Digital Twin Virtual Commissioning Patents: Leaders & White Space 2026
Filing growth compares 2021 (30 records) with 2024 (8) — 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 148 records in scope (CR5), not by the ranked leaders only.
What this landscape covers
This landscape maps 148 published records filed against a search string combining virtual commissioning, digital-twin commissioning and controller virtual commissioning with digital twin, cyber-physical system or virtual asset terminology. The scope spans 2015 through the 2026-07-31 cut-off, capturing the period in which industrial automation vendors moved from physics-based simulation of robot cells toward full digital-twin representations of plants and control logic. Publication lags filing by roughly 18 months, so the most recent filing years in this dataset are understated relative to where activity will eventually settle.
The records concentrate heavily around control-system and software claims rather than a single narrow mechanism, reflecting that virtual commissioning sits at the intersection of PLC/controller logic, physics simulation and enterprise software stacks. Receiving-office activity is led by the United States and the European Patent Office, with meaningful filing volume also routed through the PCT, Germany and India.
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Filing trend and technology composition
Two views of the same 148-record dataset: how filing activity has moved year over year, and which IPC subclasses carry the claims.
A sharp rise, a 2022 peak, then a real pullback
Filings rose from 10 in 2017 toward a peak of 37 in 2022, then declined to 8 by 2024 — a 73% fall over the 2021-2024 span. Treat 2025 and 2026 figures as provisional given publication lag; they are not evidence of a further slowdown, only of incomplete data.
Control systems and data processing carry the field
G05B (control & regulating systems) appears in 57.4% of the 148 records and G06F (electric digital data processing) in 45.9%, confirming that most claims are anchored in controller logic and software architecture rather than mechanical structure. Robotics-specific claims (B25J, 14.9%) and AI-model claims (G06N, 8.8%) are present but far less saturated, and a record can carry more than one class so these shares overlap.
Shares are the percentage of the 148 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Digital Twins — Digital-Twin Virtual Commissioning Patent Landscape with Eureka
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Try EurekaThe most-cited records in the corpus
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US9811074B1 | Optimization of robot control programs in physics-based simulated environment | 200 |
| 2 | US9671777B1 | Training robots to execute actions in physics-based virtual environment | 144 |
| 3 | US20210248289A1 | Digital platform using cyber-physical twin structures providing an evolving digital representation of a risk-… | 77 |
| 4 | US20210138651A1 | Robotic digital twin control with industrial context simulation | 63 |
| 5 | US20140207774A1 | Virtual Building Browser Systems and Methods | 60 |
| 6 | US20240046001A1 | Automated standardized location digital twin and location digital twin method factoring in dynamic data at di… | 38 |
| 7 | WO2023079139A1 | Automated standardized location digital twin and location digital twin method factoring in dynamic data at di… | 34 |
| 8 | US20170108834A1 | Human programming interfaces for machine-human interfaces | 26 |
| 9 | US20220156433A1 | Industrial network communication emulation | 23 |
| 10 | US20210141870A1 | Creation of a digital twin from a mechanical model | 23 |
Citation counts reflect influence within this searched corpus and skew toward older filings; they are not a measure of current commercial importance.
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Browse MCP servers →What the numbers mean for filing strategy
Three read-outs from the concentration, trend and technology data that matter for anyone deciding where to file or challenge.
A small group holds most of the space
With the leader alone holding 52 records and the top 5 combined controlling 124 of 148, new entrants are filing into a field where a handful of assignees already occupy the core control and simulation claims. Differentiation has to come from adjacent or combined claim territory, not head-on overlap.
Post-peak pullback, not necessarily a dying field
The 2022 peak of 37 records followed a run-up from 10 in 2017; the drop to 8 by 2024 is real but the 2025-26 figures are still incomplete due to publication lag. Read this as a maturing filing cycle around the core mechanism, not a verdict on commercial relevance.
AI-model claims are still thin relative to control claims
G05B and G06F between them anchor most filings, but G06N coverage sits well below half that level. Combining learned models with virtual-commissioning control loops is one of the least claim-dense intersections in this dataset.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to digital twins — digital-twin virtual commissioning patent landscape, with the prior art for and against each one.
Where to take this next
The dataset points to a concentrated core and a thinner set of adjacent branches. Two directions are worth pursuing before committing filing budget.
Map freedom-to-operate against the top holders
With 83.8% of records held by five assignees, a targeted claim-chart review of that group's core control and simulation patents should come before any new filing in this space.
Run a freedom-to-operate check in Eureka →Test the under-claimed intersections
AI-model classes and business-process integration both sit well below the control and software baseline; a first claim combining either with virtual commissioning has more room to stand alone.
Explore white space in Eureka →Common questions about this landscape
Virtual commissioning uses a digital twin of a machine, cell or plant to validate control logic before it ever touches physical hardware, catching timing and logic errors in simulation rather than on the factory floor. In patent terms, the claims in this landscape cluster around control-system logic (IPC class G05B) and software/data-processing implementation (G06F), with robotics-specific and AI-augmented variants appearing less frequently. The technique sits at the intersection of industrial automation, physics-based simulation and enterprise software, which is why filings span such a wide IPC spread.
The dataset of 148 records ranks 40 assignees, and the field is heavily concentrated: the top 5 hold 124 records, or 83.8% of everything in scope, with the single leading assignee alone accounting for 52 records. Filing volume drops sharply after that — fifth place holds only 3 records and tenth place just 2 — so most of the ranked assignees are narrow, single-digit filers. Any competitive review should focus first on the handful of names at the top of that ranking rather than the long tail.
Filings rose from 10 records in 2017 to a peak of 37 in 2022, then fell to 8 by 2024 — a 73% decline over that three-year span, using only years that are complete. Because publication typically lags filing by around 18 months, the 2025 and 2026 figures in this dataset are still incomplete and should not be read as continued decline; they simply have not finished filling in yet. The honest read is a filing cycle that has passed its peak intensity around the core mechanism, with the true current trajectory not yet visible in the data.
The most-cited record in this corpus, US9811074B1, covers optimisation of robot control programs inside a physics-based simulated environment and carries 200 citations within the searched set, making it a foundational reference point for later filings. Its companion record, US9671777B1, covering training robots to execute actions in a physics-based virtual environment, carries 144 citations. Designing around either typically means moving the innovation into a different layer of the stack — enterprise integration, AI-model-driven validation, or non-electric process variables — rather than restating the same simulation-to-control pipeline.
Relative to the 57.4% and 45.9% coverage of the two dominant control and software classes, AI-model claims (G06N, 8.8%), image-based twin generation (G06T, 9.5%) and non-electric process-variable control (G05D, 6.1%) all sit well below the core density. Business-process integration (G06Q, 11.5%) is also comparatively open given how central enterprise reporting is becoming to industrial digital twins. A first claim combining any of these lower-density classes with the core virtual-commissioning mechanism has meaningfully more room than one restating existing control-loop simulation claims.
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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.