Derivative works — US Patent 5704012

Defensive Disclosure: Adaptive Resource Allocation Using Neural Networks (US5704012)

This defensive disclosure aims to broaden the scope of existing prior art related to adaptive resource allocation using neural networks, making future incremental improvements in this domain obvious or non-novel. The derivations explore variations in materials, operational parameters, cross-domain applications, integration with emerging technologies, and failure modes, all grounded in the principles outlined in US Patent 5704012, specifically Claim 1.

Core Claim 1 of US5704012 (Summary for reference):

A method for controlling the response of a computer system to a workload and configuration, comprising: gathering performance data; constructing a neural network (inputs: workload, configuration; output: system response); training the neural network with performance data to model the computer system; determining system response from the trained network; and allocating resources based on the determined response and user-specified performance objectives.

Derivative Variations

1. Material & Component Substitution: Neuromorphic Resource Orchestration with Non-Volatile Memory

Enabling Description:
A method for adaptive resource allocation in a computer system utilizing a neuromorphic processing unit for neural network execution and non-volatile memory (NVM) for dynamic resource pooling. Performance data, including instruction per cycle (IPC) rates, cache hit/miss ratios, memory access latencies, and inter-core communication overhead, is gathered by a dedicated Field-Programmable Gate Array (FPGA)-based monitoring unit. This high-resolution performance data is streamed to a neural network, emulated on a neuromorphic chip (e.g., Intel Loihi or IBM TrueNorth), which is specifically designed to model the computer system's behavior with event-driven, sparse activity patterns. The neuromorphic neural network is trained using an online, asynchronous reinforcement learning algorithm, directly adapting its synaptic weights based on observed system state transitions. The predicted system response from the neuromorphic network guides a resource manager, implemented as a custom hardware accelerator (e.g., an ASIC), to allocate resources. These resources include dynamically partitioning portions of a shared Phase-Change Memory (PCM) or Resistive RAM (ReRAM) array among various job classes, adjusting memory access priority queues, and configuring specialized interconnects to meet user-defined Service Level Objectives (SLOs) such as guaranteed throughput or maximum latency thresholds.

graph TD
    A[Workload Request Streams] --> B(FPGA Performance Monitor<br>@ System Interconnect)
    B --> C{Event-Driven Performance Data}
    C --> D[Neuromorphic Processing Unit<br>(Spiking Neural Network Model)]
    D -- Trained Model Updates --> E(ASIC Resource Manager<br>Hardware Accelerator)
    F[User SLOs<br>(Latency, Throughput)] --> E
    E --> G[NVM Resource Pools<br>(PCM/ReRAM Partitions)]
    G --> H[Computer System Core Resources<br>(CPU, GPU, Interconnect)]
    H -- Actual Performance Feedback --> B

2. Operational Parameter Expansion: Ultra-Low-Latency, Hyper-Scale Resource Allocation

Enabling Description:
A method for ultra-low-latency, hyper-scale resource allocation within a distributed computing environment, specifically an exascale supercomputing cluster comprising millions of interconnected processing elements (CPUs, GPUs, FPGAs). Performance data, including sub-microsecond-level process latencies, inter-node communication contention, fabric bandwidth utilization, and on-die temperature differentials, is gathered at a frequency exceeding 100 kHz across all computational nodes. A massively parallelized Spiking Neural Network (SNN) model, distributed across multiple GPU clusters (e.g., NVIDIA DGX systems interconnected via NVLink), is constructed and trained on this high-frequency, high-volume telemetry. The SNN's inputs include real-time distributed workload patterns (e.g., Message Passing Interface (MPI) message sizes, kernel execution times, data movement rates) and dynamic resource topology (e.g., transient link failures, power fluctuations, active core counts). The SNN outputs predictive response times, resource saturation points, and potential thermal hotspots with probabilistic confidence levels. Based on these real-time predictions and pre-defined Quality-of-Service (QoS) objectives (e.g., maximum 5µs latency for critical path operations, 99.9999% throughput for specific data streams), an adaptive resource orchestrator, employing a hierarchical reinforcement learning agent with dynamic policy adaptation, allocates compute, memory, and high-speed interconnect fabric resources (e.g., InfiniBand, Slingshot) across the cluster. This orchestrator adjusts job scheduling policies (e.g., gang scheduling, topological scheduling), re-routes network traffic, and dynamically reconfigures memory placement in near real-time to mitigate predicted performance degradation and maintain hyper-scale efficiency.

graph TD
    A[Exascale Workload<br>(MPI, Kernels, Data Streams)] --> B(Distributed Telemetry Network<br>@100kHz+)
    B --> C{High-Frequency, High-Volume Performance Data}
    C --> D[Massively Parallel SNN Model<br>(GPU Clusters + NVLink)]
    D -- Predicted Response/Saturation/Hotspots --> E(Hierarchical RL Orchestrator<br>+ Dynamic Policy Engine)
    F[QoS Objectives<br>(Latency, Throughput, Efficiency)] --> E
    E --> G[Resource Allocation Actions<br>(Compute, Memory, Interconnect Routing)]
    G --> H[Exascale Supercomputing System]
    H -- Actual Micro-Latencies/Utilization --> B

3. Cross-Domain Application: Manufacturing/Industrial Automation

Enabling Description:
A method for adaptive resource allocation in a flexible manufacturing system (FMS) comprising a plurality of robotic workstations, CNC machines, and autonomous material handling units (e.g., AGVs, AMRs). Performance data, including robot cycle times, machine tool wear, buffer queue lengths, sensor fusion data (e.g., vision system defect rates, force sensor anomaly detection), and production throughput for different product families (job classes), is gathered via industrial IoT sensors communicating over OPC-UA and EtherCAT protocols. A deep neural network, specifically a Convolutional Neural Network (CNN) for sensor data processing combined with a Recurrent Neural Network (RNN) for temporal process dynamics, is constructed. This network has inputs representing machine states, current work-in-progress inventory levels, maintenance schedules, and production targets. The network is trained with historical operational data and simulated anomaly scenarios to model the FMS performance, predicting bottlenecks, quality deviations, and potential equipment failures. Based on these predictions and user-defined production objectives (e.g., maximize throughput for high-margin product A, minimize defect rate for critical component B, optimize energy consumption), a Manufacturing Execution System (MES) controller, integrating the neural network's real-time output, dynamically re-allocates resources. This includes adjusting robotic task assignments, re-prioritizing material flow via AGVs/AMRs, optimizing machine parameters (e.g., spindle speeds, feed rates, laser power), and dynamically adjusting buffer capacities to maintain optimal production flow and quality targets.

graph TD
    A[Production Orders/Workload] --> B(FMS IIoT Sensors<br>OPC-UA / EtherCAT)
    B --> C{Manufacturing Performance Data<br>(Sensor Fusion, Cycle Times, WIP)}
    C --> D[Hybrid CNN-RNN<br>(Production & Anomaly Model)]
    D -- Predicted Performance/Bottlenecks --> E(MES Controller<br>+ Optimization Engine)
    F[Production Objectives<br>(Throughput, Quality, Energy)] --> E
    E --> G[Resource Allocation Actions<br>(Robots, AGVs/AMRs, CNC, Buffers)]
    G --> H[Flexible Manufacturing System]
    H -- Actual Production Metrics/Feedback --> B

4. Cross-Domain Application: Smart Grid/Energy Management

Enabling Description:
A method for adaptive allocation of energy resources within a distributed smart grid infrastructure, balancing electricity demand and supply across various generation sources (e.g., solar farms, wind turbines, conventional power plants), energy storage units (e.g., grid-scale batteries), and demand-side management loads. Performance data, including real-time power generation output (MW), consumption patterns from different consumer classes (e.g., industrial, residential, commercial), dynamic grid stability metrics (e.g., voltage, frequency, phase angles, line losses), and energy storage levels (State of Charge), is gathered from Supervisory Control and Data Acquisition (SCADA) systems, smart meters, and grid sensors. A deep recurrent neural network (RNN), specifically a Long Short-Term Memory (LSTM) network, is constructed with inputs correlating real-time weather forecasts, historical demand profiles, fluctuating generation capacities, and dynamic grid topology. The LSTM network is trained to predict future demand-supply imbalances, grid stress points, and potential cascading failures across multiple look-ahead horizons. Based on these predictions and user-specified grid objectives (e.g., maximize renewable energy penetration, minimize curtailment, ensure N-1 security, optimize cost of energy distribution), a Grid Management System (GMS) controller, informed by the RNN's output, dynamically allocates energy resources. This involves real-time dispatching of generation units, scheduling battery charging/discharging cycles, enacting granular demand-response programs across consumer classes, and optimizing power flow control devices (e.g., Flexible AC Transmission Systems - FACTS) to maintain grid stability, efficiency, and resilience.

graph TD
    A[Energy Demand/Weather Forecast] --> B(SCADA/Smart Meters/Grid Sensors)
    B --> C{Real-time Energy Performance Data<br>(Generation, Consumption, Stability, Storage)}
    C --> D[Long Short-Term Memory (LSTM) Network<br>(Grid Prediction Model)]
    D -- Predicted Imbalances/Stress/Failures --> E(Grid Management System Controller<br>+ Optimization Algorithms)
    F[Grid Objectives<br>(Renewable % , Reliability, Cost)] --> E
    E --> G[Resource Allocation Actions<br>(Generation Dispatch, Battery Scheduling, Demand Response, FACTS)]
    G --> H[Distributed Smart Grid Infrastructure]
    H -- Actual Grid Metrics/Feedback --> B

5. Cross-Domain Application: Logistics/Supply Chain Optimization

Enabling Description:
A method for adaptive resource allocation in a global, multi-modal logistics and supply chain network, optimizing the movement and storage of goods across diverse transportation modes (ee.g., road, rail, air, sea) and interconnected warehouse/distribution centers. Performance data, including real-time vehicle utilization, estimated delivery times, container loading efficiencies, warehouse inventory levels, order fulfillment rates, port congestion, and traffic conditions for different product categories (job classes), is gathered from GPS trackers, telematics systems, warehouse management systems (WMS), and enterprise resource planning (ERP) systems. A Graph Neural Network (GNN) is constructed with inputs representing the dynamic supply chain network topology (nodes as locations, edges as routes), real-time demand fluctuations, fleet availability and status, weather conditions, and logistical constraints. The GNN is trained with historical logistics data, real-time sensor streams, and simulated disruption events (e.g., port strikes, vehicle breakdowns) to model complex supply chain dynamics, predicting delays, inventory shortages, optimal routing, and potential disruptions. Based on these predictions and user-specified logistics objectives (e.g., minimize transportation costs, maximize on-time delivery, optimize inventory turnover, reduce carbon footprint), a Supply Chain Orchestration (SCO) platform, utilizing the GNN's output, dynamically allocates resources. This includes optimizing fleet routing and scheduling, dynamically re-assigning warehouse picking and packing tasks, adjusting inventory placement across the network (e.g., pre-positioning goods), and re-negotiating carrier contracts in response to real-time conditions.

graph TD
    A[Orders/Demand Fluctuations] --> B(GPS/Telematics, WMS, ERP Systems<br>Real-time Sensor Data)
    B --> C{Logistics Performance Data<br>(Utilization, Delivery, Inventory, Traffic, Weather)}
    C --> D[Graph Neural Network (GNN)<br>(Supply Chain Dynamics Model)]
    D -- Predicted Delays/Shortages/Disruptions --> E(Supply Chain Orchestration Platform<br>+ Multi-Objective Optimizer)
    F[Logistics Objectives<br>(Cost, Delivery Speed, Inventory, CO2)] --> E
    E --> G[Resource Allocation Actions<br>(Fleet Routing, Warehouse Tasks, Inventory Placement, Carrier Mgmt.)]
    G --> H[Global Supply Chain Network]
    H -- Actual Logistics Metrics/Feedback --> B

6. Integration with Emerging Tech: Hybrid Cloud with AI/IoT/Blockchain

Enabling Description:
A method for self-optimizing, transparent, and auditable resource allocation in a hybrid cloud computing environment, integrating real-time IoT sensor data, advanced AI-driven meta-optimization, and blockchain-based provenance. Edge-deployed IoT sensors (e.g., power meters, environmental sensors, custom hardware probes) continuously monitor individual virtual machine (VM), container, and bare-metal server performance metrics (e.g., CPU cycles, memory pressure, I/O wait times, network latency, energy consumption, thermal profiles) and underlying infrastructure health. This fine-grained, high-frequency performance data is streamed to a central AI orchestration layer. A hierarchical ensemble of neural networks (e.g., a combination of deep feedforward networks for static configurations and recurrent networks for temporal patterns), orchestrated by a Reinforcement Learning (RL) agent, constructs a dynamic, predictive model of the hybrid cloud's behavior, predicting resource contention, Service Level Agreement (SLA) violations, and optimal power states. The RL agent, utilizing these predictions, dynamically adjusts resource allocations (e.g., VM migration across cloud providers, container auto-scaling policies, network bandwidth shaping, dynamic voltage and frequency scaling (DVFS)) across on-premise, private, and public cloud infrastructure. Each significant resource allocation decision, along with its justification derived from the neural network's probabilistic outputs and the RL agent's policy, is immutably recorded as a transaction on a permissioned distributed ledger (blockchain, e.g., Hyperledger Fabric). This blockchain ensures immutable auditability, transparency, and cryptographically provable compliance with user-specified performance objectives, contractual SLAs, and regulatory requirements. Furthermore, a Bayesian optimization algorithm continuously adapts and fine-tunes the neural network's hyperparameters and the RL agent's reward functions, achieving autonomous, meta-level optimization of the entire resource management system based on observed, long-term performance improvements and cost efficiencies.

graph TD
    A[Hybrid Cloud Workload & Traffic] --> B(IoT Edge Sensors + Software Performance Monitors)
    B --> C{Real-time Performance & Environmental Data}
    C --> D[Hierarchical NN Ensemble<br>(Cloud Behavior & SLA Prediction)]
    D -- Predicted Contention/Violations --> E(RL Orchestrator + Bayesian Optimizer)
    F[User SLOs/SLAs & Regulatory Compliance] --> E
    E --> G[Blockchain Ledger<br>(Immutable Allocation Records & Rationale)]
    E --> H[Dynamic Resource Allocator<br>(VMs, Containers, Network, DVFS)]
    H --> I[Hybrid Cloud Infrastructure<br>(On-prem, Private, Public)]
    I -- Actual Performance/Consumption --> B
    G -- Audit Trail/Verification --> J[Auditors/Users/Regulatory Bodies]

7. The "Inverse" or Failure Mode: Graceful Degradation & Safe-Mode Resource Allocation

Enabling Description:
A method for ensuring graceful degradation and safe-mode resource allocation in a mission-critical, real-time embedded system (e.g., an autonomous vehicle control unit, a nuclear power plant safety system, or a medical life-support device) operating under anticipated or actual component failures, environmental disturbances, or severe resource constraints. The system continuously gathers highly redundant performance data, including cross-checked sensor readings, internal diagnostic codes, CPU load, memory integrity checks (ECC), bus arbitration success rates, and task completion rates for safety-critical (high integrity) and non-safety-critical (low integrity) functions. A specialized, fault-tolerant neural network architecture (e.g., a modular neural network with active redundancy in critical layers, an ensemble of diverse neural networks with a voting mechanism, or a Bayesian Neural Network for uncertainty quantification) is constructed and trained. This training incorporates both normal operational data and an extensive library of simulated failure modes and degraded states. This neural network model continuously predicts potential system failures, resource exhaustion states, and deviations from a pre-defined "safe operating envelope" with probabilistic confidence. Upon detecting a predicted or actual failure/constraint (e.g., sensor malfunction, CPU core degradation, power supply instability, cyber-attack signature), the system automatically initiates a "limited-functionality" or "safe-mode" protocol. The neural network's output then directly guides a safety-critical resource manager, implemented as a certified real-time operating system (RTOS) kernel module, to:

Strictly prioritize safety-critical job classes: Allocate all available, verified minimal resources (e.g., 10% CPU, 20% memory, dedicated I/O channels) exclusively to core safety functions, aggressively shedding or suspending all non-essential services.
Activate redundant components and failover mechanisms: Trigger automatic switching to backup sensors, redundant processors, or failover network paths.
Initiate controlled shutdown or recovery procedures: Execute pre-defined, certified sequences for maintaining system integrity, safely transitioning to a minimal operational state, or preparing for external intervention.
The allocation strategy ensures the system remains within pre-certified "safe-operating envelopes" by dynamically scaling back performance, shedding non-essential workload, and reconfiguring hardware based on the neural network's continuous, real-time assessment of system health, failure propagation, and available fault-isolated resources. The primary objective shifts from optimal performance to maximum safety and survivability.

graph TD
    A[Mission-Critical Workload<br>(Safety-Critical & Non-Critical)] --> B(Redundant System Sensors<br>+ Diagnostics & ECC)
    B --> C{Verified System Health & Performance Data<br>(incl. Anomaly Signatures)}
    C --> D[Fault-Tolerant Neural Network<br>(Failure Prediction & Uncertainty Quantification)]
    D -- Predicted Failure/Constraint & Confidence --> E{Operational Mode Decision<br>(RTOS Kernel Module)}
    E -- Normal Operation --> F[Normal Resource Allocation]
    E -- Safe Mode/Degradation --> G[Safety-Critical Resource Manager<br>(Prioritization, Redundancy Activation)]
    F --> H[Full System Functionality]
    G --> I[Limited Functionality/Safe Mode<br>(Core Safety Functions Only)]
    H -- Actual Performance Feedback --> B
    I -- Degraded Performance/Diagnostics --> B
    J[Safety Objectives<br>+ Failure Modes & Safe Operating Envelopes] --> D

Combination Prior Art Scenarios with Open-Source Standards

These scenarios demonstrate how the principles of US Patent 5704012 could be readily combined with existing, widely adopted open-source standards to create obvious improvements.

1. US5704012 + Linux Kernel & cgroups (Control Groups)

Description:
A PHOSITA would find it obvious to integrate the neural network-based adaptive resource allocation method of US5704012 with the cgroups (control groups) feature of the Linux kernel. The neural network controller, trained on performance data gathered from a Linux system running diverse workloads, would dynamically determine optimal resource parameters for different cgroups (e1.g., CPU shares, memory limits, I/O bandwidth, block I/O weights). The resource manager component of the operating system (as described in US5704012) would then translate these neural network outputs into specific cgroup configurations. These configurations would be applied in real-time by writing to the cgroup virtual filesystems (e.g., /sys/fs/cgroup/cpu/user_jobs/cpu.shares) to adjust the resource allocations for various job classes. This combination provides an intelligent, adaptive layer for managing system resources on top of a mature and widely adopted open-source Linux resource management framework, enabling the system to dynamically respond to changing workloads and configurations in an optimized manner.

2. US5704012 + Kubernetes Resource Management & Prometheus Monitoring

Description:
It would be obvious to integrate the adaptive neural network controller of US5704012 with Kubernetes for container orchestration and Prometheus for monitoring. Prometheus, acting as the "computer system performance monitor" (step 1 of Claim 1), would continuously collect granular metrics (e.g., CPU utilization, memory usage, network I/O, latency, error rates) from Kubernetes pods, nodes, and services. This performance data, including workload and configuration details (e.g., number of replicas, deployed services), would be used to train the neural network "system model" (step 3). The trained neural network "controller" (step 5) would then process user-defined performance objectives (e.g., target latency for a specific microservice, desired throughput for a batch processing job, maximum cost for a given workload) and output optimal resource requests, limits, and autoscaling parameters (e.g., minReplicas, maxReplicas for Horizontal Pod Autoscaler - HPA, or resource values for Vertical Pod Autoscaler - VPA). The Kubernetes API would then serve as the "resource manager" to effect these allocations by dynamically updating pod specifications, HPA/VPA configurations, or even influencing node scheduling decisions (e.g., taint/toleration, affinity rules) within the cluster. This combination enhances standard container orchestration with intelligent, adaptive resource management.

3. US5704012 + Apache Mesos/YARN & Hadoop/Spark Workloads

Description:
A PHOSITA would find it obvious to apply the neural network-based adaptive resource allocation method of US5704012 to cluster management frameworks like Apache Mesos or YARN (Yet Another Resource Negotiator) which manage distributed resources for big data processing frameworks such as Hadoop and Spark. Performance data, including job completion times, task execution durations, resource utilization per executor (CPU, memory, disk I/O), and network shuffle I/O for various Hadoop MapReduce, Spark Streaming, and Spark SQL job classes, would be gathered from the Mesos/YARN monitoring components. This data would be used to train a neural network to model the performance characteristics of the distributed cluster under different workloads and resource configurations. The neural network controller would then dynamically advise the Mesos/YARN scheduler or resource manager on optimal resource allocations for different job queues or frameworks. These recommendations would aim to meet user-defined performance objectives (e.g., guarantee 95th percentile latency for interactive Spark queries, complete daily Hadoop reports by a strict deadline, ensure fairness among multi-tenant workloads). The Mesos/YARN resource management APIs would be used to implement the neural network's recommendations, dynamically adjusting resource reservations, container sizes, and task priorities across the cluster.