Generative Product Images for Missing Catalog Variants
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Solution Overview
Problem
Concierge systems face challenges in providing representative product images due to the high cost and effort involved in manually obtaining images for various product variants, especially for categories like meats and seafoods, which affects customer engagement and operational efficiency.
Innovation Solution
An online concierge system employs a fine-tuned generative image model trained on a small set of representative images and textual tokens to generate realistic images for products lacking them, enabling generation of different variants such as quantity or packaging variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual photographing or third-party hiring is used to obtain product images, then image quality and authenticity are improved, but time cost and monetary expense increase significantly
Solution Approach 1:
The patent uses generative image models to create synthetic copies of product images based on textual descriptions and category information, eliminating the need for manual photographing while maintaining visual quality and authenticity for catalog purposes
Solution Approach 2:
The patent replaces the mechanical process of manual photographing and physical product handling with an automated AI-based image generation system that processes textual inputs to produce realistic product images
2Reliability
If manual photographing or third-party hiring is used to obtain product images, then image quality and authenticity are improved, but monetary expense increases significantly
Solution Approach 1:
The patent uses generative image models to create synthetic copies of product images based on textual descriptions and category information, eliminating the need for manual photographing while maintaining visual quality and authenticity for catalog purposes
Solution Approach 2:
The patent employs cost-effective AI computation to generate images on-demand rather than investing in expensive manual photography services, using affordable computational resources to produce sufficient-quality images for online catalogs
3Productivity
If representative images are not provided for products, then operational cost is reduced, but customer engagement and consideration decrease
Solution Approach 1:
The patent implements a self-service image generation system where the concierge system automatically creates product images using AI models based on product data, eliminating the need for manual image acquisition while ensuring all products have engaging visual representations
Solution Approach 2:
The patent creates a universal image generation system that handles multiple product categories and variants through a single AI model, providingengaging images across the entire catalog without requiring separate manual photography processes for each product
4Adaptability or versatility
If multiple product variants with different quantities, packaging, or lighting are required, then product representation completeness is improved, but the complexity and cost of obtaining images increases many-fold
Solution Approach 1:
The patent uses a dynamic image generation system that can adaptively create different product variants by modifying textual descriptions and parameters in the AI model, allowing flexible generation of various quantities, packaging types, and lighting conditions without fixed acquisition processes
Solution Approach 2:
The patent creates a universal image generation system that handles multiple product categories and variants through a single AI model, providingengaging images across the entire catalog without requiring separate manual photography processes for each product
Data Source
AI summary
An online concierge system trains a fine-tuned generative image model for distinct categories of items based on a generative image model that takes a textual query as input and outputs and an associated image. Training of the fine-tuned generative image model is additionally based on a small set of representative images associated with the various categories, as well as textual tokens associated with the categories. Once trained, the fine-tuned generative image model can be used to generate realistic representative images for items in a database of the online concierge system that are lacking associated images. The fine-tuned model permits the generation of different variants of an item, such as different quantities or amounts, different packaging or packing density, and the like.