Smart Transaction Card ML Price Identification
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Solution Overview
Problem
In-person shopping makes it difficult for customers to compare prices across different merchants efficiently, leading to wasted resources in identifying optimal prices and rebates.
Innovation Solution
A method utilizing machine learning and a smart transaction card to automatically identify optimal prices and rebates by processing item data, price data, and other data received from price tags and merchant databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customers manually compare prices in-person at different merchants, then they can identify optimal prices, but it wastes significant time and computing resources
Solution Approach 1:
The system enables self-service price comparison by having the machine learning model automatically analyze prices across multiple merchants and generate rebate recommendations without requiring manual customer intervention. The transaction card system autonomously captures item data, compares prices, and identifies optimal deals.
Solution Approach 2:
The patent replaces manual mechanical price comparison processes with an automated machine learning-based electronic system. The machine learning model processes item data, price data, and other data to automatically determine optimal prices and rebates, substituting human manual comparison with computational analysis.
2Loss of time
If automated price comparison systems are implemented, then time for price comparison is reduced, but device complexity increases
Solution Approach 1:
The system is segmented into distinct functional components: the transaction card for capturing item data, the machine learning model for processing and analysis, the database for storing price and product information, and the communication interfaces for data exchange. This modular segmentation reduces overall system complexity by allowing each component to be developed and optimized independently.
Solution Approach 2:
The machine learning model acts as an intermediary between the transaction card and the database, automatically processing raw data into actionable rebate recommendations. This intermediary layer simplifies the interaction between different system components and abstracts complexity from the user interface.
3Measurement precision
If machine learning models process large amounts of price data, then optimal price identification improves, but computing resources are consumed
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing price data, product information, and rebate rules in the database before actual price comparison is needed. The machine learning model uses this pre-organized data to efficiently identify optimal prices during transactions without requiring real-time processing of all historical data.
Solution Approach 2:
The machine learning model applies local quality analysis by focusing computational resources on specific items and price points relevant to each transaction rather than processing all data uniformly. The model prioritizes analysis based on the specific product category, price differential, and customer profile, optimizing computing resource allocation.
Data Source
AI summary
A device may receive, from a client device of a customer, item data identifying a price of an item and customer data identifying the customer, where the item data may be received by a transaction card from a price tag of the item. The device may receive price data identifying prices associated with multiple items and other data identifying locations, availabilities, and terms of the multiple items, and may process the item data, the price data, and the other data, with a machine learning model, to identify an optimal price for the item. The device may provide, to the client device, data identifying the optimal price and data identifying a merchant associated with the optimal price, and may receive transaction data identifying the item, the optimal price, and the merchant when the customer purchases the item. The device may perform actions based on the transaction data.