Generative AI Shopping Environment Personalization From User Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing retail environments lack personalized and immersive shopping experiences, with a need for innovative solutions that leverage AI and ML to enhance customer interaction and satisfaction.
Innovation Solution
A system utilizing generative AI models to customize interactive shopping environments based on user input, feedback, and preferences, enabling dynamic generation and refinement of design elements, and seamless transition to purchasing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional brick-and-mortar stores integrate digital elements to enhance customer interaction, then customer engagement improves, but system complexity increases
Solution Approach 1:
The patent introduces an AI model as an intermediary between the user and the shopping environment customization system. The AI model processes user inputs and generates personalized shopping environment configurations, acting as a mediator that simplifies the interaction while managing the underlying system complexity. This allows customers to engage with sophisticated digital elements through natural interactions without needing to understand the complex systems behind them.
2Adaptability or versatility
If generative AI models are used to dynamically customize shopping environments based on user feedback, then personalization quality improves, but computational resources required increase
Solution Approach 1:
The system performs preliminary actions by pre-training the generative AI model on extensive shopping environment data and user preference patterns before actual use. This pre-processing work is done offline, so when the system operates with users, the AI model can quickly generate personalized environments without requiring excessive real-time computational resources. The model has already learned the complex relationships between user feedback and environment customization during the training phase.
Solution Approach 2:
The patent utilizes parameter changes in the AI model's processing to balance personalization quality with computational efficiency. By adjusting model parameters such as generation complexity, detail level, and refinement iterations based on user needs and system capacity, the system can deliver high-quality personalized experiences while managing computational resource consumption. The model can dynamically adjust the level of customization detail provided based on the situation.
3Productivity
If visual representations are converted into user selectable elements for seamless purchasing, then conversion efficiency improves, but system integration complexity increases
Solution Approach 1:
The patent merges the visualization function and the purchasing function into a unified interactive system. The visual representations generated by the AI model are directly integrated with the e-commerce platform's purchasing mechanisms, creating a seamless experience where users can transition from viewing personalized shopping environments to making purchases without leaving the interface. This integration consolidates multiple system functions into a cohesive workflow, improving conversion efficiency while managing integration complexity through unified architecture.
Data Source
AI summary
A method of customizing an interactive shopping environment for a user includes receiving user input associated with a shopping session. A design element is generated based on at least a portion of the received user input. The interactive shopping environment including a visual representation of the design element is generated that is configured to enable user interaction with the visual representation within the interactive shopping environment. User feedback is received associated with the visual representation. The interactive shopping environment is updated based at least in part on the received user feedback to generate a revised interactive shopping environment including a revised visual representation of the design element. Responsive to receiving user input indicating approval of the revised interactive shopping environment, the revised visual representation is converted into a user selectable element.