Machine Learning TRIZ Cases

This case uses remote session data and user journeys to train chatbots, improving resolution accuracy and reducing support escalations.

The case scores map units by recency, frequency, location, and route overlap to reduce redownloads and latency.

Extrinsic guidance data filters training text, reducing computing complexity while preserving decision-relevant information.

Ultrasonic B-scans and high-definition rail images help neural networks detect and classify internal and surface faults automatically.

An auxiliary model assigns dummy duration labels to unclicked content, reducing sample bias and improving browsing-duration prediction.

This case uses codebooks, compression-ratio control, and inverse-Hessian feedback to reduce memory and latency without retraining.

This case uses digital twin RSRP prediction to trigger earlier measurement reports and reduce wireless handover latency.

A node index model prioritizes longevity-relevant reviews, improving guidance without requiring consumers to assess every review.

This case compares user-defined undesirable expressions with published media to alert depicted users before unwanted content spreads.

Attributable and un-attributable events are privacy-biased to improve conversion predictions for content selection.

Identity, parity, and opportunity points seed a genetic algorithm for repeatable fairness and accuracy tuning.

This RPA case uses user actions and goals to generate tailored next steps, reducing manual effort in automation program creation.

Machine learning extracts data points while independent users compare sub-transactions and route marked differences for review.

An evaluation unit scores trained AI models and limits publication to all users, selected users, or the creator.

Residualized TensorFlow models separate driver effects from confounders, improving sensitivity analysis for targeted interventions.

This case uses regression machine learning on IR control-flow features to improve static profiles without costly dynamic execution.

Separate coverage and capacity models help NR network nodes predict issues and adjust cell and SSB configurations dynamically.

The network requests only needed AI capabilities, reducing UE transmission overhead.

Local models share updates through a federated server, combining marketing insights while customer data remains with each operator.

Separate modules model uncontrolled and controlled behavior, optimizing actions with less data and more repeatable convergence.

Machine learning matches backhauls to routes while reducing empty miles.

A vulnerability model uses existing network resources as virtual honeypots to detect attack patterns and reduce computing overhead.

Multiple data controllers and DMA masters decompress weight data in parallel, reducing loading time for accelerator cores.

Self-supervised browsing-history training uses attention and prediction heads to improve targeting when data collection is restricted.

Replace brittle OS rules with machine learning that classifies security-data sequences for scalable, precise version identification.

Contextual post-interaction data reaches smart cards and user devices through fallback routing.

A bias-mapping layer replaces domain-specific words with alternatives, improving multi-party summaries without extensive retraining.

Predefined strategies guide genome selection and agent reproduction, enabling predictive models to adapt to dynamic events autonomously.

This case shows how policy-based monitoring adjusts predictive routing decisions to reduce disruptions and SLA failures.

Mean values, sorting, and minimum-derivative analysis rank categorical features, reducing model complexity while supporting generalization.

This engineering case addresses costly temporal modeling by combining self-attention and convolution for efficient local and global context.

Time-series webpage events are transformed into features for machine learning that distinguishes bot behavior from human activity.

Route transactions around overloaded processors with real-time capacity-aware selection.

This case uses causal convolution and support-patch attention to generate data items with fewer examples and training steps.

Transaction-based item vectors match branded products with private-label substitutes for real-time retail recommendations.

Learn how discrete probability sampling in integer coupling layers reduces backpropagation bias for accurate image analysis.

Predefined feature sets are tailored to entity data profiles. Candidate models deliver timely predictions with fewer training resources.

This case uses network-entity trust reevaluation to screen UE data, preserving training reliability while supporting broad collection.

Machine-learning scores are calibrated against subscriber history to support adaptive, real-time digital threat decisions.

Yogi uses additive learning-rate updates to limit decay and improve convergence in non-convex, sparse-gradient training.

Asynchronous federated learning shares hyperparameters instead of raw data, enabling tuned models with less cloud traffic.

Middle-layer neural embeddings improve application similarity and recommendation relevance.

Distributed asynchronous AI processes local and external data to predict service degradation and propose proactive customer actions.

This case separates pre-trained ML parameters from a programmable fine-tuning portion for faster, lower-complexity task adaptation.

Dynamic rate limits use behavioral analysis to protect authentication services while preserving legitimate access during DDoS traffic.

Balances rock types in cutting-image datasets to improve lithology prediction.

Generative models switch content layers as attention changes, creating a feedback loop for scalable personalized delivery.

PMI and data binning correct distribution mismatches in baseline data for consistent anomaly detection and model explainability.

A physical model and learned correction model improve radio-power estimates while adaptive control helps limit base station energy use.

This case combines model requests through a unified NWDAF interface to reduce signaling exchanges and network load.