AI Watermark Detection Patents: Who Leads, Where Gaps Are 2026
Filing growth compares 2021 (343 records) with 2024 (452) — a three-year span. 2024 is the most recent year we treat as complete: publication lags filing by roughly 18 months, so 2025 onwards are still filling in and any growth rate that ends there would understate the field. Top-5 share is the combined record count of the five largest assignees divided by all 4,029 records in scope (CR5), not by the ranked leaders only.
What the AI watermark detection patent record shows
AI watermark detection covers methods for embedding, extracting and verifying signals that mark content or model outputs as machine-generated or as originating from a specific system. The 4,029 records in scope span 2015 through mid-2026, with publication lag meaning the most recent one to two years understate real filing activity. The field sits at the intersection of older digital watermarking practice — much of it originally built for media and supply-chain provenance — and newer claims aimed specifically at AI model outputs and generative content.
Filing offices skew heavily toward the United States, with meaningful activity at the EPO, in Australia, through WIPO's PCT route, and in India and Canada. That distribution suggests the technology is being protected primarily as a US-anchored asset class with selective international filing rather than a globally uniform strategy.
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Filing trends and technology composition
Two views of the same 4,029-record dataset: how filing volume has moved year over year, and which IPC subclasses carry the claim weight.
Filing trend, 2017-2026
Filings climbed from 111 in 2017 to a peak of 452 in 2024, with 2021-to-2024 growth of 32%. 2025 and 2026 figures (down to 88 by 2026) reflect publication lag rather than a genuine slowdown — treat only 2024 as the last complete year.
IPC subclass composition
G06F (37.8% of records) and H04N (21.0%) lead, followed by H04L (18.9%), G06V and G06T (15.0% each), G06K (14.1%), G06Q (13.9%) and G06N (10.7%). Because records carry multiple classes, these shares sum to more than 100% of the 4,029 records in scope.
Shares are the percentage of the 4,029 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
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Try EurekaRepresentative filing and most-cited prior art
System and method for AI model watermarking (US20220300842A1, Huawei Cloud Computing Technologies)
Filed by Huawei Cloud Computing Technologies and published 2022-09-22, this record describes watermarking prediction outputs from a first AI model so that a second, distilled model can later be detected as derived from those outputs. The mechanism inserts a periodic watermark signal into the probability values the model returns, rather than into the underlying training data or model weights.Detection here works on output-side probability signals, not on model internals — a distinction that matters for anyone trying to design around it.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US6850252B1 | Intelligent electronic appliance system and method | 4,059 |
| 2 | US20160026253A1 | Methods and systems for creating virtual and augmented reality | 3,440 |
| 3 | US20090254572A1 | Digital information infrastructure and method | 2,827 |
| 4 | US6606744B1 | Providing collaborative installation management in a network-based supply chain environment | 2,512 |
| 5 | US20100250497A1 | Electromagnetic pulse (EMP) hardened information infrastructure with extractor, cloud dispersal, secure stora… | 1,777 |
| 6 | US20070053513A1 | Intelligent electronic appliance system and method | 1,452 |
| 7 | US20060178918A1 | Technology sharing during demand and supply planning in a network-based supply chain environment | 1,304 |
| 8 | US20040064351A1 | Increased visibility during order management in a network-based supply chain environment | 1,295 |
| 9 | US7124101B1 | Asset tracking in a network-based supply chain environment | 1,278 |
| 10 | US7813822B1 | Intelligent electronic appliance system and method | 1,198 |
Citation counts favour older filings simply because they have had longer to accumulate citations inside this corpus — read them as a signal of influence on the field, not as a ranking of current technical importance.
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Three readings of the same dataset, aimed at where to file, where to watch, and where claim space is genuinely open.
Leadership exists but the field is not locked up
The top 5 assignees account for 16.7% of all 4,029 records and the top 10 for 23.7%. That leaves roughly three-quarters of filings spread across a long tail of the remaining 90 ranked companies and unranked filers — a structure that rewards a well-targeted claim more than it punishes a late entrant.
Momentum is real, not a blip
Filings grew from 343 in 2021 to 452 in 2024, the last year that can be treated as complete given publication lag. That three-year trajectory signals active investment in the space rather than a technology cooling off, even though the raw 2025-2026 counts look lower on paper.
Most claims still route through general data processing, not AI-native classes
G06F (electric digital data processing) covers 37.8% of records, far ahead of G06N (computing based on AI models) at 10.7%. That gap suggests many watermarking claims are still framed in general computing or image-processing terms rather than as AI-model-specific technique, leaving room for sharper AI-native claim drafting.
US-anchored protection with selective international reach
The United States receives far more filings than any other office, with the EPO, Australia, WIPO's PCT route, India and Canada trailing well behind. Strategies built only around US filing risk leaving the rest of the world's claim space to whoever files there next.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to ai safety, evaluation & assurance: ai watermark detection patent landscape, with the prior art for and against each one.
| Assignee | Co-assignee | Shared families |
|---|---|---|
| Silverbrook Research Pty Ltd | SILVEBROOK KIA | 4 |
| Digimarc Corp | RODRIGUEZ TONY F | 2 |
| Digimarc Corp | RHOADS GEOFFREY B | 2 |
| Digimarc Corp | DAVIS BRUCE L | 2 |
| Digimarc Corp | CONWELL WILLIAM Y | 2 |
| Citrix Systems Inc | YAO YAJUN | 2 |
| Citrix Systems Inc | YAO PENG | 2 |
| Citrix Systems Inc | XIAO TIANYU | 2 |
Co-assignee pairings are limited — only 10 pairs recorded, most tied to legacy digital-watermarking assignees rather than newer AI-model-specific filers, indicating that collaborative filing is not yet a common structure in this space.
Where to take this analysis
The dataset points to specific next steps depending on whether the goal is defensive clearance, offensive filing, or competitive tracking.
Map claim language against the leading assignees
With the top 10 holding under a quarter of records, a freedom-to-operate review needs to look past the named leaders into the long tail before assuming a route is clear.
Explore assignees in EurekaPressure-test AI-native claim framing
The low G06N share relative to G06F suggests claims written specifically around AI model behaviour, rather than general data processing, may face thinner prior art.
Run a claim comparison in EurekaTrack filings outside the US
With the US dominating receiving offices, monitoring EPO, PCT and India filings closely can surface competitive moves before they reach the US docket.
Set up jurisdiction alerts in EurekaCommon questions about AI watermark detection patents
This dataset covers 4,029 published records filed or published between 2015 and mid-2026, drawn from a search combining AI, watermark and detection terms. That figure counts individual published records rather than consolidated patent families, so the underlying number of distinct inventions is smaller. Filing activity grew substantially over the period, reaching a peak of 452 in 2024, the most recent year that can be treated as complete given typical publication lag.
The assignee ranking covers 100 companies, with the leader holding 267 records and the fifth-ranked company holding 73. The top 5 assignees combined account for 16.7% of all 4,029 records, and the top 10 account for 23.7%, meaning the majority of filings come from outside the most active companies. Names in this space include established digital-watermarking specialists alongside large technology and telecommunications firms, reflecting the field's roots in media provenance as much as its newer AI-specific applications.
Filings increased from 343 in 2021 to 452 in 2024, a 32% rise over that three-year span, indicating genuine growth rather than a plateau. Counts for 2025 and 2026 appear lower in the raw data, but that reflects the roughly 18-month lag between filing and publication rather than an actual slowdown. Only years through 2024 should be read as a complete picture of filing behaviour.
General electric digital data processing (IPC class G06F) appears in 37.8% of the 4,029 records, the largest single share, followed by pictorial communication and video technology (H04N) at 21.0% and digital information transmission (H04L) at 18.9%. Classes specific to image and video recognition, image data processing, and AI model computation (G06N) each appear in a smaller share of records, with G06N at 10.7%. Because records can carry multiple IPC classes, these figures overlap rather than sum to a whole.
The comparatively low 10.7% share for G06N, the IPC class covering AI model computation directly, against the 37.8% share for general data processing classes suggests many existing claims are framed around broader digital or image processing rather than AI-model-specific mechanisms. Filing office data also shows heavy concentration in the United States relative to Europe, Australia, the PCT route, India and Canada, which points to under-filed jurisdictions for companies willing to file internationally. Reviewing recent representative filings, such as output-probability watermarking approaches, can help identify which specific technical mechanisms remain lightly claimed.
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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.
Machine translation. Assignee and organisation names originally recorded in Chinese, Japanese or Korean have been rendered into English by an AI translation step so that the tables stay readable. These renderings are best-effort and may not match a company’s registered English name; the original name is what the underlying patent record carries, and it is what any Eureka query launched from this page uses.