Active Learning for Anomaly Labeling — PatSnap Eureka
Active Learning for Anomaly Labeling 2026
Active learning for anomaly labeling selectively queries domain experts to label only the most informative anomalous samples, reducing annotation cost while preserving detection accuracy. Patent filings span 2013–2026 across industrial, cybersecurity, scientific, and computer vision domains.
How Active Learning Is Reshaping Anomaly Annotation
Active learning for anomaly labeling addresses a core challenge in machine intelligence: anomalies are rare, labels are expensive, and purely unsupervised detectors suffer from excessive false alarms under concept drift. The core mechanism is an iterative loop where a model scores unlabeled data, a query strategy selects informative candidates, a human oracle labels them, and the model is retrained.
Within this dataset, four principal sub-mechanisms organize the field: uncertainty and inconsistency scoring, graph-based propagation, generative and adversarial augmentation, and feedback-driven retraining pipelines. These approaches span both academic literature focused on query strategy design and patent filings covering deployed annotation services and industrial inspection systems.
Publication and filing dates range from 2008 to 2026, revealing a three-phase trajectory: a foundational phase (2008–2015) establishing stopping criteria and disagreement-based strategies, a development phase (2016–2021) with deep active learning methods and first substantive patent filings, and a maturity phase (2022–2026) concentrated on anomaly-specific architectures and deployed feedback loops.
Innovation is moderately concentrated at the top in this dataset: Google, Amazon, Schlumberger, and IBM together account for roughly half of the patent filings in retrieved records. Chinese university assignees—Zhejiang University and Guangdong University of Technology—target anomaly-specific active learning architectures, differentiating from US hyperscaler filings that address more general annotation infrastructure.
Filing Activity and Technology Cluster Distribution
The retrieved dataset spans patent filings and literature publications from 2008 to 2026. Activity accelerated from 2020 onward, with the maturity phase (2022–2026) producing anomaly-specific feedback-loop and graph-propagation patents.
Patent Filings by Jurisdiction (Dataset Snapshot)
The US accounts for the largest share of patent filings in this dataset (~12 filings), followed by CN (~6), with WO, EP, and IN each contributing 2 filings in retrieved records.
↗ Click bars to exploreTechnology Cluster Patent Count — Active Learning Anomaly Labeling (Dataset Snapshot)
Feedback-loop and uncertainty-based query strategy clusters account for the highest patent counts in this dataset, each with approximately 6–7 filings across US and international jurisdictions in retrieved records.
↗ Click bars to exploreKey Deployment Domains for Active Anomaly Labeling
Active learning for anomaly labeling has been deployed across six major application domains in retrieved records, from industrial inspection and cybersecurity to astronomical discovery and subsurface exploration. Each domain presents distinct labeling cost and concept-drift challenges that active learning directly addresses.
Industrial Inspection & Manufacturing
Graph-propagation patents from Guangdong University of Technology and Zhejiang University target product image anomaly detection using autoencoder reconstruction errors to score defective products. Shenzhen Xinrunfulian Digital Technology’s 2021 CN patent applies confidence-threshold-based active labeling to fault detection classification models in manufacturing, reducing manual annotation overhead in incremental model updates. General Electric’s 2020 US and EP patents address engineering component anomaly analytics with incremental AI model updates.
Industrial AICybersecurity & Fraud Threat Scoring
Sift Science filed two closely related US patents in 2023 covering anomaly detection in ML-based digital threat scoring ensembles, including automated identification of drift behavior in fraud scoring models and intelligent simulation to inform successor model structure. IBM’s 2021 and 2023 US filings on noisy label detection and rectification are applicable to cybersecurity settings where historical anomaly labels may be unreliable. These patents collectively address ensemble replacement workflows for production threat scoring pipelines.
CybersecurityAstronomical & Environmental Discovery
The 2021 literature papers Astronomaly and Active anomaly detection for time-domain discoveries deploy active learning to triage billions of light-curve observations, presenting only high-value anomaly candidates to expert astronomers by iteratively modifying isolation forest weights. The 2020 literature paper on active learning for anomaly detection in environmental data demonstrates that active querying of domain experts for in-situ sensor anomaly labels reduces labeling time while maintaining detection performance comparable to fully labeled datasets.
Scientific DiscoveryTelecom Network & Subsurface Monitoring
The 2022 literature paper Little Help Makes a Big Difference addresses KPI-based anomaly detection in telecom networks where concept drift from network reconfigurations causes false alarm proliferation in unsupervised detectors, showing that active learning operator feedback substantially reduces false alarm rates. Schlumberger/SLB’s active learning framework patents (WO 2020, US 2021, US 2024) target subsurface interpretation where labeled observations are expensive and anomaly quality metrics govern what data enters the training pool.
Network & EnergyLeading Patent Assignees in Active Learning for Anomaly Labeling (Retrieved Records)
In this dataset, Google LLC leads with 5 filings across US, WO, IN, and EP jurisdictions, while Amazon Technologies holds 4 US filings covering both general active labeling services and anomaly-specific feedback-loop systems. Together, Google, Amazon, Schlumberger, and IBM account for roughly half of all patent filings in retrieved records.
Top Assignees by Filing Count in Retrieved Records (Dataset Snapshot)
↗ Click bars to exploreGoogle LLC
Google LLC holds 5 filings in this dataset across US, WO, IN, and EP jurisdictions, filed between 2021 and 2025. The core portfolio centers on active learning via sample consistency assessment, which perturbs unlabeled samples to compute prediction inconsistency values and rank candidates for ground-truth labeling. The 2025 US and EP continuations remain active, indicating ongoing claim expansion in this approach.
United StatesAmazon Technologies
Amazon Technologies holds 4 US filings in this dataset spanning 2021 to 2024, covering an active learning loop-based data labeling service (2021), an augmented manifest labeling service (2022), and a two-patent series on feedback-based training for anomaly detection (2022, 2024). The feedback-based anomaly detection patents capture operator feedback via a GUI ordered by importance ranking and incorporate missed anomaly, proper result, and improper result signals into model retraining, representing a productized closed-loop anomaly labeling pipeline.
United StatesFive Emerging Directions in Active Anomaly Labeling (2022–2026)
The most recent filings in this dataset (2023–2026) signal five converging technical directions: hybrid multi-model discrepancy triggers, graph neural network ensemble architectures, feedback-loop cloud services, noisy label preprocessing, and GAN-assisted class-imbalance mitigation.
Hybrid Classification + Anomaly Model Triggers (2026)
Bentley Systems’ January 2026 US patent proposes running classification and anomaly models in parallel, using consistency between their inferences to boost confidence and inconsistency to trigger additional training data acquisition. This represents a shift from single-model active learning toward multi-model discrepancy-driven labeling triggers. It is the most recent patent filing in this dataset.
Graph Neural Network Ensemble Active Anomaly Detection (2023–2025)
Zhejiang University’s 2023 CN patent trains multiple graph anomaly detection models and selects samples via four strategies—node centrality, node uncertainty, propagation suspicion, and node discriminability—for iterative ensemble improvement. Guangdong University of Technology’s 2023 and 2025 CN filings construct k-nearest-neighbor propagation matrices from autoencoder embeddings to propagate annotations through graphs. Both assignees have 2025 continuations active, indicating sustained R&D investment in graph-propagation architectures.
Uncertainty-Based vs. Graph-Based Active Anomaly Labeling
Click any row to explore further.
| Dimension | Uncertainty / Consistency Sampling | Graph-Based Propagation |
|---|---|---|
| Dimension: Representative Assignees | Google LLC, Schlumberger Technology Corporation | Zhejiang University, Guangdong University of Technology |
| Primary Mechanism | Perturbs samples to compute prediction inconsistency or uncertainty scores for query ranking | Constructs k-NN propagation matrix from autoencoder embeddings; propagates annotations through graph after each labeling step |
| Query Strategy | Inconsistency value ranking; quality metrics from auxiliary inspection components | Node centrality, node uncertainty, propagation suspicion, node discriminability |
| Jurisdiction Focus | US, WO, IN, EP — broad international coverage | CN — concentrated in Chinese academic institution filings |
| Filing Period | 2020–2025 (with active continuations as of 2025) | 2023–2025 (with active continuations as of 2025) |
| Anomaly Specificity | General annotation infrastructure; not anomaly-specific by design | Explicitly targets anomaly scoring via reconstruction error thresholds and graph-based rarity modeling |
| White Space Outside Home Jurisdiction | Broadly covered via PCT/WO and EP continuations | No equivalent PCT or US filings identified in this dataset — potential white space for non-Chinese applicants |
Frequently Asked Questions: Active Learning for Anomaly Labeling
The core mechanism is an iterative loop where a model scores unlabeled data, a query strategy selects the most informative subset (typically uncertain, diverse, or anomaly-prone candidates), a human oracle labels that subset, and the model is retrained with the expanded labeled pool. This selectively queries domain experts to label only the most informative anomalous or borderline samples.
In this dataset, Google LLC leads with 5 filings across US, WO, IN, and EP jurisdictions. Amazon Technologies holds 4 US filings. Schlumberger Technology Corporation holds 3 filings across WO and US. IBM and Sift Science each hold 2 US filings. These counts represent retrieved records only and should not be interpreted as a comprehensive industry ranking.
Retrieved records cover industrial inspection and manufacturing quality control, cybersecurity and digital threat scoring, telecommunications network KPI monitoring, autonomous driving and computer vision annotation, scientific discovery (astronomy and environmental monitoring), subsurface and energy exploration, and engineering asset monitoring.
Graph-based propagation constructs a k-nearest-neighbor propagation matrix from autoencoder embeddings and reconstruction errors, then propagates annotation signals from labeled nodes to unlabeled neighbors. In this dataset, Guangdong University of Technology (CN, 2023 and 2025) and Zhejiang University (CN, 2023 and 2025) are the primary assignees pursuing this approach. No equivalent PCT or US filings were identified in this dataset.
Amazon Technologies’ 2022 US patent (continued in 2024) applies a scoring ML model to an unlabeled dataset, presents results via a GUI ordered by importance ranking, and incorporates operator feedback—including missed anomaly, proper result, and improper result signals—into model retraining. The 2024 continuation expands claim scope toward anomaly-specific GUI feedback capture and multi-metric test evaluation.
The most recent filing in this dataset is Bentley Systems’ January 2026 US patent on integrating machine learning classification models and machine learning anomaly models. It proposes running classification and anomaly models in parallel, using consistency between their inferences to boost confidence and inconsistency to trigger additional training data acquisition—a shift from single-model to multi-model discrepancy-driven labeling triggers.
Data and insights on this page are based on a limited patent and literature dataset and are for reference only. Figures may not represent the complete technology landscape.