Commerce System Optimizing Shopping Lists via Weighted Preferences
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Solution Overview
Problem
Consumers face difficulties in making informed purchasing decisions due to lack of access to comprehensive, reliable, and objective product information, while retailers struggle to effectively evaluate marketing promotions and optimize pricing strategies, leading to inefficiencies in sales and profit maximization.
Innovation Solution
A commerce system that collects and stores product information, allows consumers to create shopping lists with weighted preferences, generates individualized discounts, and optimizes purchasing decisions by aggregating products from multiple retailers based on consumer preferences and available discounts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If consumers rely on retailer-sponsored product information, then they can access product details, but the information is subjective and incomplete, limiting their ability to make informed purchasing decisions
Solution Approach 1:
The patent introduces a consumer service provider as an intermediary between retailers and consumers. This intermediary collects product information from multiple retailers, processes it through a personal assistant engine, and delivers objective, comprehensive information to consumers. The intermediary resolves the contradiction by filtering out retailer bias and aggregating data from multiple sources to provide reliable, complete product information.
Solution Approach 2:
The patent segments the information flow into distinct components: product information collection from multiple retailers, processing through a personal assistant engine that applies consumer preferences and product models, and delivery of customized information. This segmentation allows each component to specialize in its function, ensuring comprehensive data gathering while maintaining objectivity through systematic processing.
2Quantity of substance
If retailers use mass marketing promotions, then they can reach many consumers, but they cannot effectively evaluate promotion effectiveness or optimize pricing strategies
Solution Approach 1:
The patent implements a feedback mechanism where the consumer service provider tracks consumer responses to promotions, purchase behaviors, and preference data. This feedback loop provides retailers with actionable information about promotion effectiveness, enabling them to optimize future marketing strategies and pricing decisions while maintaining broad consumer reach.
Solution Approach 2:
The system performs preliminary actions by collecting and analyzing consumer preference data and product information before executing marketing promotions. This advance preparation allows retailers to target promotions more effectively and evaluate their potential success before full deployment, resolving the contradiction between broad reach and effective evaluation.
3Reliability
If consumers manually research products at multiple retailers, then they can compare prices and features, but they spend excessive time and effort on shopping research
Solution Approach 1:
The patent enables self-service by allowing consumers to input their preferences into a personal assistant engine, which then automatically performs the research and comparison work across multiple retailers. The system retrieves product information, applies consumer preferences and product models, and delivers customized recommendations, eliminating the need for manual research while maintaining high accuracy.
Solution Approach 2:
The consumer service provider performs multiple functions in one system: collecting product information from various retailers, processing data through a personal assistant engine, applying consumer preferences and product models, and delivering customized recommendations. This multi-functional approach consolidates what would otherwise require separate manual tasks into a single automated service.
4Productivity
If retailers offer individualized discounts to maximize profit, then they can optimize pricing strategies, but they lack access to consumer preference data needed for effective personalization
Solution Approach 1:
The consumer service provider acts as an intermediary that collects and processes consumer preference data, then shares relevant insights with retailers. This intermediary enables retailers to offer individualized discounts and optimize pricing strategies by providing them with processed consumer preference information without requiring retailers to directly handle sensitive consumer data collection.
Data Source
AI summary
A commerce system has a plurality of retailers offering products for sale. Product information associated with the products is collected and stored in a central database. A consumer uses a website to create a shopping list with weighted preferences for product attributes. An individualized discount is generated by a consumer service provider for products on the shopping list directed to the consumer. The shopping list with all products aggregated for one retailer is optimized based on the product information in the database, the weighted preferences for the product attributes, and the individualized discounts. The consumer uses the optimized shopping list to assist with purchasing decisions. An incremental profit can be determined based on an aggregation of the products on the optimized shopping list. Purchasing decisions within the commerce system are controlled by enabling the consumer to select the products for purchase from the retailer.