Semantic Virtual Account Selection at the Point of Sale
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Solution Overview
Problem
Current payment processor systems lack efficient methods for selecting the appropriate virtual payment account during transactions, especially in dynamic environments where user preferences and item identities need to be semantically matched in real-time.
Innovation Solution
A payment processor system with a memory that stores user and virtual payment account data, using semantic matching to assign a virtual account based on inferred item and user payment identities, and optionally applying goodwill adjustments based on sensor inputs and profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional payment account selection methods are used, then the system is simple to operate, but the system cannot accurately match virtual accounts with user preferences and item identities in real-time
Solution Approach 1:
The patent introduces semantic matching algorithms as an intermediary layer between the payment account selection system and the transaction data. This intermediary processes item identities, user preferences, and contextual information to generate semantically matched account recommendations, thereby improving selection accuracy without directly complicating the core payment processing system.
Solution Approach 2:
The system performs preliminary semantic analysis and account matching before the actual payment transaction occurs. Virtual payment accounts are pre-configured with semantic tags and user preferences are pre-processed, enabling rapid and accurate account selection at the point of sale without adding complexity to the transaction execution phase.
2Productivity
If semantic matching is implemented for real-time account selection, then transaction efficiency is improved, but computational resources and processing time are increased
Solution Approach 1:
Semantic profiles, item tags, and account characteristics are pre-processed and stored in optimized data structures before transactions occur. This preliminary preparation enables the system to perform rapid semantic matching during transactions using pre-computed similarity metrics, thereby improving transaction efficiency while minimizing real-time computational resource consumption.
Solution Approach 2:
The system implements incremental semantic matching that processes only the most relevant features and dimensions for each specific transaction type. Rather than performing exhaustive semantic analysis on all possible account attributes, the system selectively evaluates the most impactful semantic dimensions, reducing computational overhead while maintaining matching accuracy.
Data Source
AI summary
A payment processor system includes a point-of-sale device having at least one first sensor, a processor, a memory storing a plurality of users and a plurality of virtual payment accounts, wherein the processor is configured to apply semantic drift to select a virtual payment account in rapport with an item semantic identity in a payment transaction initiated at the point of sale.