Receipt Data Reminder Scheduling via POS Intermediary
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Solution Overview
Problem
Current point-of-sale systems are limited in their ability to collect and analyze electronic receipt data, hindering their capacity to implement advanced services and marketing opportunities.
Innovation Solution
A system and method that utilizes electronic receipt data to generate and schedule reminders for customers based on products with predictable purchase periodicity, integrating with POS systems to provide personalized reminders through a computer program product, enabling the use of modules like receipt and reminder modules for data analysis and notification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If POS systems collect and analyze electronic receipt data, then customer engagement and personalized services are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent introduces a cloud-based processing system as an intermediary between the POS system and the customer. The POS system simply collects receipt data and transmits it to the cloud, where complex analysis is performed. This mediator handles the computational burden externally, allowing the POS system to remain relatively simple while still providing advanced customer engagement capabilities through personalized reminders and recommendations.
Solution Approach 2:
The patent creates a universal service layer that handles multiple functions: data collection from various receipt sources, data cleaning and normalization, purchase periodicity analysis, reminder generation, and multiple delivery channels. This multi-functional system can serve different retail environments and product types without requiring custom complex processing for each scenario, thereby improving adaptability while managing complexity through standardization.
2Measurement precision
If the system analyzes receipt data to determine purchase periodicity, then reminder accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining common purchase periodicities (e.g., monthly, quarterly, annually) and pre-establishing algorithms to detect these patterns. When receipt data is received, the system compares against these pre-established patterns rather than performing complex real-time analysis, significantly reducing processing time while maintaining high accuracy in determining when reminders should be sent.
Solution Approach 2:
The system implements feedback mechanisms where customer responses to reminders and actual repurchase behavior are fed back into the analysis. This feedback allows the system to refine its periodicity detection over time, improving accuracy without requiring increasingly complex processing. The feedback loop enables continuous optimization of the detection algorithms using real-world data, maintaining precision while managing computational resources efficiently.
3Adaptability or versatility
If the system integrates with both legacy and modern POS systems, then adaptability is improved, but integration complexity and compatibility requirements increase
Solution Approach 1:
The patent employs a standardized intermediary interface layer that acts as a universal translator between diverse POS systems and the cloud processing system. This intermediary handles various data formats, communication protocols, and system architectures (legacy mainframes, modern cloud-based POS, mobile POS) by normalizing all inputs into a unified data structure before analysis. This mediator approach enables broad compatibility across different POS generations without requiring the core analysis system to become increasingly complex.
Solution Approach 2:
The system manages integration complexity by focusing on parameter standardization rather than system-specific customization. By defining standard parameters for receipt data (transaction date, items, amounts, customer information) and transforming all incoming data into these standardized parameters, the system can adapt to various POS architectures without implementing complex compatibility layers for each system type. This parameter-based approach simplifies integration while maintaining versatility.
Data Source
AI summary
A computer-implemented method is disclosed for calendaring reminders. Within the method, one or more point-of-sale transactions may be conducted. The transactions may involve a computer system and a customer possessing a mobile computing device. The computer system may store receipt data documenting the transactions. The computer system may also search the receipt data in an effort to locate one or more products having predictable purchase periodicity. When such products are identified, one or more appropriate reminders for subsequent purchases of the one or more products may be scheduled.