Cognitive Rewards Recognition for Mobile Wallet Payment Optimization
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Solution Overview
Problem
Conventional mobile wallet payment transaction systems fail to effectively organize and maximize reward data for users, leading to suboptimal payment option choices during transactions.
Innovation Solution
A cognitive rewards recognition method and system that determines and ranks payment options based on cognitive factors such as location, weather, purchase type, and reward balances, presenting a ranked list to users to maximize benefits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional mobile wallet systems present all payment options to users, then users have complete information, but users cannot efficiently identify optimal reward options
Solution Approach 1:
The patent introduces a cognitive rewards recognition system as an intermediary between the mobile wallet system and the user. This system analyzes multiple payment options, evaluates their associated rewards based on user profile and cognitive factors, and presents a ranked list of recommendations. The intermediary processes and filters reward information, transforming raw data into actionable insights without losing important details.
Solution Approach 2:
The system changes the parameter of information presentation by transforming a static list of all payment options into a dynamic ranked list based on calculated reward values. The cognitive rewards recognition system evaluates payment options using multiple parameters (user profile, purchase context, cognitive factors) and reorders them to highlight optimal choices, making the best options immediately visible while preserving access to all alternatives.
2Productivity
If cognitive rewards recognition system analyzes multiple factors for each payment option, then reward maximization is achieved, but system complexity increases
Solution Approach 1:
The patent segments the complex cognitive analysis into distinct functional modules: user profile analysis, purchase context evaluation, cognitive factor assessment, and reward calculation. Each module handles a specific aspect of the analysis independently, processing relevant data and passing results to the next stage. This modular segmentation manages system complexity by breaking down the overall task into manageable, specialized components.
Solution Approach 2:
The cognitive rewards recognition system operates autonomously, automatically analyzing payment options and generating recommendations without requiring user intervention. The system self-manages the complex multi-factor analysis by internally accessing user profiles, evaluating purchase contexts, and calculating optimal choices, thereby handling the computational complexity internally while presenting simple, actionable outcomes to users.
3Measurement precision
If the system processes and ranks all payment options in real-time, then optimal rewards are identified, but processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing user profile data, payment option characteristics, and cognitive factor weights before actual transactions occur. The system maintains updated user profiles and pre-evaluates available payment options and their reward structures in advance. When a purchase opportunity arises, the system performs a faster ranked list generation by building upon this pre-computed foundation, reducing real-time processing requirements while maintaining evaluation accuracy.
Data Source
AI summary
The present invention provides a method and system for cognitive rewards recognition in a mobile wallet of a user. A cognitive rewards recognition software application installed in the mobile wallet for each purchase opportunity: determines payment options and associated rewards; determines cognitive factors linking and relating the user, the payment options, and the rewards to purchases; and generates a ranked list of the payment options in response to the cognitive factors, wherein the rewards are distributed between purchases to maximize benefits to the user in response to the related cognitive factors. The ranked list is presented for each purchase opportunity to the user on his mobile wallet.