Dynamic Spending Limit Visualization via Augmented Reality
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Solution Overview
Problem
Cardholders with payment cards lacking preset spending limits face difficulties in determining their dynamic purchasing power, as it varies based on credit history and transaction data, leading to uncertain spending capabilities.
Innovation Solution
A system utilizing a machine learning model to infer a dynamic spending limit based on credit card and credit report data, computing purchasing power, and visualizing it through augmented reality on the physical credit card, providing real-time updates and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a payment card has no preset spending limit to provide flexibility, then adaptability is improved, but the cardholder's ability to determine purchasing power deteriorates
Solution Approach 1:
The patent introduces an intermediary system comprising a machine learning model and augmented reality display that mediates between the cardholder and the dynamic spending limit. The ML model processes credit data to infer the dynamic limit, while the AR display visualizes this information overlaying the physical card, thus resolving the information visibility problem without restricting spending flexibility
Solution Approach 2:
The patent replaces traditional mechanical information display methods (physical card details, paper statements) with an augmented reality digital overlay that provides real-time, context-aware purchasing power information. This substitution enables dynamic information presentation that adapts to the cardholder's needs while maintaining the flexibility of no preset limits
2Measurement precision
If a machine learning model is used to infer dynamic spending limit, then measurement precision of purchasing power is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal machine learning model that performs multiple functions: inferring dynamic spending limits, classifying purchasing power levels, and providing recommendations. This multi-functional approach consolidates complexity into a single system rather than requiring separate mechanisms for each function, making the increased complexity more manageable and justifiable
Solution Approach 2:
The machine learning model operates autonomously, continuously analyzing credit data and transaction patterns to self-adjust the dynamic spending limit without requiring manual intervention from the cardholder or complex administrative systems. This self-service capability reduces operational complexity while maintaining high measurement precision
3Ease of operation
If augmented reality visualization is implemented on the physical card, then ease of operation is improved, but loss of time for data processing increases
Solution Approach 1:
The system performs preliminary computations by continuously analyzing credit data and training the machine learning model in advance, so that when the cardholder needs purchasing power information, the dynamic spending limit is already inferred and ready for immediate AR visualization. This eliminates the need for real-time computation during transactions
Solution Approach 2:
The machine learning model operates continuously in the background, continuously updating the dynamic spending limit based on new credit data and transactions. This continuous operation ensures that the AR visualization always displays current, accurate information without requiring intermittent batch processing, thus reducing perceived processing time for the user
Data Source
AI summary
Disclosed embodiments pertain to determining and communicating purchasing power through a visualization. A trained machine learning model can be invoked on a credit card without a preset limit to predict a dynamic spending limit based on credit card data or credit score data. Purchasing power can be computed as the difference between the dynamic spending limit and a current balance, and a purchase power class can be determined based on the purchasing power. Further, a graphic representation of a purchasing power class can be determined. Subsequently, presentation of the graphic representation can be triggered in a manner that overlays the graphic representation on or around the physical card.