Distributed Node Resource Allocation Using Real-Time IPI Models

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional systems struggle to efficiently allocate resources for data processing in distributed networks, particularly during peak hours and emergency situations, leading to downtime and high resource consumption without real-time solutions.

Innovation Solution

An instantaneous process identifier (IPI) using deep learning neural networks analyzes real-time data parameters and system configurations to implement instance-based strategies, optimizing resource allocation and maintaining operational resiliency through automated, real-time adjustments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional resource allocation methods are used in distributed networks, then system simplicity is maintained, but resource consumption increases and real-time responsiveness deteriorates during peak hours

Engineering Contradiction:

Improvereal-time resource allocation efficiencyVSAvoidcomputing resource consumption

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system continuously monitors real-time data parameters from distributed nodes and feeds this information back to the IPI model, which dynamically adjusts resource allocation decisions based on current system state, enabling responsive optimization without manual intervention

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The IPI model autonomously analyzes metadata and determines optimal resource allocation without requiring external control systems or manual configuration, allowing the distributed network to self-optimize resource distribution in real-time based on observed patterns

Inventive Principle:

Principle #25Self-service

2Measurement precision

If deep learning neural networks are deployed for real-time analysis, then resource allocation accuracy improves, but system complexity increases

Engineering Contradiction:

Improveresource allocation accuracyVSAvoidsystem architecture complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The IPI model serves as an intermediary layer between raw metadata collection and resource allocation decisions, transforming complex neural network processing into simplified instance-based strategies that can be efficiently implemented across distributed nodes without requiring each node to run complex models

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system transforms metadata into vector representations (copies of the original data in a different format) that are optimized for neural network processing, enabling accurate analysis while keeping the original data structures intact for other purposes

Inventive Principle:

Principle #26Copying

3Reliability

If instance-based strategies are implemented dynamically, then operational resiliency improves during emergencies, but processing time increases

Engineering Contradiction:

Improveoperational resiliencyVSAvoidprocessing latency

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system pre-processes metadata into vector representations and maintains trained IPI models ready for deployment, so when emergencies occur or peak loads are detected, resource allocation decisions can be made rapidly using pre-prepared data structures and models rather than processing raw data from scratch

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adapts its processing depth and model complexity based on real-time conditions, using simpler instance-based strategies for routine operations and more complex analysis only when necessary, thereby maintaining fast response times while ensuring reliability when needed

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20260023622A1Systems and methods for process optimization using advanced computational models for data analysis and automated processing

Publication Date: 2026.01.22 BANK OF AMERICA CORP

AI summary

Systems, computer program products, and methods are described herein for process optimization using advanced computational models for data analysis and automated processing. The present disclosure is configured to collect metadata from a node associated with data, wherein the node is configured to process the data, and wherein the metadata comprises real-time parameters of the data; train an instantaneous process identifier (IPI) using the metadata, wherein the IPI comprises a deep learning neural network; analyze the metadata using a classification procedure, wherein the classification procedure determines resources needed to process the data; determine a processing node to process the data, wherein determining the processing node is based on the processing node's availability and the processing node's processing capabilities; and allocate resources to process the data, wherein allocating the resources comprises an instance-based allocation determined by the IPI.