Biometric Payment Rewards via ML Wellness Profiles
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Solution Overview
Problem
Current systems for monitoring and improving user wellness and implementing insurance policies are limited in their ability to accurately assess and respond to individual health and behavior data, leading to inefficient insurance premium calculations and reward offerings.
Innovation Solution
A payment service network that utilizes biometric data and user behavior information, combined with machine learning models, to update wellness profiles and offer personalized rewards and insurance policy adjustments, thereby improving user wellness and insurance policy management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If biometric data and user behavior information are integrated to determine insurance premiums and rewards, then measurement precision and reliability improve, but device complexity and data processing requirements increase
Solution Approach 1:
The patent introduces a payment service system as an intermediary that collects, processes, and integrates biometric data from wearable devices with user behavior information from payment transactions. This intermediary handles the complex data processing and machine learning model execution, allowing the wearable device itself to remain relatively simple while achieving high measurement precision through the integrated system.
Solution Approach 2:
The payment service system serves multiple functions: it processes payments, collects biometric data, analyzes user behavior, runs machine learning models to determine wellness scores, and manages insurance premium calculations. By consolidating these diverse functions into a single multi-functional system, the patent reduces overall system complexity while improving measurement precision through integrated data analysis.
2Productivity
If real-time biometric data processing and machine learning model execution are implemented, then productivity and responsiveness improve, but use of energy and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing biometric data and user behavior information as they are collected, and by pre-training machine learning models offline. This allows the system to make quick wellness assessments and insurance premium calculations in real-time without requiring intensive computational resources during actual transactions, thus improving productivity while managing energy consumption.
Solution Approach 2:
The patent implements partial processing by selectively analyzing only the most relevant biometric data and behavior patterns needed for wellness assessment, rather than processing all available data. The machine learning models focus on key predictors of wellness outcomes, performing sufficient analysis to achieve accurate results without excessive computational energy consumption.
3Adaptability or versatility
If comprehensive user data collection and analysis are performed to personalize rewards and insurance policies, then adaptability and measurement precision improve, but loss of information privacy and security risks increase
Solution Approach 1:
The patent applies local quality by processing and analyzing user data in a distributed manner, where sensitive biometric information remains primarily on the user's wearable device or in encrypted form, while only processed results (wellness scores, risk assessments) are shared with insurance providers. This allows personalized rewards and policies to be generated through localized data processing that minimizes privacy exposure.
Solution Approach 2:
The payment service system acts as a trusted intermediary that handles data collection, processing, and sharing between users, wearable devices, and insurance providers. This intermediary implements security protocols, data encryption, and controlled access mechanisms that protect user privacy while enabling comprehensive data analysis for personalized insurance policies and rewards.
Data Source
AI summary
A method comprising receiving, by a payment service system (PSS), a first biometric dataset produced by a wearable device of a user having a user account with the PSS associated with a respective wellness profile; training a machine learning model that uses as input user behavior and outputs a reward offer associated with behavior that improves a wellness score; determining, using the model, the reward offer for the user with a merchant; presenting the reward offer, wherein activation of the reward offer causes an association with the user account; receiving, from the mobile device, an indication that the user has activated the reward offer via a transaction with the merchant; obtaining a second biometric dataset at a time that is based on activation of the reward offer; and updating the user's wellness profile based on a comparison of the two biometric datasets.