Electronic Cash Register Receipt Linking for Personalized Discounts
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Solution Overview
Problem
Existing loyalty programs are limited in scope, requiring customers to accumulate multiple loyalty cards and are not feasible for small merchants lacking technological infrastructure, and there is no effective method for small merchants to determine optimal discounts based on individual customer behavior without complex machine learning systems.
Innovation Solution
A method using a computer-implemented system that determines an individual discount amount for customers by linking customer IDs to transaction data through a remote server, employing machine learning to calibrate a sigmoid response curve for each customer, considering purchasing power and susceptibility to incentives, and integrating with existing electronic cash registers to collect receipt data without modifying them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine learning systems are used to determine optimal discounts, then individualized discount accuracy is improved, but device complexity and computational resources required increase
Solution Approach 1:
The patent extracts the machine learning component from the ECR device and relocates it to a remote server. The ECR only performs simple data collection and transmission functions, while the complex ML algorithms run remotely. This extraction resolves the contradiction by maintaining high measurement precision through sophisticated ML while reducing device complexity at the ECR level.
Solution Approach 2:
The patent introduces a remote server as an intermediary between the ECR and the discount computation. This intermediary handles the complex computational tasks externally, allowing the ECR to remain simple while still benefiting from accurate, individualized discount determination through the server's ML capabilities.
2Adaptability or versatility
If loyalty programs are expanded to work with multiple payment methods, then adaptability is improved, but device complexity and infrastructure requirements increase
Solution Approach 1:
The patent implements a universal loyalty program architecture where the remote server can process transactions from multiple payment methods (credit cards, cash, smartphone payments) through a single integrated system. The ECR simply captures transaction data regardless of payment method, and the server's universal processing capability handles the diversity, improving adaptability without increasing ECR complexity.
Solution Approach 2:
The remote server acts as an intermediary that provides unified processing for various payment methods. Instead of the ECR needing complex infrastructure to handle each payment type, the server's intermediary role consolidates the complexity remotely, allowing the ECR to maintain simplicity while achieving broad payment method coverage.
3Productivity
If discounts are increased to motivate customers, then sales volume is improved, but profit margins deteriorate
Solution Approach 1:
The patent dynamically changes the discount parameter based on individual customer characteristics and transaction context. Rather than applying uniform high discounts, the system adjusts discount levels (parameter changes) to optimize the balance between motivating customers and preserving margins. The remote server calculates personalized discount values that maximize sales impact while minimizing margin erosion.
Solution Approach 2:
The patent applies local quality by tailoring discount levels to individual customers and specific transactions rather than applying blanket discounts. Each customer receives customized discount parameters based on their purchasing behavior and preferences, allowing the system to motivate sales effectively while preserving overall profit margins through targeted rather than universal discounting.
Data Source
AI summary
A computer-readable medium storing a program including instructions that, when executed by an electronic cash register, may perform a method of rewarding a customer who is making a purchase at the electronic cash register at a point of sale of a merchant. The method may include computer implemented customer method for rewarding a customer making a purchase at an electronic cash register at a point of sale of a merchant, comprising the steps of:the customer presents his Customer ID in the form of a QR code or electronic identification device;a first module in the electronic cash register reads the Customer ID and send it to a remote server;the electronic cash register authorizes a payment from a customer (20);the electronic cash register generates a receipt for the payment;an electronic cash register utility module executed in the electronic cash register extracts receipt data and send it to a remote server;the remote server links the Customer ID with the receipt data and registers the linked data as a new transaction;an analytical discount computation module determines an individual discount on future transaction, using an analytical response to discount amount curve,wherein said response to discount amount curve is adapted to each customer using customer specific parameters retrieved by a machine learning module trained with previous transactions of said customer with said merchant and with other merchants.