Semantic Payment Account Selection for Reward-Matched Transactions
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Solution Overview
Problem
Payment transaction systems face challenges in automatically optimizing transactions by determining the most favorable circumstances for users, as existing systems lack efficient methods to select the appropriate payment account based on item semantics and user preferences.
Innovation Solution
A payment processor system that uses semantic analysis to infer item semantic identities and user payment semantic identities, assigning the most suitable payment account for transactions by matching these identities and considering rewards associated with the accounts, with the option to integrate sensors for additional inputs such as gestures or environmental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If semantic analysis is implemented to automatically optimize payment transactions, then transaction optimization and user experience are improved, but system complexity increases
Solution Approach 1:
The patent introduces a semantic analysis engine as an intermediary component that bridges the gap between simple transaction processing and complex optimization decisions. This engine analyzes item semantics, user preferences, and account characteristics to automatically select optimal payment accounts, thereby achieving high-level automation without requiring the entire system to become equally complex.
Solution Approach 2:
The system enables self-service by allowing the semantic analysis engine to autonomously make payment account selection decisions based on predefined semantic rules and user profiles. The engine independently processes transaction data, compares account options, and executes selections without requiring manual user intervention or complex external coordination for each transaction.
2Adaptability or versatility
If multiple payment accounts are managed with semantic matching, then payment optimization capability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing semantic characteristics of payment accounts and user preferences in structured formats before transactions occur. User profiles, account attributes, and item semantic categories are prepared in advance, enabling rapid matching during actual transactions without requiring complex real-time analysis of all account details.
Solution Approach 2:
The semantic matching process applies local quality by focusing analysis only on relevant semantic attributes specific to each transaction context. Rather than comparing all possible account features universally, the system identifies and evaluates only the locally relevant semantic characteristics (e.g., reward categories, spending patterns) that matter for the specific items being purchased, thereby reducing processing overhead.
3Ease of operation
If sensor integration is added for gesture and environmental detection, then user interaction capability is improved, but device complexity and power consumption increase
Solution Approach 1:
The sensor integration operates on periodic action principles by activating sensors only at specific moments when payment initiation is detected or expected, rather than continuously monitoring. The system uses periodic scanning or event-triggered activation where sensors remain dormant until a gesture or environmental cue suggests a payment interaction is beginning, thereby reducing overall power consumption while maintaining ease of operation.
Data Source
AI summary
A payment processor system includes a memory storing user payment account data and associations between item semantic identities and corresponding items for purchase. A point of sale device includes a wireless transceiver, a processor and a computer program operable to detect a first payment transaction and assign a first account to service the first payment transaction. The first account is assigned as a function of a semantic matching between one or more inferred item semantic identities and a user payment semantic identity associated with the first account, and further based on a reward provided by the first account, the reward being inferred as being applicable to the user payment semantic identity and the item semantic identities.