IIoT Anomaly Detection Patents: Leaders & White Space 2026
Top-5 share is the combined record count of the five largest assignees divided by all 37 records in scope (CR5), not by the ranked leaders only.
What the IIoT anomaly detection patent record actually shows
Industrial IoT anomaly detection sits at the intersection of sensor networks, digital transmission protocols and machine-learning inference. The 37 records in scope span filings from 2015 through the middle of 2026, with the earliest activity only appearing from 2017 onward. This is a young, still-forming filing pattern rather than an established one with decades of prior art to search against. Most records carry claims tied to AI-model computing (IPC G06N) alongside digital information transmission (H04L) and control and regulating systems (G05B), which together frame anomaly detection as a data-pipeline problem: sense, transmit, infer.
Because publication typically lags filing by around 18 months, the 2025 and 2026 figures in this dataset understate true filing activity for those years; the visible peak at 2025 is a floor, not a ceiling. Family-level counting is used throughout so that continuation filings and multi-jurisdiction copies of the same invention are not double-counted, giving a fairer read of how many distinct inventions are actually in play.
Filing trend, assignee concentration and technology mix
Three views of the same 37 records: when filings happened, who filed them, and what technical ground they claim.
A pattern with no history before 2017
Filings rose from zero in 2017 to a peak of 13 in 2025, the most recent complete-enough year in the dataset. With fewer than four full years of post-lag data available, no growth rate can be responsibly stated; the honest read is that activity is concentrated almost entirely in the last two to three years, and the 2026 count will keep rising as later publications land.
AI-model claims dominate the technology mix
G06N (AI-model computing) appears in 73.0% of the 37 records, ahead of H04L (54.1%, transmission) and G05B (45.9%, control systems). Because a single record can carry several IPC classes, these shares add to more than 100% of the record total — that overlap itself is informative: most inventions here combine a machine-learning inference layer with a transmission or control layer rather than claiming either alone.
Shares are the percentage of the 37 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
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Try EurekaThe most-cited records and a representative recent filing
Encryption retransmission IIoT device for providing resiliency against attacks (US12563022B2)
The claimed device pairs a sensor generating raw data with a processing unit that turns it into analytics data, then routes that analytics data through a network switch to one of several connected encryption retransmission devices. Each retransmission device encrypts the egressing packet before it leaves the network switch, and the switch itself is responsible for identifying which external device and which retransmission path a given packet should use.Abstract condensed from the granted claim language; full claim scope is rendered separately.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | WO2024155584A1 | Systems, methods, devices, and platforms for industrial internet of things | 95 |
| 2 | US20190297101A1 | Blockchain for securing distributed IIOT or edge device data at rest | 43 |
| 3 | CA3252125A1 | Systems, methods, devices, and platforms for industrial internet of things | 9 |
| 4 | US10819722B2 | Blockchain for securing distributed IIoT or edge device data at rest | 5 |
| 5 | EP4652552A1 | Systems, methods, devices, and platforms for industrial internet of things | 2 |
Citation counts reward older records simply for having more time to accumulate citations inside this searched corpus — read them as a signal of past influence on the field's framing, not as a ranking of current technical importance.
Publication numbers are shown where the record carries one (5 of 5 rows); clicking a row searches Eureka by that number.
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Four figures from this dataset carry direct implications for where to file, who to watch, and which technical framing is already crowded.
The top of the ranking is thin, not dominant
With the leader holding 5 families and fifth place holding only 2, no single assignee has locked up the field. The top 10 combined reach 51.4% of all 37 records, meaning close to half the dataset is held by entities appearing once or twice — academic groups, individual inventors and early-stage filers rather than incumbents defending a moat.
This is a post-2017 filing pattern
There is no prior art to search before 2017 in this scope, and the 13 filings recorded in 2025 mark the current peak. Because publication lags filing by roughly 18 months, 2025 and 2026 counts will continue to rise as more applications publish — treat the recent years as understated rather than as a plateau.
Anomaly detection is claimed as an ML problem first
G06N outranks both H04L (54.1%) and G05B (45.9%), and the three classes overlap heavily within individual records. Claim drafting that frames detection purely as a control-systems or signal-processing exercise, without an inference layer, is now the minority approach in this dataset.
Filing activity skews heavily toward one receiving office
India accounts for the large majority of receiving-office filings in this dataset, with the United States a distant second at 5 and other jurisdictions (Australia, Canada, Germany, EPO) each in single digits. A landscape this jurisdiction-concentrated changes where a competitive-intelligence team should monitor first.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to industrial iot: iiot anomaly detection patent landscape, with the prior art for and against each one.
Where to take this analysis next
The dataset points to specific follow-up questions rather than a finished picture; these are the natural next moves for an R&D or IP team acting on it.
Map the co-filing clusters
The 10 identified co-assignee pairs, concentrated around a small set of academic co-inventors, suggest emerging research clusters worth tracking before they mature into commercial filings.
Explore co-filing patterns in EurekaWatch the under-claimed IPC branches
Classes like G06Q, A61P, A61Q and B23Q each sit at or below 13.5% of records, suggesting adjacent applications of anomaly detection are barely claimed yet.
Run a white-space search in EurekaRe-check the trend after publication catches up
Because 2025 and 2026 counts are understated by publication lag, revisit the filing trend in a future data pull before drawing conclusions about whether the field has peaked.
Set a monitoring alert in EurekaFrequently asked questions about IIoT anomaly detection patents
This dataset identifies 37 published patent records in scope, spanning filings from 2015 through mid-2026 with actual activity beginning in 2017. The assignee ranking behind this figure covers 97 companies and individual filers, counted by patent family so continuations and multi-jurisdiction copies are not double-counted. Because publication lags filing by roughly 18 months, the true count for 2025-2026 filings is understated in any dataset pulled today.
No single assignee dominates: the top-ranked filer holds 5 families, and the top 5 combined account for 37.8% of all 37 records in scope. The top 10 combined reach 51.4%, meaning roughly half of all activity sits with entities that appear only once or twice, including academic institutions and individual inventors alongside industrial players. This is a field with a thin leadership layer rather than an entrenched incumbent.
The dominant IPC classification is G06N, covering AI-model-based computing, present in 73.0% of the 37 records, followed by H04L (digital information transmission, 54.1%) and G05B (control and regulating systems, 45.9%). Because records often carry multiple IPC codes, these percentages overlap rather than sum to 100%, and the overlap itself shows most inventions combine a sensing or transmission layer with a machine-learning inference layer. Smaller classes such as G06Q, A61P, A61Q and B23Q each cover a much smaller slice, pointing to specific niche applications rather than a broad secondary cluster.
There is no filing activity recorded before 2017 in this dataset, and activity rose to a peak of 13 records in 2025, the highest year observed so far. Because there are fewer than four complete years of post-lag data, no reliable growth rate can be calculated from this dataset. What can be said is that the field is recent and still accumulating filings, with 2026 figures certain to rise as later publications appear.
India is the leading receiving office by a wide margin, with 25 filings recorded, compared with 5 for the United States and single-digit counts for Australia, Canada, Germany and the EPO. This concentration is unusual compared with many industrial-IoT subfields and suggests that a substantial share of current invention activity, including academic and individual filings, is originating from or being routed through Indian patent offices. Teams monitoring this space should not assume US or European filings are representative of the whole field.
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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.