locations, event venues, and clothing items, creating customized recommendations for events and generating depictions of recommended products being worn by the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional product recommendation systems are used, then product recommendations are provided, but the correlation between digital and physical experiences is insufficient
Solution Approach 1:
The patent creates digital twin models that are virtual copies of physical entities including users, clothing items, event venues, and geographical locations. These digital twins enable the system to simulate and correlate digital experiences (online shopping, virtual try-on) with physical experiences (actual wear, event attendance) without losing information about user preferences, item characteristics, or contextual factors. The digital twin of the user captures physical attributes and preferences, while the digital twin of the clothing item preserves material properties and style characteristics, allowing accurate recommendation generation that bridges digital and physical domains.
2Measurement precision
If digital twin models are generated for users, locations, venues, and clothing items, then product recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex recommendation system into distinct digital twin components: user digital twin, clothing item digital twin, event venue digital twin, and geographical location digital twin. Each digital twin is an independent module that captures specific aspects of its corresponding entity. This segmentation allows the system to manage complexity by creating specialized representations for each entity type while maintaining overall system coherence through their interrelationships in the recommendation generation process.
Solution Approach 2:
The digital twin models serve as intermediaries between the physical world and the digital recommendation system. Rather than directly processing complex physical attributes and contextual information, the system uses digital twins as mediating representations that simplify the correlation process. The digital twin of the user acts as an intermediary that translates physical user characteristics into digital format, while the digital twin of the clothing item mediates between physical item properties and virtual try-on experiences, reducing overall system complexity.
3Ease of operation
If customized product recommendations are generated with depictions, then user shopping experience is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by generating digital twin models in advance and pre-computing recommendation scenarios. The system creates digital representations of users, clothing items, and event contexts before the actual recommendation query is made. When a user seeks recommendations, the system leverages these pre-established digital twins to rapidly generate customized recommendations with depictions, significantly reducing processing time compared to computing everything in real-time.
Solution Approach 2:
The system uses digital copies (digital twins) of users and clothing items to simulate the shopping experience and generate recommendations. Instead of physically trying on items or manually evaluating compatibility, the system creates virtual depictions by compositing images of the user with recommended clothing items in the context of the event venue. This copying approach provides an enhanced shopping experience with visualizations while maintaining efficient processing speeds.
Data Source
AI summary
Using a digital twin model of a user, a digital twin model of a geographical location, a digital twin model of an event venue located at the geographical location, and a plurality of digital twin models of clothing items, a product recommendation customized to the user and a planned event is generated, the planned event planned to occur at the event venue. A product recommendation depiction is generated, the product recommendation depiction comprising a depiction of the product recommendation being worn by the user at the planned event. An answer to a natural language query regarding the product recommendation depiction is generated.