AI Transfer Classification and IoT Triggers for Flexible Savings
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Solution Overview
Problem
Consumers face challenges in consistently contributing to financial goals and managing utility consumption due to changing availability of funds and expenses, necessitating improved systems for automated and flexible account management and transaction processing.
Innovation Solution
An AI architecture utilizing a support vector machine (SVM) engine processes transactions between accounts based on historical data, classifies fund transfer requests, and adjusts utility consumption through an IoT module to facilitate automated and flexible savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual fund transfer initiation is used, then user control over transactions is maintained, but consistency in contributing to financial goals deteriorates due to manual intervention requirements
Solution Approach 1:
The system enables self-service automation by using AI classification to automatically determine destination accounts for fund transfers based on memo field terms. The transaction processing platform autonomously routes funds without requiring manual selection of destination accounts, though users can still review and approve transfers through notifications sent to their computing devices.
Solution Approach 2:
The patent replaces manual mechanical processing of fund transfers with an automated AI-based classification system. The support vector machine (SVM) engine substitutes for manual decision-making by classifying transfer requests and determining destination accounts automatically, thereby eliminating the need for consistent manual intervention while maintaining user control through approval mechanisms.
2Adaptability or versatility
If flexible modification of contributions is allowed, then adaptability to changing financial goals improves, but difficulty in consistently contributing deteriorates without automated triggers
Solution Approach 1:
The system implements dynamics by allowing flexible modification of contribution parameters while maintaining consistent execution through automated triggers. Users can modify contribution amounts, timing, and destination accounts, and the system adapts its automated routing accordingly. The AI classification system dynamically adjusts to new transfer patterns while the trigger mechanism ensures consistent execution of modified contribution strategies.
Solution Approach 2:
The patent incorporates feedback mechanisms through notifications sent to user computing devices before fund transfers are executed. Users receive feedback about proposed transfers and can approve or modify them, ensuring that flexible contribution modifications are consistently applied according to user intentions. This feedback loop maintains both adaptability and consistency by aligning automated actions with user goals.
3Ease of operation
If automated fund transfers are implemented, then ease of operation improves, but measurement precision of transfer classification deteriorates without AI models
Solution Approach 1:
The patent replaces manual classification of fund transfer requests with an AI-based support vector machine (SVM) engine. This substitution dramatically improves ease of operation by automatically determining destination accounts based on memo field terms, while the SVM model provides high measurement precision through its trained classification capabilities. The system accurately distinguishes between different transfer types and routes funds to appropriate accounts with high accuracy.
4Productivity
If multiple account transfers are processed, then productivity of fund management improves, but device complexity increases due to classification requirements
Solution Approach 1:
The patent applies segmentation by dividing the transaction processing system into distinct functional modules: the support vector machine (SVM) engine for classification, the trigger engine for automation, and the notification system for user interaction. This segmentation allows the system to process multiple account transfers efficiently by handling classification and routing decisions in dedicated modules, thereby improving productivity while managing complexity through modular architecture.
Data Source
AI summary
Aspects of the disclosure relate to artificial intelligence (AI)-based processing of account transfers between different accounts associated with a client. In particular, various aspects of this disclosure relate to triggering transfers based on data associated with electronic transfers (e.g., between accounts associated with different users) and/or information associated with card-based transactions. Additional aspects of the disclosure relate to using internet of things (IOT) modules to determine predicted consumption associated with utilities, and at least triggering account transfers based on the predicted consumption.