Soft-Sensor Bioprocessing Patents: Who Leads, Where Gaps Are 2026
- 57.5% of all 160 records sit with just five assignees — concentration is real, but a long tail of 33 more ranked companies still files.
- Filings grew 16% from 2021 to 2024 (32 to 37, the last year with a complete publication window), peaking at 37 before 2025-26 data starts filling in.
- G06N covers 79.4% of records but wireless (H04W, 13.8%) and image recognition (G06V, 10.0%) show the update-loop is still spreading into new signal paths.
Filing growth compares 2021 (32 records) with 2024 (37) — 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 160 records in scope (CR5), not by the ranked leaders only.
What this landscape covers
This dataset tracks patent filings at the intersection of soft, virtual and inferential sensors and the mechanisms that keep their underlying models current: online learning, drift detection, recursive estimation, transfer learning and update-interval control. The IPC scope centres on control systems (G05B13/04), bioprocess-adjacent classification (C12Q3/00) and machine-learning architectures (G06N20/00), which is why the composition skews heavily toward AI-model classes even though the search string starts from sensor terminology.
160 published records fall within scope from 2015 through the 2026-07-31 cut-off, with 38 assignees appearing in the ranking. Because publication trails filing by roughly 18 months, the 2025 and 2026 figures in any trend chart are still incomplete and should not be read as a slowdown.
Filing trend and technology composition
Two views of the same 160-record set: how filing activity has moved year over year, and which IPC subclasses the claims actually sit in. Because a single record can carry several IPC codes, the composition shares add up to well over 100% of the record total.
Filing trend, 2017-2026
Filings ran from 5 in 2017 to a peak of 37 in 2024, with 2021's 32 filings growing 16% to reach that 2024 peak. 2025 and 2026 show fewer records (down to 3 by 2026), which reflects publication lag rather than a real drop in filing activity.
IPC subclass composition
G06N (AI-model computing) appears in 79.4% of the 160 records, far ahead of G05B control systems (40.0%) and G06F data processing (39.4%). Business/admin processing (G06Q, 30.0%) and digital transmission (H04L, 27.5%) show the update-loop increasingly touching commercial and networked infrastructure, while wireless (H04W, 13.8%) and vision-based classes (G06V 10.0%, G06K 8.8%) remain comparatively thin.
Shares are the percentage of the 160 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Soft-Sensor Bioprocessing Adaptive Updating with Eureka
This page is one run against one query. Ask Eureka your own question about soft-sensor bioprocessing adaptive updating and every answer comes back with the patent numbers behind it.
Try EurekaRepresentative filing and most-cited records
Enhanced process and hardware architecture to detect and correct realtime product substrates (US20220122865A1)
A semiconductor processing tool integrates witness sensors with a chamber and feeds their data to a dedicated drift detection module, which outputs to a dashboard for real-time monitoring and correction of process substrates.Filed by Applied Materials; published 2022-04-21.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20210157312A1 | Intelligent vibration digital twin systems and methods for industrial environments | 715 |
| 2 | US20190303759A1 | Training, testing, and verifying autonomous machines using simulated environments | 330 |
| 3 | US20090198350A1 | Robust adaptive model predictive controller with tuning to compensate for model mismatch | 291 |
| 4 | US20200327371A1 | Intelligent Edge Computing Platform with Machine Learning Capability | 286 |
| 5 | US20210248514A1 | Artificial intelligence selection and configuration | 279 |
| 6 | US20150134123A1 | Predictive monitoring and control of an environment using cfd | 156 |
| 7 | US20180262525A1 | Multi-modal, multi-disciplinary feature discovery to detect cyber threats in electric power grid | 140 |
| 8 | WO2024155584A1 | Systems, methods, devices, and platforms for industrial internet of things | 95 |
| 9 | WO2022133210A3 | Market orchestration system for facilitating electronic marketplace transactions | 71 |
| 10 | US20140309756A1 | System, Method and Apparatus for Determining Properties of Product or Process Streams | 66 |
Citation counts favour older filings that have had more time to accumulate citations inside the searched corpus — read them as a signal of influence, not of which route is currently winning.
Each row carries its publication number; clicking a row searches Eureka by that number.
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Browse MCP servers →What the filing pattern tells you
The numbers point to a field where a handful of large industrial and software players have staked out core AI-model claims, while sensor-specific and bioprocess-specific applications remain comparatively open.
The top of the field is genuinely concentrated
Five assignees account for 92 of the 160 records in scope, and the top ten extend that to 74.4% (119 records). The leader alone holds 26 records against a fifth-place figure of 9, so the drop-off from first place is steep even within the top tier.
Growth through the last complete year
Filings rose from 32 in 2021 to a peak of 37 in 2024, a 16% increase over that span. Several leading assignees show flat or declining activity in the most recent (partial) year, which is consistent with publication lag rather than a genuine pullback.
AI-model claims dominate the IPC mix
Nearly four in five records carry a G06N classification, reflecting how much of the adaptive-updating mechanism (online learning, drift detection, retraining triggers) is claimed as a machine-learning method rather than as a sensor hardware innovation.
Filing is US-centred with a real PCT and EPO presence
The United States receives the largest share of filings at 62, with Europe (EPO) and WIPO/PCT each at 25. Australia (17), Singapore (8) and Canada (7) round out a filing footprint that suggests applicants are pursuing multi-jurisdiction protection rather than filing domestically only.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to soft-sensor bioprocessing adaptive updating, with the prior art for and against each one.
Who is filing, and where the field is still open
The ranked assignee list spans large industrial-control incumbents, AI-infrastructure firms and narrower specialists. Momentum data shows even leading assignees cooling in the most recent (partial) year, which is expected given publication lag rather than a sign of retreat.
A clear leader, then a steep drop
The top-ranked assignee holds 26 of the 160 records in scope, well ahead of the fifth-place figure of 9 and the tenth-place figure of 3. That gap is the clearest sign that core claim territory near the top is already occupied.
Recent-year activity is cooling across the board
Several of the most active assignees show flat or negative year-on-year momentum in the latest year, including a -33% figure for one leading filer and -100% for others whose latest-year count fell to zero. Given the roughly 18-month publication lag, this reads as incomplete data rather than a genuine slowdown.
A long tail keeps entering the field
Beyond the five assignees holding 57.5% of records, another 33 ranked companies are filing in smaller numbers, spanning industrial automation, process control and AI-infrastructure vendors. That breadth suggests the underlying techniques are being adapted across more than one industry vertical.
| Assignee | Recent year | YoY |
|---|---|---|
| STRONG FORCE TX PORTFOLIO 2018 LLC | 2 | -33% |
| Strong Force IoT Portfolio 2016 LLC | 0 | -100% |
| ISTARI DIGITAL INC | 0 | -100% |
| NVIDIA Corporation | 0 | -100% |
| General Electric Company | 0 | — |
| Fog Corner Systems Inc | 0 | — |
| Fisher-Rosemount Systems, Inc. | 0 | — |
| Solution Seeker AS | 0 | — |
Where to take this next
The landscape data narrows the search; the next step is testing a specific claim direction against it.
Map a freedom-to-operate question against the top assignees
If a design concept touches drift detection or recursive model updating, checking it against the claims held by the top five assignees first is the fastest way to find out whether it is already blocked.
Run a claim comparison in Eureka →Track momentum in the under-claimed branches
Wireless update scheduling and vision-based calibration show thinner coverage than the core AI-model classes — worth monitoring as filing activity there develops.
Set up a monitoring alert in Eureka →Common questions about this landscape
A soft sensor (also called a virtual or inferential sensor) is a model that estimates a process variable from other measured signals rather than measuring it directly. In this dataset, the scope is narrowed to soft sensors paired with mechanisms for keeping the underlying model current, such as online learning, drift detection, recursive estimation, transfer learning or explicit update-interval control. That combination is what separates this landscape from a broader soft-sensor search: the emphasis is on how the model adapts after deployment, not just on the initial estimation method.
The ranking covers 38 assignees, and it is genuinely top-heavy: the top five hold 57.5% of the 160 records in scope, and the top ten hold 74.4%. The leading assignee alone accounts for 26 records, well ahead of the fifth-place figure of 9. Beyond that top tier, though, a long tail of companies files in smaller numbers, spanning industrial control, AI infrastructure and process-specific specialists, so the field is not a two-company duopoly.
Filings grew from 32 in 2021 to a peak of 37 in 2024, a 16% increase, and 2024 is the most recent year with a reasonably complete publication record. The apparent drop-off in 2025 and 2026 is a data artefact: patent publication typically lags filing by around 18 months, so recent years are always undercounted until later filings work through the pipeline. Reading the 2025-26 dip as a real slowdown would be a mistake based on this dataset alone.
G06N, the classification for AI-model computing, appears in 79.4% of the 160 records, making it by far the dominant class. Control and regulating systems (G05B) and general data processing (G06F) each cover roughly 40% of records, and business/admin data processing (G06Q) and digital transmission (H04L) both exceed a quarter of the dataset. Because records often carry multiple IPC codes, these shares overlap rather than sum to 100%, which reflects how often adaptive-updating claims combine a machine-learning method with a control or data-processing context.
The composition data points to comparatively thin coverage in vision-based soft-sensor calibration (G06V, 10.0% of records) and wireless sensor-network update scheduling (H04W, 13.8%), both well below the 79.4% carried by core AI-model claims. Bioprocess-specific drift-threshold methods and cross-batch transfer learning also appear less saturated than the general industrial-control claims held by the top assignees. Any of these directions still needs a freedom-to-operate check against the leading assignees' existing claims before committing to a filing strategy.
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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.