Dynamic Shopping List Generation via Location and Schedule Analysis
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Solution Overview
Problem
Consumers face inefficiencies in managing recurring purchases due to reliance on ad-hoc shopping lists, leading to forgotten items, unnecessary trips, and missed rewards opportunities, as they struggle to keep track of consumable items across multiple stores and schedules.
Innovation Solution
A location-based and calendar-based computer-implemented method generates electronic shopping lists by analyzing item-level transaction data to determine purchase intervals and identifying nearby merchants, ensuring consumers receive timely reminders based on their schedule and location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If consumers rely on ad-hoc shopping lists and memory, then they can maintain simple manual tracking, but they forget items and make unnecessary trips
Solution Approach 1:
The system automatically generates shopping lists by monitoring purchase intervals and consumer schedules without manual intervention. The computer system self-updates the shopping list based on transaction data and calendar information, eliminating the need for consumers to manually maintain lists while ensuring accuracy and reliability.
Solution Approach 2:
The system continuously monitors purchase intervals and compares them against current dates and consumer schedules to dynamically update shopping lists. This feedback mechanism ensures the shopping list remains accurate and relevant by automatically incorporating new purchase patterns and schedule changes.
2Adaptability or versatility
If consumers manually update shopping lists, then they can adapt to changing needs, but they spend time and effort on list maintenance
Solution Approach 1:
The system automatically adapts the shopping list to changing consumer needs by analyzing purchase interval data and schedule information. It dynamically updates item priorities and inclusion based on consumption rates and upcoming dates without requiring consumer time or effort for manual updates.
Solution Approach 2:
The system performs preliminary analysis of purchase patterns and schedule constraints to pre-generate optimized shopping lists before consumers need them. This advance preparation ensures the lists are already adapted to upcoming needs and schedule availability, eliminating the need for last-minute manual adjustments.
3Adaptability or versatility
If consumers shop at multiple merchants for different items, then they can access better selection and prices, but they miss rewards opportunities and incur travel costs
Solution Approach 1:
The system merges shopping trips by consolidating items from multiple merchants into single optimized lists based on location and schedule. It combines nearby merchants into one trip when feasible, reducing travel time and energy expenditure while still providing access to diverse item selection and price comparisons across different merchants.
Solution Approach 2:
The system optimizes merchant selection based on local conditions such as consumer location, nearby stores, and schedule constraints. It dynamically adjusts which merchants to include in each shopping trip based on proximity and relevance, ensuring the best local options are utilized while minimizing travel requirements.
4Productivity
If consumers do not track purchase intervals, then they can avoid frequent shopping trips, but they forget to purchase items before they are needed
Solution Approach 1:
The system continuously monitors purchase intervals by analyzing transaction data and provides feedback through automatically updated shopping lists. It calculates consumption rates and predicts when items will be needed, ensuring consumers receive timely reminders without needing to manually track or remember purchase intervals.
Solution Approach 2:
The system performs preliminary calculation of purchase intervals and generates shopping list reminders before items are actually needed. By analyzing historical purchase data in advance, it proactively schedules shopping reminders to ensure items are purchased at optimal times, preventing both early and late purchases.
Data Source
AI summary
A host computer is coupled to a source of item-level transaction data and a consumer computing device such as a Smartphone. A purchase program determines respective purchase intervals of respective items purchased by consumer from respective merchants using respective item-level electronic transaction data representing prior purchases of respective items by consumer. A shopping list program receives a location of consumer and/or data of an electronic calendar of consumer at host computer, identifies at least one merchant within a pre-determined distance of the received location and/or at which consumer has time to shop as determined from received calendar or schedule data, generates at least one electronic shopping list comprising at least one item previously purchased by consumer from the at least one merchant, and transmits the electronic shopping list from the host computer through a network to a computing apparatus of consumer.