Derivative works — US Patent 11650968

Defensive Disclosure for US Patent 11650968: Systems and Methods for Predictive Early Stopping in Neural Network Training

This document details derivative variations and integration scenarios for US Patent 11650968, aiming to broaden the scope of disclosed prior art and render incremental improvements by competitors "obvious" or "non-novel" to a person having ordinary skill in the art (PHOSITA). These disclosures are based on the core inventive concepts of predictively stopping neural network (NN) training using a model trained on other NNs' loss data to determine a probability of improvement.

Derivative 1.1: Material & Component Substitution - Neuromorphic Hardware for Training and Early Stopping Model Execution

Enabling Description: The computer-implemented method for predictively stopping neural network training is instantiated on a neuromorphic computing system. Specifically, the target neural network (NN) is a Spiking Neural Network (SNN) trained directly on a neuromorphic processing unit (NPU), such as an IBM TrueNorth or Intel Loihi chip. Training involves adjusting synaptic weights based on spike-timing-dependent plasticity (STDP) or other neuromorphic learning rules. The "loss" for each training period (epoch) is characterized by aggregated event-driven metrics, such as a deviation in output spike patterns from target patterns, total energy consumption per inference, or a measure of network entropy. This event-stream loss history, along with the SNN's topological parameters, is transmitted to a dedicated, co-located neuromorphic early stopping predictive model. This predictive model, also implemented as an SNN on the NPU and pre-trained on a corpus of event-stream loss histories from various other SNN training runs, determines the probability of future improvement. The probability calculation leverages sparse, event-based computations. If the determined probability falls below a pre-configured spike-rate threshold, or if an event-count-based wait value exceeds its limit, a halt signal is issued across the NPU's inter-chip communication fabric, pausing or terminating the SNN training to conserve neuromorphic computational cycles and power.

flowchart TD
    A[SNN Training on Neuromorphic Substrate] --> B{Compute Spike-Rate Loss / Energy Metrics};
    B --> C[Generate Event-Stream Loss History];
    C --> D[Neuromorphic Early Stopping Model (Predictive Model)];
    D -- Inputs: SNN Parameters, Event-Stream Loss History --> E{Determine Probability of Improvement};
    E -- Prob < Threshold OR Wait > Threshold --> F{Halt Signal via Inter-Chip Fabric};
    E -- Else --> A;

Derivative 1.2: Material & Component Substitution - FPGA-accelerated Early Stopping Model with Custom Loss Accumulators

Enabling Description: In this derivative, the training of the target neural network is performed on conventional Graphics Processing Unit (GPU) clusters. However, the predictive early stopping model is accelerated and implemented on a Field-Programmable Gate Array (FPGA). Loss data from the GPU-based NN training, typically a floating-point scalar per epoch, is streamed over a high-bandwidth interconnect (e.g., PCIe, NVLink) to the FPGA. On the FPGA, custom hardware logic gates are configured to perform real-time feature extraction from the incoming loss history, including calculating first and second-order differences, running-mean, and standard deviation in fixed-point arithmetic. The core of the predictive model, such as a Gradient Boosting Machine (GBM) decision tree ensemble, is instantiated with optimized hardware blocks for ultra-low latency inference. The FPGA's digital logic directly performs the probabilistic determination and threshold comparisons. Upon satisfaction of the stopping criteria (e.g., probability below a configurable threshold or wait value exceeding a maximum count), the FPGA generates a dedicated control signal that is sent directly to the GPU cluster's scheduler, issuing a command to halt or suspend the associated NN training job, thereby offloading critical decision-making from the host CPU and achieving microsecond-level response times.

graph TD
    A[GPU Cluster NN Training] --> B{Stream Loss Data};
    B --> C[FPGA Early Stopping Accelerator];
    C -- Custom Logic: GBM Inference, Feature Extraction, Prob Calc --> D{Decision Logic};
    D -- Stop Training --> E[Control Signal to GPU Cluster];
    D -- Continue Training --> B;

Derivative 1.3: Material & Component Substitution - In-Memory Database for Loss History and Federated Learning Model for Prediction

Enabling Description: This derivative employs a distributed in-memory database system (e.g., Apache Ignite, Redis Enterprise) for managing the extensive loss histories of all neural networks, both the target NN and the plurality of other NNs used to train the predictive model. This architecture minimizes I/O latency, facilitating rapid access to historical data. The predictive early stopping model itself is realized through a federated learning paradigm. Multiple client nodes, each responsible for training a subset of NNs or specific hyperparameter configurations, locally maintain and update instances of the early stopping predictive model. These local models are trained on loss histories pertaining to their respective training tasks, leveraging the low-latency in-memory data store. Periodically, these local models transmit anonymized model updates (e.g., gradient aggregates, differential privacy-preserved model parameters) to a central orchestrator. The orchestrator synthesizes these updates into a global early stopping model, which is then broadcast back to all client nodes. When a client trains a target NN, its local federated early stopping model, informed by the latest global model, queries the in-memory database for the target NN's loss history and parameters, computes the probability of improvement, and makes a local decision to stop or continue training based on predefined thresholds.

sequenceDiagram
    participant C1 as Client 1 (NN Training)
    participant C2 as Client 2 (NN Training)
    participant DB as In-Memory Database
    participant O as Orchestrator (Federated Learning)
    C1->>DB: Store Loss History (NN_A)
    C2->>DB: Store Loss History (NN_B)
    O->>DB: Aggregate Anonymized Loss Patterns
    O->>O: Refine Global Early Stopping Model
    O->>C1: Distribute Global Model Update
    C1->>C1: Update Local Federated ES Model
    C1->>DB: Query Loss History (NN_A)
    C1->>C1: Local ES Model Predicts Probability
    C1->>C1: Stop NN_A Training if criteria met

Derivative 1.4: Operational Parameter Expansion - Predictive Early Stopping for Exascale Foundation Model Training

Enabling Description: The computer-implemented method is adapted for the training of massively parameterized foundation models on exascale high-performance computing (HPC) systems. The "target NN" comprises billions to trillions of parameters, trained across hundreds of thousands of interconnected processing units. The "loss" function includes not only primary objective metrics (e.g., perplexity for language models, reconstruction error for generative models) but also secondary, emergent capability indicators derived from periodic evaluations on representative downstream tasks. The "training periods" are measured in highly granular, distributed computational steps rather than traditional epochs. The predictive early stopping model is itself a sophisticated, distributed meta-learning agent (e.g., a large-scale recurrent neural network or transformer) trained on an exabyte-scale dataset of historical training curves, intermediate checkpoint performance, and resource utilization profiles from prior exascale model development efforts. This meta-model accurately determines the probability of achieving further meaningful improvement in the target foundation model's performance within the current training trajectory. Dynamic thresholds are employed, adapting based on observed scaling laws and the current optimization phase (e.g., higher patience during critical emergent learning phases). Stopping signals are disseminated through a low-latency, resilient HPC messaging interface to prevent wasted exaflops and energy consumption.

graph TD
    A[Exascale Compute System] --> B[Distributed Foundation Model Training];
    B --> C{Collect Loss & Performance Metrics (Exabytes)};
    C --> D[Massive Transformer-based ES Model];
    D -- Inputs: Multi-modal training metrics, historic exascale runs --> E{Compute Prob. of Improvement (High Rigor)};
    E -- Prob < Dynamic Threshold OR Wait > Threshold --> F{Distributed Halt Signal};
    F --> G[Optimize Resource Allocation];
    E -- Else --> B;

Derivative 1.5: Operational Parameter Expansion - Micro-Scale Edge AI Training with Resource-Constrained Early Stopping

Enabling Description: This derivative applies predictive early stopping to neural networks undergoing continuous, incremental training on resource-constrained edge devices, such as microcontrollers or embedded System-on-Chips (SoCs). The "target NN" is typically a highly quantized, compact model for tasks like keyword spotting or anomaly detection, constantly adapting to local sensor data. Due to severe memory and computational limitations (e.g., KB of RAM, MHz clock speeds), the "loss history" is maintained as a compact, fixed-size circular buffer storing the last N validation loss values. The "model trained using training losses of a plurality of NNs" is a pre-trained, deeply compressed model (e.g., an extremely shallow decision tree ensemble or a quantized linear regression model), which is executed directly on the edge device's low-power microcontroller unit. Feature extraction from the loss history is minimalistic (e.g., simple moving average, last derivative). The "probability of improvement" calculation is approximated using integer or fixed-point arithmetic. The stopping criteria are critically sensitive to power consumption: training is halted not only when performance gains plateau, but also if the marginal improvement per Joule consumed falls below an energy-efficiency threshold. This ensures maximal battery life and sustained on-device operation.

stateDiagram-v2
    state "Edge Device Idle" as Idle
    state "Collect Sensor Data" as Collect
    state "Tiny NN Training" as TrainNN
    state "Compute Quantized Loss" as ComputeLoss
    state "Update Rolling Loss History" as UpdateHistory
    state "Run Lightweight ES Model" as RunES
    state "Determine Stop Condition" as DecideStop

    Idle --> Collect: Sensor Event
    Collect --> TrainNN: New Data Batch
    TrainNN --> ComputeLoss: Epoch Complete
    ComputeLoss --> UpdateHistory: New Loss Value
    UpdateHistory --> RunES: Loss History Ready
    RunES --> DecideStop: Predictive Output
    DecideStop -- Prob < Threshold OR Wait > Threshold --> Idle: Stop Training (Save Energy)
    DecideStop -- Else --> TrainNN: Continue Training

Derivative 1.6: Cross-Domain Application - Predictive Early Stopping for Biopharmaceutical Drug Discovery (Protein Folding Models)

Enabling Description: The predictive early stopping method is deployed within the computational pipeline for biopharmaceutical drug discovery, specifically for training deep learning models that predict protein folding or simulate molecular dynamics. The "target NN" is a complex graph neural network, a transformer-based architecture (e.g., similar to AlphaFold), or a large-scale generative model, configured to predict the 3D structure of novel proteins from amino acid sequences, or to estimate binding affinities of potential drug candidates. The "loss" function integrates metrics such as Root Mean Square Deviation (RMSD) from experimentally determined structures (if available), predicted binding free energy (ΔG), and metrics quantifying conformational stability or diversity. The "model trained using training losses of a plurality of NNs" is a specialized ensemble predictive model trained on a vast historical dataset comprising thousands of previous protein folding and molecular simulation model training runs. This historical data includes loss curves, convergence patterns, and final RMSD/ΔG scores across a diversity of protein targets, ligand chemistries, and model hyperparameters. This predictive model determines the probability that further training epochs for a current target NN will yield significant improvements in structural accuracy or binding affinity. Early stopping prevents over-allocation of supercomputing resources to models whose potential for further biological relevance is statistically low.

flowchart LR
    A[Raw Protein Sequence Data] --> B{Pre-processing & Graph Generation};
    B --> C[Target Graph NN (Protein Folding)];
    C -- Training Epochs --> D{Compute RMSD & Binding Loss};
    D --> E[Record Loss History & Model State];
    E --> F[Predictive Early Stopping Model (Trained on Protein Folding History)];
    F -- Inputs: NN State, Loss History, Protein Features --> G{Determine Prob. of Improvement (RMSD/Binding)};
    G -- Prob < Threshold OR Wait > Threshold --> H[Halt Supercomputer Training Job];
    G -- Else --> C;

Derivative 1.7: Cross-Domain Application - Predictive Early Stopping for Autonomous Navigation System Training (Reinforcement Learning)

Enabling Description: This derivative applies the early stopping methodology to the training of reinforcement learning (RL) agents for autonomous navigation within high-fidelity simulation environments (e.g., for self-driving vehicles, robotic manipulators, or aerial drones). The "target NN" is the policy network (e.g., a Deep Q-Network, Actor-Critic, or PPO agent) of the RL system. The "loss" is derived from the negative of cumulative episode rewards, policy entropy, or value function errors. The "training periods" correspond to blocks of simulation episodes. The "model trained using training losses of a plurality of NNs" is a meta-RL model or a statistical surrogate model. It is trained on a comprehensive historical dataset of RL agent training logs, including episode rewards, convergence rates, exploration metrics, and final task performance (e.g., success rate, collision avoidance) across various RL algorithms, environmental complexities, and hyperparameter settings. This predictive model determines the probability that the current RL agent will achieve a higher cumulative reward, reduced collision frequency, or improved task completion rate beyond its current trajectory, or surpass the performance of other candidate RL agents. Early stopping mitigates simulation resource expenditure and prevents over-optimization to simulator artifacts, facilitating faster deployment of robust policies.

sequenceDiagram
    participant S as Simulation Environment
    participant RL as RL Agent (Target NN)
    participant ES as Early Stopping Model
    S->>RL: State Observation
    RL->>S: Action
    S->>RL: Reward, Next State
    loop Training Episode
        RL->>RL: Update Policy Network (compute "loss")
        RL->>RL: Record Episode Rewards/Metrics
    end
    RL->>ES: Send Training Metrics (Loss History, Rewards)
    ES->>ES: Determine Prob. of Improvement (Cumulative Reward)
    ES-->>RL: Stop Signal if (Prob < Threshold OR Wait > Threshold)
    RL->>RL: Halt Training or Continue

Derivative 1.8: Cross-Domain Application - Predictive Early Stopping for Predictive Maintenance Models in Industrial IoT

Enabling Description: The early stopping method is implemented for the training of predictive maintenance machine learning models in an Industrial Internet of Things (IIoT) ecosystem. The "target NN" is a recurrent neural network (RNN), a temporal convolutional network (TCN), or a transformer model, designed to process high-frequency time-series data from industrial sensors (e.g., vibration, temperature, current, acoustic emissions) to forecast impending equipment failures. The "loss" function is tailored for prognostics, incorporating metrics such as mean absolute error (MAE) in Remaining Useful Life (RUL) prediction, F1-score for fault classification, or economic cost of false alarms/missed detections. The "model trained using training losses of a plurality of NNs" is a domain-specific predictive model, leveraging a repository of historical training curves, hyperparameter configurations, and deployment performance from various predictive maintenance projects across different industrial assets (e.g., pumps, motors, turbines) and manufacturing plants. This predictive model quantifies the probability that the current RNN's RUL prediction accuracy or fault detection capability will improve beyond its current state, or outperform other models. Early stopping reduces compute load on distributed IIoT training infrastructure (e.g., fog computing nodes) and ensures that effective models are deployed to prevent costly equipment downtime without prolonged, inefficient training cycles.

flowchart TD
    A[Industrial Machinery Sensors] --> B[Time Series Data Stream (Vibration, Temp)];
    B --> C[Data Preprocessing (Anomaly Detection, Feature Eng)];
    C --> D[Target RNN/Transformer (Failure Prediction)];
    D -- Training Epochs --> E{Compute Prediction Accuracy Loss (FPs/FNs)};
    E --> F[Record Loss History];
    F --> G[Predictive Early Stopping Model (Trained on IIoT ML History)];
    G -- Inputs: RNN Params, Loss History, Machine Type --> H{Determine Prob. of Improvement (Accuracy/Cost)};
    H -- Prob < Threshold OR Wait > Threshold --> I[Halt Training & Deploy];
    H -- Else --> D;

Derivative 1.9: Integration with Emerging Tech - AI-driven Adaptive Thresholding for Predictive Early Stopping

Enabling Description: The predictive early stopping method is enhanced by an autonomous AI-driven adaptive thresholding agent. Instead of static, pre-configured probability and wait thresholds, these critical parameters (e.g., probability_threshold, wait_threshold) are dynamically adjusted in real-time by a meta-learning system. This meta-agent, which could be another reinforcement learning agent, a Bayesian optimization system, or an evolutionary algorithm, continuously monitors the efficacy of the early stopping mechanism itself. It collects feedback on various performance indicators such as total training time saved, final model generalization error relative to theoretical optimum, computational cost overhead of the early stopping process, and instances of premature stopping or late stopping. Based on this meta-data, the AI agent iteratively refines the thresholds, predicting optimal values for the current target NN's training context (e.g., specific dataset, architecture, available computational budget, and business objectives prioritizing speed vs. absolute accuracy). This creates a self-optimizing early stopping system that intelligently adapts its stopping policy to achieve superior overall resource efficiency and model development outcomes across diverse machine learning workflows.

stateDiagram-v2
    state "Initialize Fixed Thresholds" as Init
    state "NN Training" as TrainNN
    state "Early Stopping Model Prediction" as ES_Predict
    state "Apply Stopping Logic" as ApplyStop
    state "Collect ES System Performance Metrics" as CollectMetrics
    state "AI-driven Adaptive Threshold Agent" as AdaptiveAgent
    state "Update Adaptive Thresholds" as UpdateThresholds

    Init --> TrainNN
    TrainNN --> ES_Predict: Loss History, NN Params
    ES_Predict --> ApplyStop: Prob, Wait
    ApplyStop --> TrainNN: Continue (if not stopped)
    ApplyStop --> CollectMetrics: Stop/Continue Decision & Outcomes
    CollectMetrics --> AdaptiveAgent: ES Performance Data
    AdaptiveAgent --> UpdateThresholds: New Optimal Thresholds
    UpdateThresholds --> TrainNN: Apply New Thresholds

Derivative 1.10: Integration with Emerging Tech - Blockchain for Verifiable Loss Histories and Auditable Early Stopping

Enabling Description: This derivative integrates blockchain technology to establish an immutable and auditable record of neural network training progress and early stopping decisions. For each training period (epoch) of a target NN, the computed loss, associated hyperparameters, a cryptographic hash of the training data subset used, and the current state of the NN (e.g., model weights hash) are packaged into a transaction. This transaction is cryptographically signed and appended to a distributed ledger (blockchain). This creates a tamper-proof "loss history" record for the NN. The parameters and training data references of the "model trained using training losses of a plurality of NNs" (e.g., the LightGBM models) are also recorded on the blockchain, ensuring transparency of the predictive mechanism itself. When the early stopping model determines a probability of improvement and subsequently issues a stop/continue decision, this decision, along with the inputs (loss history hash, current hyperparameters), the calculated probability, and the thresholds applied, is also recorded as a signed transaction on the blockchain. This provides a verifiable, unalterable audit trail for every training run and stopping decision, which is critical for compliance in regulated industries (e.g., financial services, healthcare) and for establishing trusted provenance of AI models. Smart contracts can be deployed to automatically enforce stopping rules or trigger alerts based on on-chain conditions.

flowchart TD
    A[NN Training Epoch] --> B{Compute Loss, Collect Params};
    B --> C[Hash Loss & Params];
    C --> D[Create Blockchain Transaction];
    D -- Add to Ledger (Immutable) --> E[Distributed Ledger (Blockchain)];
    E --> F{Retrieve Verifiable Loss Histories & Model Params};
    F --> G[Predictive Early Stopping Model (Oracle Node)];
    G -- Inputs: Verifiable History, Current Params --> H{Determine Prob. of Improvement};
    H -- Decision (Stop/Continue) --> I[Record Decision on Blockchain (Signed)];
    I --> J[Auditable Training Record];

Derivative 1.11: Integration with Emerging Tech - Real-time IoT Performance Monitoring for Early Stopping with Telemetry

Enabling Description: The early stopping method is augmented by integrating real-time operational performance telemetry from deployed IoT devices that utilize the neural network post-training. For instance, a NN trained for computer vision tasks (e.g., object detection) on smart cameras, has its training progress informed by live feedback loops from a fleet of deployed cameras. This real-world telemetry includes metrics such as actual detection accuracy on live data streams, inference latency under varying network conditions, false positive/negative rates in different environments, and energy consumption during real-world inference. This live, contextual IoT data is streamed back to the training system. The "loss history" for the predictive early stopping model is dynamically extended to include these real-world performance indicators, alongside traditional validation loss. Consequently, the "probability of improvement" is refined to represent the likelihood of achieving better real-world operational performance (e.g., higher true positive rate, lower inference energy) rather than solely optimizing for static validation set metrics. This approach enables a more robust and practical early stopping mechanism, preventing models from over-training on potentially unrepresentative validation data and ensuring fitness for purpose in diverse operational environments.

graph TD
    subgraph Training System
        A[NN Training] --> B{Compute Validation Loss};
        B --> C[Predictive ES Model];
    end

    subgraph IoT Deployment
        D[Deployed NN on IoT Device] --> E{Real-time Inference};
        E --> F[Performance Telemetry (Accuracy, Latency)];
        F --> G[IoT Sensors (Contextual Data)];
    end

    F & G --> H[Telemetry Aggregation & Preprocessing];
    H --> C;
    C -- Inputs: Val Loss, IoT Telemetry, Context --> I{Determine Prob. Real-World Improvement};
    I -- Prob < Threshold OR Wait > Threshold --> A: Halt Training;
    I -- Else --> A: Continue Training;

Derivative 1.12: The "Inverse" or Failure Mode - Energy-Aware Early Stopping for Sustainable AI Training

Enabling Description: This derivative integrates explicit energy cost considerations into the early stopping decision process. The "computer-implemented method" tracks, alongside the traditional loss, the cumulative energy consumption (e.g., GPU Watt-hours, total data center power draw) associated with the target NN's training. The "model trained using training losses of a plurality of NNs" is extended to incorporate historical energy consumption profiles alongside loss histories. The "probability of improvement" is redefined as the likelihood of achieving a target loss within a predefined energy budget, or, more precisely, the probability of obtaining a significant marginal improvement in loss per unit of additional energy expended. A dynamic energy_efficiency_threshold is introduced: if the predicted energy cost to achieve further significant loss reduction exceeds this threshold, or if a dedicated energy_wait_value (incremented when energy efficiency drops below a minimum) surpasses its limit, training is stopped. This allows for a "low-power" or "sustainable AI" early stopping mode, where training is halted even if minor performance gains are still possible, prioritizing energy conservation and carbon footprint reduction over maximizing infinitesimal model performance.

flowchart TD
    A[NN Training] --> B{Compute Loss & Energy Consumption};
    B --> C[Loss History + Energy History];
    C --> D[Predictive ES Model (Energy-Aware)];
    D -- Inputs: NN Params, Loss/Energy History --> E{Determine Prob. of "Efficient" Improvement};
    E -- Prob < Energy_Threshold OR Wait > Energy_Wait_Threshold --> F[Halt Training (Energy Optimized)];
    E -- Else --> A;

Derivative 1.13: The "Inverse" or Failure Mode - Degradation-Tolerant Early Stopping for Continual Learning Systems

Enabling Description: The early stopping mechanism is specifically tailored for continual learning (CL) systems, where neural networks learn sequentially from non-stationary data streams without forgetting previously acquired knowledge. For the "target NN" in a CL setting, the "loss" function is augmented to include a "forgetting loss" (e.g., evaluation on a small, representative replay buffer or on a set of pseudo-rehearsal samples from old tasks), in addition to the current task loss. The "model trained using training losses of a plurality of NNs" is built from historical CL experiments, encompassing various CL strategies (e.g., regularization-based, replay-based) and their associated performance trajectories on both current and old tasks. This predictive model determines the "probability of net improvement," defined as the likelihood of achieving better performance on the current task while simultaneously avoiding catastrophic forgetting on previous tasks. If the predictive model forecasts that continued training on the new data stream will lead to an unacceptable probability of significant degradation on previously learned tasks (even if current task loss is improving), early stopping is triggered. This forces the CL system into a "degradation-avoidance" or "limited-functionality" mode, where it might stop learning, trigger a consolidation phase, or initiate a task-switching mechanism.

flowchart LR
    A[New Task Data Stream] --> B{Continual Learning NN Training};
    B -- Training Epochs --> C{Compute Current Task Loss & Forgetting Loss (Replay Buffer)};
    C --> D[Combined Loss History];
    D --> E[Predictive ES Model (Degradation-Aware)];
E -- Inputs: NN State, Combined Loss History --> F{Determine Prob. of NET Improvement (New + Old Tasks)};
    F -- Prob < Degradation_Threshold OR Wait > Forgetting_Wait --> G[Halt Training (Prevent Forgetting)];
    G -- Else --> B;

Derivative 1.14: The "Inverse" or Failure Mode - Safe-Failure Early Stopping for Critical Systems

Enabling Description: This derivative implements a safety-critical variant of the predictive early stopping method, designed for neural networks operating in high-assurance or life-critical applications (e.g., medical diagnostics, autonomous flight control, industrial safety systems). The "loss" function for the target NN is augmented with real-time safety metrics, such as a probabilistic quantification of system uncertainty, deviation from verified safe operating envelopes, or the predicted probability of violating critical safety constraints. The "model trained using training losses of a plurality of NNs" is explicitly trained on a curated dataset of safety-validated training runs, where each historical trajectory includes both performance loss and comprehensive safety assurance metrics. This specialized predictive model calculates the "probability of safe improvement," which is the likelihood that further training will enhance performance without compromising safety-critical thresholds. If the predictive model determines that the probability of maintaining safety during continued training falls below a stringent, pre-defined critical_safety_threshold, or if it forecasts an increasing trend toward an unsafe operating region, training is immediately halted in a "safe-failure mode." This mode prioritizes system integrity and human safety by, for example, reverting to the last-known safe model checkpoint, triggering an emergency alert, or initiating a controlled shutdown process, overriding traditional performance-only stopping criteria.

graph TD
    A[NN Training (Critical System)] --> B{Compute Standard Loss & Safety Metrics (e.g., Uncertainty, Safety Constraint Violation)};
    B --> C[Augmented Loss History (Includes Safety)];
    C --> D[Predictive ES Model (Safety-Critical)];
    D -- Inputs: NN State, Augmented Loss History, Safety Parameters --> E{Determine Prob. of SAFELY Improving};
    E -- Prob_Safety < Critical_Threshold --> F[Initiate Safe-Failure Protocol];
    F --> G[Halt Training & Revert to Safe State / Alert];
    E -- Else --> A;

Combination Prior Art Scenarios with Open-Source Standards

The core method of US11650968 can be combined with existing open-source standards, making further incremental improvements obvious to a person skilled in the art.

US11650968 + MLflow Tracking Standard:
- Description: The predictive early stopping method (Claim 1) is integrated into an experimental tracking workflow managed by the open-source MLflow platform. During the training of a target neural network, each epoch's loss and other relevant metrics are logged using mlflow.log_metric(). The loss history described in US11650968 is directly extracted from the MLflow tracking server's backend database (e.g., PostgreSQL, SQLite, or equivalent artifact store for mlruns). The "model trained using training losses of a plurality of NNs" ingests this MLflow-tracked data to build its predictive capabilities. When the early stopping criteria (probability < threshold OR wait value > threshold) are met, the early stopping module utilizes mlflow.end_run() or a custom status update to mark the experiment as stopped and log the reason, final predicted loss, and other decision parameters back into MLflow, creating a comprehensive and auditable record of the early stopping decision within a standard ML lifecycle management framework.
- Obviousness: It would be obvious to a PHOSITA in MLOps to integrate an efficient early stopping mechanism with a widely adopted experiment tracking standard like MLflow. The core functionalities of MLflow—logging metrics, parameters, and managing run lifecycles—directly align with the data collection and control requirements for implementing and documenting the patented early stopping method.
US11650968 + ONNX (Open Neural Network Exchange) Standard:
- Description: The predictive early stopping model itself (e.g., a LightGBM ensemble or other tree-based model as mentioned in the patent) is converted and serialized into the Open Neural Network Exchange (ONNX) format. This ONNX-formatted early stopping model is then deployed via an ONNX Runtime. When the target NN is being trained, its loss history and hyperparameters are processed to extract the necessary features. These features are then provided as input to the ONNX Runtime, which executes the ONNX-formatted predictive early stopping model to determine the probability of improvement. This allows the early stopping logic to be executed efficiently and consistently across diverse hardware (CPUs, GPUs, custom accelerators) and software environments (TensorFlow, PyTorch, Caffe2) without requiring the entire training stack to be uniform. The communication of the stop signal would occur through standard API calls (e.g., Python function calls) from the ONNX Runtime host to the NN training process.
- Obviousness: Given the industry-wide focus on model interoperability and efficient deployment, it would be obvious for a PHOSITA to leverage a universal model exchange format like ONNX for the predictive early stopping model. Standardizing the representation and execution of the predictive logic ensures broader applicability and reduces integration complexity across heterogeneous ML ecosystems.
US11650968 + Prometheus Monitoring for Resource-Aware Early Stopping:
- Description: The predictive early stopping method is augmented with real-time resource utilization monitoring through the open-source Prometheus system. Alongside the target NN's training, custom Prometheus exporters running on the training infrastructure (e.g., GPU servers, compute clusters) continuously scrape and expose metrics such as GPU utilization percentage, VRAM consumption, CPU load, network bandwidth, and instantaneous power draw. This rich set of resource metrics, timestamped and stored in Prometheus's time-series database, is incorporated into an extended "loss history" for the early stopping predictive model. The predictive model (Claim 1), now trained on historical data correlating loss trajectories with resource consumption, determines the probability of achieving further loss reduction with a favorable resource efficiency. Prometheus Alertmanager rules can be configured to trigger a soft stop or a "low-power training mode" if the predicted resource consumption for marginal gain becomes excessively high, even before pure loss-based stagnation. This integration provides a comprehensive, resource-aware early stopping strategy within a standard, widely adopted monitoring framework.
- Obviousness: For a PHOSITA in cloud computing or MLOps concerned with optimizing infrastructure costs and efficiency, it would be obvious to combine a sophisticated early stopping mechanism with an industry-standard monitoring solution like Prometheus. The ability to collect and analyze granular resource metrics in real-time naturally extends the decision-making capabilities of an early stopping algorithm beyond just performance metrics to include operational efficiency.