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Run your analysis now →A patent landscape review of single-cell data batch effect integration algorithms: filing trends, IPC composition, leading assignees and where claim space remains open.
Filing growth = 2021 (1 records) → 2024 (6); 2024 is the last year we treat as complete. Top-5 share = the 5 largest assignees ÷ all 18 records in scope (CR5), not the ranked leaders only.
This landscape tracks patent families where the claims combine single-cell data, batch effect or batch correction, and an integration or algorithmic method — the technical core of tools that reconcile single-cell datasets collected across instruments, labs or timepoints. The scope is narrow by design: 18 records worldwide meet this exact combination, which makes the field legible enough to read assignee by assignee rather than only in aggregate.
Because the corpus is small, a handful of filings can move the concentration figures noticeably, and any single well-drafted application can occupy a meaningful share of the claim space that exists today.
Pick a task. Every answer cites the patents behind it.
Two views of the same 18 records: how filing activity has moved year over year, and which IPC subclasses the claims sit under.
Annual filings rose from 1 in 2021 to a peak of 6 in 2024 — a +500% increase over that three-year span. 2025 and 2026 show fewer published records, but that reflects the roughly 18-month lag between filing and publication rather than a slowdown; 2024 remains the last year that can be read as complete.
Publication lags filing by roughly 18 months, so 2025 onwards are still filling in. Growth rates on this page therefore end at 2024; running them to the last bar would understate the field.
G16B (Bioinformatics) appears on 88.9% of the 18 records, with G06F and G06N — general data processing and AI-based computing — each on 50.0%. Classes tied to physical measurement or imaging (C12Q, G01N, G02B, G06T, G06V) each cover 11.1% or less, since a record can carry multiple classes these shares add up to more than 100%.
Shares are the percentage of the 18 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
This page is one run against one query. Ask Eureka your own question about single cell data batch effect integration algorithm patent landscape and every answer comes back with the patent numbers behind it.
Try EurekaIntroduced here is a module for converting single-cell data sets to transformed bulk-cell datasets for use in augmenting single-cell data, identifying cell type subsets, and in assessing factors associated with diseases. Such cell type subsets include immune cell subsets.Filed by Verily Life Sciences; published as WO2025064862A1.


When you want the answer in the next five minutes.
The agent works the prompt against patents and technical literature, citing every source.
Run your analysis now →When it has to run inside your own pipeline.
Patent search, landscape analysis and assignee resolution as MCP tools. Drop them into any agent framework, or call REST directly.
Browse MCP servers →Three readings of the same 18-record dataset, each pointing to a different practical question.
The leading assignee holds only 2 of the 18 records in scope, and the top 5 combined account for 33.3% of all records. That is a fragmented field, not one guarded by a dominant filer — there is room to file without immediately colliding with an entrenched leader.
Filings went from 1 in 2021 to 6 in 2024, a five-fold increase inside three years. That trajectory marks single-cell batch correction as a technical area that moved from occasional filing to active claim-staking within a short window.
Nearly nine in ten records sit under G16B, with G06F and G06N each present on half the records — the claim language is overwhelmingly computational. Classes tied to optics, imaging or recognition barely register, each under 6% of records.
China accounts for 15 of the 18 records, with WIPO/PCT and India each contributing the remainder. A freedom-to-operate review outside China should not assume the same density of prior art applies.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to single cell data batch effect integration algorithm patent landscape, with the prior art for and against each one.
The dataset points to specific follow-up work rather than a single conclusion.
With no assignee holding more than 2 of 18 records, a family-by-family review of the ranked leaders is more useful than reading the field as a two-horse race.
Explore the assignee ranking in EurekaThe +500% run to 2024 is the clearest trend line in this dataset; treat 2025-2026 figures as provisional until publication catches up.
Track filing trends in EurekaG01N, G02B, G06T and G06V each cover 11.1% or less of records, suggesting the computational core is well-claimed but adjacent measurement and imaging integrations are not.
Run a white space search in EurekaThe dataset covers 18 patent families in total, and the field is fragmented rather than dominated: the leading assignee holds only 2 of the 18 records. The top 5 assignees combined account for 33.3% of all records, and the top 10 reach 61.1%. This means there is no single gatekeeper controlling the core claim space, though a longer tail of single-filing entrants makes up the rest of the field.
Filings rose from 1 in 2021 to a peak of 6 in 2024, a +500% increase over that three-year span. That is the clearest growth signal in the dataset. Figures for 2025 and 2026 appear lower, but that reflects the roughly 18-month lag between filing a patent and its publication, not an actual slowdown in activity.
Almost nine in ten records, 88.9% of the 18 in scope, are classified under G16B, the bioinformatics subclass, confirming that most claims are computational methods applied to biological data. G06F (general digital data processing) and G06N (AI-based computing) each appear on 50.0% of records, often alongside G16B on the same filing. Classes tied to physical measurement or imaging, such as C12Q, G01N, G02B, G06T and G06V, each cover 11.1% of records or less, marking those as comparatively open areas.
The heaviest concentration of both filings and IPC classification sits in China, which accounts for 15 of the 18 records, with the computational core (G16B, G06F, G06N) most densely claimed. Areas combining single-cell integration with optical systems, image processing or recognition (G02B, G06T, G06V) show only one record each in this dataset, suggesting comparatively thin prior art. A freedom-to-operate search should still be run jurisdiction by jurisdiction rather than assuming uniform density.
WO2025064862A1 describes a module for converting single-cell datasets into transformed bulk-cell datasets, used to augment single-cell data, identify cell type subsets including immune cell subsets, and assess disease-associated factors. It was published on 2025-03-27 and represents one of the more recent filings in this 18-record dataset. Anyone building tools that convert or reconcile single-cell outputs into bulk-equivalent formats should review its specific claim scope before designing similar conversion modules.
Go past this page: query the whole single cell data batch effect integration algorithm patent landscape corpus yourself, in your own scope.
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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.
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