Automated Inter-Account Transfer System Using ML Selection
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Solution Overview
Problem
Traditional online shopping, banking, and other online resources prioritize data privacy and do not allow interactions between one customer and another, limiting the ability to perform random acts of kindness or facilitate important functionalities such as automated inter-account interactions.
Innovation Solution
A system for automated account interaction that accesses information about asset accounts, receives transaction indications, and automatically selects transactions and accounts based on location, time, quantity, or trained machine learning models, enabling transfers between user accounts without a third-party intermediary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional online shopping and banking systems prioritize data privacy and restrict interactions to customer-merchant only, then data security is improved, but the ability to facilitate inter-customer interactions and random acts of kindness deteriorates
Solution Approach 1:
The patent introduces a payment gateway as an intermediary system that enables indirect interactions between customers while maintaining data privacy. The gateway acts as a mediator that facilitates inter-customer transfers without requiring direct customer-to-customer system access, thus preserving security while enabling versatility.
Solution Approach 2:
The system segments the interaction process into distinct components: customer initiation, gateway verification, and execution. This segmentation allows each component to operate within its own security boundaries while collectively achieving inter-customer interaction capability.
2Productivity
If automated selection of transactions and accounts is implemented using machine learning models, then transaction processing efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models with historical transaction data before actual transaction processing. This preliminary training enables the models to make rapid selections during live operations, improving efficiency without adding real-time complexity.
Solution Approach 2:
The machine learning models perform self-service by autonomously selecting transactions and accounts based on learned patterns, reducing the need for manual configuration and complex rule-based systems while maintaining high processing efficiency.
Data Source
AI summary
A system for automated account interaction has access to information about asset accounts, and receives transaction indications indicating transactions made by those accounts. The system automatically selects a first transaction indication for a first transaction made using a first asset account, for instance based on location, time, asset quantity, randomization, and/or a trained machine learning model. The system automatically selects a second asset account associated with a second user, for instance based on the second user's location, account balance, randomization, and/or a trained machine learning model. The system automatically transfers an asset quantity from the second asset account to the first asset account, and communicates an indicator indicating transfer completion. The asset quantity can be the quantity corresponding to the first transaction, or a portion thereof. The confirmation indicator can be sent to device(s) associated with either or both account(s) and/or can be published to a feed, graph, and/or index.