Adaptive AI Engine for Distributed Ledger Node Orchestration
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Solution Overview
Problem
Enterprise organizations face challenges in dynamically determining which distributed ledger to use for specific information, ensuring secure addition of nodes, and managing changes in node status over time, which can lead to security issues and data corruption.
Innovation Solution
A computing platform with an adaptive AI engine that uses historical ledger and node confidence information to determine the appropriate distributed ledger for a given node, generating a node addition score to assess the node's trustworthiness and suitability for addition, and dynamically updating the AI engine through feedback loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static monitoring and addition of information from nodes to ledgers is used, then the system is simple to operate, but the system cannot adapt to changing node status and may allow malicious nodes to corrupt ledgers
Solution Approach 1:
The patent implements dynamic node evaluation by continuously monitoring node behavior and updating trust scores in real-time. The system transitions from static ledger assignments to dynamic assignments where nodes can be moved between ledgers based on their current trustworthiness. This allows the system to adapt to changing node status while maintaining security through automated reevaluation and reassignment mechanisms.
Solution Approach 2:
The patent employs feedback loops where node actions are continuously monitored, evaluated, and used to update trust scores. The system collects feedback from node interactions, validates information against trust criteria, and uses this feedback to dynamically adjust node assignments. This feedback mechanism enables the system to respond to changing node status while maintaining operational simplicity through automation.
2Reliability
If manual verification of each node is performed, then security is improved, but the processing time and operational complexity increase significantly
Solution Approach 1:
The patent implements self-service verification where nodes automatically provide information about their behavior and history, and the system automatically evaluates this information against predefined criteria. The trust evaluation system operates autonomously, collecting data from node interactions, calculating trust scores, and making verification decisions without human intervention. This maintains high security through comprehensive verification while dramatically reducing processing time through automation.
Solution Approach 2:
The patent replaces manual verification processes with automated computational systems. Instead of human operators manually checking node credentials and behavior, the system uses algorithms to automatically evaluate node trustworthiness based on behavioral data, historical performance, and interaction patterns. This substitution maintains verification reliability while eliminating the time loss associated with manual processes.
3Reliability
If information is added to ledgers without dynamic evaluation, then the process is fast and simple, but malicious or corrupted nodes can compromise ledger integrity
Solution Approach 1:
The patent implements preliminary evaluation of nodes before they are added to ledgers. The system pre-assesses node trustworthiness by analyzing historical behavior, validation records, and interaction patterns before granting ledger access. This preliminary action ensures that only trustworthy nodes are added, maintaining ledger integrity while streamlining the addition process through automated pre-verification rather than post-addition validation.
Solution Approach 2:
The patent introduces an intermediary trust evaluation system that sits between node requests and ledger addition. This intermediary layer automatically assesses node credibility, validates information sources, and determines appropriate ledger assignments based on trust scores. The intermediary maintains ledger integrity by filtering out malicious nodes while preserving productivity through automated decision-making that eliminates manual review bottlenecks.
Data Source
AI summary
A computing platform may train, using historical ledger information and historical node confidence information, an adaptive AI engine to output, for a given input node, a particular distributed ledger to which the given input node should be added. The computing platform may receive a request to add a first node to a ledger. The computing platform may input first node information into the adaptive AI engine, which may cause the adaptive AI engine to output a node addition score indicating a first distributed ledger corresponding to an information type of the request. Based on identifying that the node addition score meets or exceeds a node addition threshold, the computing platform may cause the first node to be added to the first distributed ledger. The computing platform may update, using a dynamic feedback loop and based on the node addition score, the adaptive AI engine.