ML Authorization Engine for Autonomous IoT Resource Transfers

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

There is a need for a centralized system to facilitate secure and reliable autonomous resource transfers between distributed IoT devices, as existing technologies lack effective authorization mechanisms for ensuring the integrity and security of these transactions.

Innovation Solution

A machine learning-based system that authorizes autonomous resource transfers by processing information from IoT devices, including device profiles, historical claims, and exposure data, using supervised and unsupervised learning algorithms to determine transaction authorization and constraints, and communicates authorization decisions to the devices.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If machine learning algorithms are used to authorize autonomous resource transfers between IoT devices, then security and reliability of transactions are improved, but system complexity increases

Engineering Contradiction:

Improvesecurity and reliability of resource transfersVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent introduces a centralized resource transfer engine as an intermediary system that mediates all resource transfers between distributed IoT devices. This engine contains the complex machine learning authorization logic, isolating the complexity from individual IoT devices while providing centralized security and reliability oversight for all transactions in the network.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system segments the authorization function by separating device-specific information (stored in device profiles) from the centralized authorization logic (in the resource transfer engine). This segmentation allows the complex ML algorithms to operate centrally while individual devices remain relatively simple, maintaining security without requiring complex ML implementation at each device.

Inventive Principle:

Principle #1Segmentation

2Reliability

If centralized resource transfer engine is implemented to facilitate autonomous resource transfers, then transaction authorization and control are improved, but loss of time in processing transactions occurs

Engineering Contradiction:

Improvetransaction authorization and controlVSAvoidtime to process transaction authorization

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by maintaining pre-computed device profiles that contain historical information, security credentials, and behavioral patterns of IoT devices. When a resource transfer request arrives, the centralized engine can quickly retrieve and evaluate these pre-prepared profiles using machine learning algorithms, significantly reducing the time required for authorization decisions compared to analyzing raw device data from scratch.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If machine learning algorithms process device profiles and historical data, then accuracy of authorization decisions is improved, but use of energy for data processing increases

Engineering Contradiction:

Improveaccuracy of authorization decisionsVSAvoidenergy for data processing

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The centralized resource transfer engine acts as an intermediary that consolidates the energy-intensive machine learning processing in a single location rather than distributing it across all IoT devices. Individual devices can remain low-power while the centralized engine performs the computationally expensive authorization analysis, achieving high accuracy without requiring each device to consume significant energy for processing.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11341485B2Machine learning based system for authorization of autonomous resource transfers between distributed IOT components

Publication Date: 2022.05.24 BANK OF AMERICA CORP

AI summary

Systems, computer program products, and methods are described herein for machine learning based system for authorization of autonomous resource transfers between distributed IoT components. The present invention is configured to receive, from a first autonomous IoT device, a transaction authorization request to execute a transaction with a second autonomous IoT device; receive information associated with the first autonomous IoT device, information associated with the second autonomous IoT device, and information associated with the transaction; initiate an execution of one or more machine learning algorithms; determine that the first autonomous IoT device is authorized to execute the transaction with the second autonomous IoT device; transmit a transaction authorization to the first autonomous IoT device to execute the transaction; and receive, from the first autonomous IoT device, an indication that the transaction has been executed.