Blockchain Data Structure Classifiers for Near-Real-Time Anomaly Detection

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Solution Overview

Problem

Conventional technologies fail to efficiently gather, synthesize, and analyze data for anomaly identification and compliance tracking in complex regulatory environments without compromising security, leading to increased administrative burdens and security threats due to human error and data centralization.

Innovation Solution

A machine learning-based system using blockchain-validated documents and deep learning approaches to identify anomalies and trigger remedial actions, employing neural networks and smart contracts to automate compliance reporting and anomaly detection in trusted blockchain networks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional technologies are used for data gathering and analysis, then human error and data centralization increase security risks, but implementing blockchain and deep learning systems increases device complexity

Engineering Contradiction:
ImprovesecurityVSAvoidsystem complexity
Core Design Contradiction:
ReliabilityVSDevice complexity

Solution Approach 1:

The patent introduces a blockchain as an intermediary layer between data sources and analysis systems. The blockchain validates and stores data from multiple sources, providing a trusted foundation that reduces security risks associated with human error and data centralization, while the distributed nature of the system shares computational complexity across multiple nodes rather than concentrating it in a single complex system.

Inventive Principle:
Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual human analysis and centralized data processing with automated deep learning models and neural networks. This substitution eliminates human error in data interpretation and compliance tracking, while the computational complexity is distributed across multiple processing nodes rather than requiring a single complex mechanical system.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If manual compliance tracking is performed, then administrative burden increases, but implementing automated machine learning systems requires significant computational resources and time for training

Engineering Contradiction:
Improvecompliance reporting efficiencyVSAvoidcomputational resources
Core Design Contradiction:
ProductivityVSUse of energy by moving object

Solution Approach 1:

The patent implements pre-trained deep learning models and neural networks that are trained in advance on historical compliance data. These pre-trained models can immediately begin performing compliance tracking and anomaly detection without requiring extensive real-time training, thus reducing the computational resources and time needed during actual compliance operations while maintaining high productivity.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The system employs self-learning mechanisms where the neural networks continuously refine their anomaly detection capabilities by analyzing new data patterns. The system automatically identifies and adapts to evolving compliance requirements and supply chain anomalies without requiring manual retraining or significant ongoing computational intervention, thereby maintaining high productivity with reduced resource consumption.

Inventive Principle:
Principle #25Self-service

3Ease of operation

If centralized data access is used for compliance tracking, then ease of operation improves, but security risks increase due to human error and single point of failure

Engineering Contradiction:
Improvedata access convenienceVSAvoidsecurity threats
Core Design Contradiction:
Ease of operationVSObject-affected harmful factors

Solution Approach 1:

The patent segments the centralized data access system into a distributed blockchain network where data is stored and validated across multiple nodes. This segmentation eliminates the single point of failure and reduces security risks associated with centralized data storage, while the blockchain provides standardized access protocols that maintain ease of operation through consistent query and retrieval mechanisms across the distributed network.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The blockchain acts as an intermediary layer that provides secure, standardized access to data from multiple sources. It maintains ease of operation by providing a uniform interface for data queries while simultaneously enhancing security through distributed validation and cryptographic protection, thereby eliminating the trade-off between operational convenience and security risks.

Inventive Principle:
Principle #24Intermediary (Mediator)

Data Source

PatentUS20250323782A1Neural network classifiers for block chain data structures
Publication Date: 2025.10.16 LEDGERDOMAIN INC
  • US20250323782A1 patent drawing
  • US20250323782A1 patent drawing
  • US20250323782A1 patent drawing

AI summary

Disclosed is a neural network enabled interface server and blockchain interface establishing a blockchain network implementing event detection, tracking and management for rule based compliance, with significant implications for anomaly detection, resolution and safety and compliance reporting.