Warranty Classification via Multi-Modal ML
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing model for distributing extended warranties is inefficient, as it relies on manual identification and selection, leading to inflated prices and limited automation, especially for products not manually reviewed for warrantability.
Innovation Solution
A computer-implemented method using machine learning classifiers to automatically select and offer appropriate warranty plans by analyzing webpage data, including text and image content, allowing for direct offering to online shoppers without retailer partnerships, utilizing a browser extension to integrate warranty offers seamlessly into the shopping experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification and selection of warranties is performed on a SKU-by-SKU basis, then appropriate warranties can be identified for each product, but the process is inefficient and prices are inflated
Solution Approach 1:
The patent replaces the manual mechanical process of warranty selection with an automated machine learning system. The ML model analyzes product data, images, and descriptions to automatically classify products and recommend appropriate warranties, eliminating the need for manual SKU-by-SKU review while maintaining accurate warranty matching
Solution Approach 2:
The system enables self-service by allowing the ML model to autonomously perform warranty identification and classification without human intervention. The automated system processes products through the warranty distribution flow independently, significantly improving efficiency while preserving selection accuracy
2Measurement precision
If manual review is performed for each product, then warrantability can be determined accurately, but products not manually reviewed cannot be offered warranties
Solution Approach 1:
The machine learning system provides universal applicability across all products in the catalog. Unlike manual review which is limited to selected products, the automated ML system can process and determine warrantability for any product type, expanding coverage scope while maintaining determination accuracy through consistent application of the classification model
3Device complexity
If retailers retain 60-70% of extended warranty prices, then the warranty distribution model is simple, but the final price to consumers is substantially inflated
Solution Approach 1:
The patent extracts the warranty distribution process from the traditional retailer-centric model. By using automated ML classification and enabling direct-to-consumer warranty sales, the system removes the intermediary retention layer, allowing consumers to pay only for the actual warranty cost rather than inflated prices that include retailer markup
4Measurement precision
If multiple machine learning classifiers are used to analyze different data types, then product classification accuracy is improved, but the system complexity increases
Solution Approach 1:
The system segments the classification task into multiple specialized ML models, each handling specific data types (product title, description, images, specifications). This segmentation allows each model to focus on its data type's unique characteristics, improving overall classification accuracy while organizing system complexity into manageable, modular components
Data Source
AI summary
Some embodiments described herein relate to a computer-implemented method that includes receiving an indication of text content and an indication of image content appearing on a webpage that offers a product for sale. A first trained machine learning classifier can be applied to the text content to produce a first classification of the product. A second trained machine learning classifier can be applied to the image content to produce a second classification. A trained combination machine learning classifier can be applied to the first classification and the second classification. The combination machine learning classifier can be configured to predict a third class of the product. A warranty can be defined and/or offered based on the third class of the product.