Real-time Enterprise System Mapping via Neural Network Tokens
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Solution Overview
Problem
Conventional methods for tracking and reconciling system components in enterprise environments are inefficient due to lack of real-time awareness, leading to inefficiencies and security risks from duplicated systems and uneven resource allocation.
Innovation Solution
A system that uses a token processor and network agent to extract metadata from system components, generate custom tokens, and process them through neural networks to create a real-time, searchable visual representation of system relationships and data usage, enabling continuous monitoring and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual periodic overview and reconciliation methods are used to track system components, then device complexity is reduced, but real-time system awareness is lost leading to inefficiencies and security risks
Solution Approach 1:
The system automatically monitors itself by deploying network agents to SORs that extract metadata and generate custom tokens, eliminating the need for manual periodic reviews while maintaining real-time awareness of system components and their relationships
Solution Approach 2:
The patent replaces manual mechanical monitoring processes with an automated digital system using network agents, token processors, and neural networks to continuously track system components, data flows, and relationships in real-time
2Adaptability or versatility
If uniform application configurations are applied to all SORs, then device complexity is reduced, but adaptability to specific functional requirements is lost
Solution Approach 1:
The system extracts specific metadata from each SOR and generates custom tokens tailored to that SOR's unique characteristics and functional requirements, allowing each system component to be configured and monitored according to its specific needs rather than using uniform configurations
Solution Approach 2:
The patent dynamically adjusts configuration parameters by extracting metadata from each SOR and generating customized tokens that modify monitoring and management parameters based on the specific functionality and characteristics of each system component
3Adaptability or versatility
If cloud computing capabilities are utilized to spin up new environments, then system flexibility is improved, but duplication of existing SOR functions increases
Solution Approach 1:
The system continuously monitors network traffic and extracts metadata about SORs, generating custom tokens that are processed by neural networks to identify duplicate functions and provide feedback about system redundancy, enabling detection of duplicated SOR functions created through cloud deployment
Solution Approach 2:
The patent introduces network agents as intermediaries deployed to each SOR that extract metadata and generate custom tokens, serving as a mediator between the cloud deployment process and the central monitoring system to enable identification of duplicate functions
4Loss of time
If manual reconciliation of system data is performed, then loss of information is reduced through periodic checks, but loss of time increases due to non-real-time awareness
Solution Approach 1:
The system continuously monitors network traffic and extracts metadata from SORs in real-time, maintaining continuous awareness of system components and their relationships without interruption, eliminating the time loss associated with periodic manual reconciliation
Data Source
AI summary
Systems, methods, and apparatus are provided for system mapping based on processing of custom network packets. A token processor may deploy a network agent to a system of record (SOR). The network agent may scan the SOR and extract SOR metadata through a set of API calls. The network agent may aggregate the SOR data and generate a token according to parameters established by the token processor. The token processor may process the token through a neural network to generate a set of output vectors for the SOR. A system mapping engine may determine relationships between network SORs based on the output vectors and generate an integrated visual representation of enterprise systems. The output vectors may be adjusted based on new tokens received from the SOR and the visual representation may be updated in real time.