Multi-Device Transaction Coordination With Automatic Triggers

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Solution Overview

Problem

Current devices require significant user involvement in transaction processes, such as swiping, hovering, or entering payment information, which is time-consuming and distracts from other tasks, and are limited in data processing capabilities for automated transactions.

Innovation Solution

A multi-device network, like an IoT network, uses machine-learning processes to create a knowledge base for transaction-related information, enabling devices to initiate transactions based on pattern recognition and behavior analysis without user input, and select payment devices based on contextual attributes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If users manually initiate transactions by swiping, hovering, scanning or entering payment information, then transaction security can be maintained, but user time consumption increases and productivity decreases

Engineering Contradiction:

Improvetransaction securityVSAvoiduser time consumption

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables devices to automatically initiate and process transactions without requiring manual user intervention. The transaction processing platform autonomously analyzes device data, identifies transaction needs, selects appropriate payment methods, and executes transactions based on pre-configured parameters and machine learning patterns, allowing the system to serve itself rather than requiring continuous user input

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system pre-configures transaction parameters, payment methods, and security protocols before transactions occur. Device data is pre-loaded with historical transaction information, and the transaction processing platform pre-establishes patterns and triggers based on machine learning algorithms, enabling automatic transaction initiation without requiring users to manually configure settings each time

Inventive Principle:

Principle #10Preliminary action

2Extent of automation

If devices store and process transaction data to enable automated transactions, then transaction automation capability improves, but device complexity increases

Engineering Contradiction:

Improvetransaction automation capabilityVSAvoiddata processing complexity

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The system divides transaction processing into separate functional modules: data collection, data storage, machine learning analysis, transaction initiation, and execution. The transaction processing platform acts as a centralized coordinator that receives data from multiple devices, processes it through specialized algorithms, and triggers appropriate transactions, separating complexity from individual devices while enabling automation across the network

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The transaction processing platform serves as an intermediary between devices and the transaction execution system. It receives raw device data, applies machine learning patterns, selects appropriate transactions, and coordinates execution with payment processors and merchants, abstracting the complexity of automated decision-making from individual devices while enabling sophisticated automation

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If devices monitor various attributes to determine when to initiate transactions, then transaction timing accuracy improves, but ease of operation decreases

Engineering Contradiction:

Improvetransaction timing accuracyVSAvoiduser monitoring requirement

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system continuously monitors device attributes and transaction patterns, using machine learning algorithms to analyze historical data and identify optimal transaction timing. The transaction processing platform receives feedback from device status updates, compares it against learned patterns, and automatically triggers transactions at the most appropriate moments without requiring users to manually monitor or interpret device attributes

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system replaces manual user monitoring and decision-making with automated machine learning patterns recognition. Instead of users actively watching device attributes and manually triggering transactions, the system uses algorithms to detect patterns in historical data and automatically initiates transactions based on predicted optimal timing, substituting mechanical user action with intelligent automation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12437304B2Intelligent coordination of transaction processing in a multi-device network

Publication Date: 2025.10.07 BANK OF AMERICA CORP

AI summary

Rules and triggers for initiating and generating transactions can be dynamically and automatically created by a computing device based on learning behaviors for a knowledgebase. This knowledgebase may be created by storing historical transaction information for devices within a particular network such as an Internet of Things (IoT) network where devices are interconnected and capable of processing data and instructions. Such a network may be associated with a user or location or organization, and may conform to a specific communication protocol.