Receipt Analysis System for Personalized Shopping Lists
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Solution Overview
Problem
Many customers are unfamiliar with online shopping technology or lack the time and motivation to create an account with a store, thereby missing out on additional services and benefits offered by retailers, such as personalized shopping features and purchase suggestions.
Innovation Solution
A computer system processes customer receipts to analyze purchase patterns and record purchase information, associating it with a customer account, and provides personalized purchase suggestions based on historical data, allowing customers to receive reminders about frequently bought items during online shopping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If customers are required to create an account with the store to access personalized shopping features and purchase suggestions, then the store can provide personalized shopping experiences and increase customer engagement, but customers may be discouraged from adopting online shopping due to the additional time and effort required
Solution Approach 1:
The system performs preliminary actions by automatically analyzing customer receipts and pre-processing purchase data before the customer even logs in. The receipt analysis system extracts product information, purchase frequency, and spending patterns from uploaded receipts, creating a preliminary customer profile that enables personalized recommendations without requiring the customer to manually create an account or input data.
Solution Approach 2:
The system implements self-service by allowing customers to simply upload their receipts through the mobile application, and the system automatically processes the data to generate personalized shopping lists and product recommendations. The customer does not need to manually categorize items or configure settings - the system autonomously analyzes the receipt data and provides tailored shopping assistance.
2Productivity
If the store implements a comprehensive online shopping system with receipt analysis and personalized recommendations, then customer engagement and sales can be increased, but the system complexity and implementation costs increase
Solution Approach 1:
The system achieves multi-functionality by combining multiple capabilities into a single integrated platform: receipt scanning and image processing, optical character recognition (OCR) for text extraction, automated data analysis for purchase pattern recognition, dynamic shopping list generation, and personalized product recommendation. This universal system handles various customer needs through a unified architecture, reducing overall system complexity compared to implementing separate systems for each function.
Solution Approach 2:
The patent introduces an intermediary component - the receipt analysis system - that acts as a mediator between the customer and the online shopping platform. This intermediary automatically processes raw receipt data, extracts meaningful information, and translates it into structured purchase patterns that the recommendation engine can utilize, thereby simplifying the interaction between customers and the complex recommendation system.
3Loss of information
If the system processes and analyzes customer receipt data to generate personalized purchase suggestions, then valuable purchase patterns can be identified and utilized, but customer privacy and data security concerns may arise
Solution Approach 1:
The system applies local quality by processing and analyzing receipt data locally on the customer's device through the mobile application rather than centrally on server infrastructure. The receipt image processing, OCR extraction, and pattern analysis occur in the local environment, with only anonymized or aggregated results transmitted to the server. This localized processing minimizes exposure of sensitive personal information and reduces data privacy risks.
Data Source
AI summary
A method for providing purchase suggestions to a customer is provided. The method may include a computer server associated with a store receiving multiple receipts for past purchase transactions from a customer. The server may identify products on the receipts and analyze the products and receipt dates to determine a frequency with which the customer purchases a particular product. The server may also determine a date when the customer last purchased the particular product and transmit a purchase suggestion for the product to the customer at a time correlated with the customer needing to purchase the particular product based on a date when the customer last purchased the particular product and the frequency with which the customer purchases the particular product.