Imaging Flow Cytometry Patents: Who Leads, Where the Gaps Are 2026
- 29.0% sits with five assignees. The top five combined account for 226 of the 778 records in scope — concentrated enough that a new entrant's freedom-to-operate review starts with a short list, not a long one.
- Filings peaked in 2019 at 78, then eased. The complete-year comparison (2021 to 2024) shows a 31% decline in annual filings, though 2025–2026 counts are still filling in due to publication lag.
- Image processing classes rival the core assay class. G06T (image data processing) appears on 30.7% of records and G06V (image/video recognition) on 15.8% — the analytics layer is claimed almost as heavily as the fluidics and optics core.
Filing growth compares 2021 (52 records) with 2024 (36) — 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 778 records in scope (CR5), not by the ranked leaders only.
What the imaging flow cytometry patent record shows
Imaging flow cytometry sits at the intersection of fluidic sample handling, optical detection and image-based classification, and the patent record reflects all three layers. Records in scope span 2015 to 2026, with the bulk of activity concentrated in the material-analysis class G01N alongside a substantial secondary cluster in image processing and recognition classes. That split matters for strategy: a filing that only covers the assay chemistry or optical path leaves the downstream classification and feature-extraction methods open to a separate claim, and vice versa.
The assignee ranking covers 100 companies, and the concentration figures show a distinct top tier — five assignees hold 29.0% of the 778 records in scope, rising to 41.8% once the ranking extends to ten. Below that tier the ranking thins quickly into single- and few-filing entities, which is where much of the near-term freedom-to-operate risk and licensing opportunity actually lives.
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Filing trend and technology composition
Two views of the same 778-record dataset: the year-by-year filing count, and the IPC subclasses that show where the claims are concentrated.
Filing activity, 2017–2026
Filings rose from 46 in 2017 to a peak of 78 in 2019, then eased through the early 2020s. The only reliable growth comparison is the complete-year window 2021 (52) to 2024 (36), a 31% decline; 2025 and 2026 are undercounted because publication lags filing by roughly 18 months.
Technology composition by IPC subclass
G01N (material analysis and testing) anchors 60.2% of records, with G06T (image data processing) at 30.7% and G06V (image/video recognition) at 15.8% forming a distinct analytics cluster alongside G02B optics (14.5%) and the C12Q/C12M biological-assay classes (15.2% and 8.2% respectively). Because records can carry multiple classes, these shares are read against the full 778-record base, not against each other.
Shares are the percentage of the 778 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Imaging Flow Cytometry with Eureka
This page is one run against one query. Ask Eureka your own question about imaging flow cytometry and every answer comes back with the patent numbers behind it.
Try EurekaThe most-cited foundational filings
Cell Image Analysis Apparatus, Cell Image Analysis System, Method of Generating Training Data, Method of Generating Trained Model, Training Data Generation Program, and Method of Producing Training Data
A cell image analysis apparatus that can achieve less time and effort for labeling for generation of teaching data than in a conventional example is provided. The cell image analysis apparatus includes an image obtaining unit that obtains a cell image including a removal target that is obtained by a microscope for observation of a cell, a teaching data generator that specifies a removal target region including the removal target within the cell image by performing predetermined image processing and generates as teaching data for machine learning, a label image that represents a location of the removal target region in the cell image, and a training data set generator that generates a set of training data.Filed by Shimadzu, published 2021-01-21 — the claims target automated labeling for machine-learning training sets, a step that most competing filings still handle manually.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US9423353B2 | Apparatus and methods for fluorescence imaging using radiofrequency-multiplexed excitation | 286 |
| 2 | US20140152801A1 | Detecting and Using Light Representative of a Sample | 189 |
| 3 | US20120157160A1 | Compact wide-field fluorescent imaging on a mobile device | 178 |
| 4 | US20120220022A1 | High throughput multichannel reader and uses thereof | 174 |
| 5 | WO2015109323A2 | Systems and methods for three-dimensional imaging | 156 |
| 6 | US20080317325A1 | Detection of circulating tumor cells using imaging flow cytometry | 150 |
| 7 | US20170328826A1 | Fluorescence Imaging Flow Cytometry With Enhanced Image Resolution | 146 |
| 8 | US6463438B1 | Neural network for cell image analysis for identification of abnormal cells | 142 |
| 9 | US20050014201A1 | Interactive transparent individual cells biochip processor | 139 |
| 10 | US6418236B1 | Histological reconstruction and automated image analysis | 136 |
Citation counts inside a searched corpus favour older filings — read this table as a map of influence on later work, not as a ranking of current commercial importance.
Each row carries its publication number; clicking a row searches Eureka by that number.
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Browse MCP servers →What the numbers mean for strategy
Three patterns stand out once the raw counts are read against each other: concentration at the top, a shrinking but not disappearing annual filing rate, and an analytics layer that is claimed almost as densely as the physical instrument.
A narrow leading group, then a long tail
The leading assignee alone accounts for 91 records, and the gap to fifth place (25) is steep. Below tenth place (18), the ranking spreads across many single- and few-filing entities, which is typically where licensing-in opportunities and overlooked prior art both surface.
Complete-year filings have cooled since 2019's peak
The 2021–2024 window is the only comparison that avoids the publication-lag distortion, and it shows a real pullback from 52 to 36 annual filings. Treat 2025–2026 figures as provisional; they will revise upward as publications catch up.
Image processing claims sit alongside the assay itself
Nearly a third of records carry an image-data-processing classification and 15.8% carry image/video recognition — evidence that the classification pipeline, not just the fluidics or optics, is an active claiming target in its own right.
Filing is US-centred with a real European and PCT presence
United States filings (358) lead, followed by EPO (132) and WIPO/PCT (114) routes, with smaller but non-trivial counts in Australia and Germany — a filer weighing a European strategy is not choosing between one route but at least two active ones.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to imaging flow cytometry, with the prior art for and against each one.
Who holds the claim space
The ranked leaders combine large diagnostics and life-sciences instrument makers with genomics-platform entrants; momentum data shows even the most active recent filers slowing rather than accelerating.
A single assignee well ahead of the field
The top-ranked assignee's 91 records put it well clear of fifth place at 25, reflecting a sustained, multi-year filing programme across the assay, optics and analytics layers rather than a single burst of activity.
Even active filers are pulling back
Recent-year momentum data shows the most recently active named assignees filing fewer records year-on-year, including outright drops to zero for several previously active filers — consistent with the broader 2021–2024 decline rather than an isolated event.
Co-filing is limited and mostly university-linked
Only ten co-assignee pairs appear in the dataset, with the strongest pairings running three shared records each — evidence that most patent activity in this field is filed by a single owner rather than through joint ventures or research consortia.
| Assignee | Recent year | YoY |
|---|---|---|
| Becton, Dickinson and Company | 2 | -50% |
| The Regents of the University of California | 0 | -100% |
| Illumina, Inc. | 0 | — |
| AMNIS CORP | 0 | — |
| Recursion Pharmaceuticals, Inc. | 0 | — |
| Life Technologies Corporation | 0 | -100% |
| Cytek Biosciences, Inc. | 0 | — |
| Cytyc Corporation | 0 | — |
Where to take this next
The dataset points to specific follow-up work depending on whether the goal is freedom-to-operate, licensing, or identifying open filing space.
Run a freedom-to-operate check against the top tier
With 29.0% of records held by five assignees, a targeted claim-scope review of that group is more efficient than a broad landscape search before committing engineering resources.
Explore assignee claims in Eureka →Watch the 2021–2024 filing pullback for signs of a turn
A 31% decline over a complete-year window is a real signal, not noise, but 2025–2026 data is still incomplete — re-check this trend once publication lag closes.
Track filing trends in Eureka →Search the analytics layer separately from the assay
With G06T and G06V each covering a meaningful share of records, image-processing and classification claims deserve their own prior-art search rather than being folded into an assay-only review.
Search IPC classes in Eureka →Frequently asked questions
The assignee ranking in this dataset covers 100 companies, with a single leader holding 91 of the 778 records in scope and a steep drop to fifth place at 25. The top five combined account for 29.0% of all records, rising to 41.8% across the top ten, which means the field has a clearly identifiable leading tier rather than an evenly spread set of filers. Below that tier, filing activity spreads across many single- and few-record entities, so a full competitive picture requires looking past the leaders into that longer tail.
Filings peaked in 2019 at 78 and the only reliable complete-year comparison, 2021 to 2024, shows a 31% decline from 52 to 36 annual filings. Figures for 2025 and 2026 are still incomplete because publication typically lags actual filing by around 18 months, so it is too early to call the most recent years a continuation of that decline. The safest reading is that filing activity has cooled from its 2019 peak but the current trajectory is not yet fully visible in the data.
The dominant class is G01N, material analysis and testing, appearing on 60.2% of the 778 records in scope, reflecting the core assay and detection function. Image-processing class G06T (30.7%) and image/video recognition class G06V (15.8%) form a substantial secondary cluster, alongside optics (G02B, 14.5%) and biological assay classes C12Q and C12M. Because a single record can carry multiple IPC classes, these percentages are each measured against the full record count and will sum to more than 100%.
The clearest under-claimed areas relative to the core assay and optics classes are automated training-data labeling workflows, on-chip real-time feature extraction, and data storage or compression schemes built specifically for high-throughput image streams. These branches sit adjacent to heavily claimed territory but show thinner density in the dataset, which typically signals either genuine technical difficulty or an area competitors have not yet prioritized. A first claim there would need to specify the automation step precisely enough to distinguish it from manual-labeling prior art already on file.
US20210019499A1, filed by Shimadzu and published January 2021, claims a cell image analysis apparatus that automates the generation of labeled training data for machine-learning models from microscope-captured cell images. It is a representative filing rather than the single most-cited one, so its blocking effect is narrower than the highest-cited records in this dataset, but it does sit squarely in the automated-labeling sub-area. Anyone building a training-data pipeline for cell image classification should review its specific claim language on the label-image generation step before assuming a workaround is unnecessary.
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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.