Patient-Ventilator Synchrony Patents: Who Leads, Where Gaps Are 2026
- 100% of records sit with three assignees. Every one of the 21 records in scope traces to the ranked leader group of three companies — there is no long tail of independent filers to track.
- Filing peaked in 2018 at 8, then thinned out. Volume from 2021 to 2024 fell -25% (4 to 3), though the two most recent years are still filling in as publication catches up with filing.
- A61M and G16H dominate; G06N is a minority thread. 90.5% of records touch body-fluid device claims and 76.2% touch healthcare informatics, but only 23.8% engage AI-model computing directly.
Filing growth compares 2021 (4 records) with 2024 (3) — 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.
What the patent record shows
Patient-ventilator synchrony — detecting and correcting mismatches between a mechanically ventilated patient’s breathing effort and the ventilator’s delivered breath — is a narrow but technically demanding niche inside respiratory care. The patent record here is small: 21 published records spanning 2017 to a partial 2026, concentrated entirely within a ranked group of three assignees. There is no fragmented long tail; the field looks more like a handful of adjacent product lines than an open competitive race.
Filing activity peaked in 2018 and has since settled into a lower, more variable pace, with the most recent complete year showing a modest pullback from 2021 levels. Because publication trails filing by roughly a year and a half, the 2025-2026 figures are provisional and should not be read as a slowdown in actual R&D.
Filing trend and technology composition
Two views of the same 21 records: how filing volume moved year over year, and which IPC subclasses the claims sit in.
Filing trend, 2017-2026
Volume rose from zero in 2017 to a peak of 8 in 2018, then declined toward the mid-single digits. The 2021-to-2024 span shows a -25% change (4 to 3 records), the last window unaffected by publication lag; years after 2024 are still incomplete.
IPC subclass composition
A61M (devices for body fluids) appears in 90.5% of the 21 records and G16H (healthcare informatics) in 76.2%, confirming that most filings pair a physical ventilation mechanism with a data or decision-support layer. G06N (AI-model computing) appears in only 23.8%, marking machine-learning-native asynchrony detection as the smaller of the two technical approaches on record.
Shares are the percentage of the 21 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Patient-Ventilator Synchrony with Eureka
This page is one run against one query. Ask Eureka your own question about patient-ventilator synchrony and every answer comes back with the patent numbers behind it.
Try EurekaMost-cited records
Use of diaphragmatic ultrasound to determine patient-specific ventilator settings and optimize patient-ventilator asynchrony detection algorithms
A medical device includes a processing component that receives ventilation waveform data during mechanical ventilation and runs a patient-ventilator asynchrony monitoring method: detecting initial asynchrony events during a training period from patient measurements, then training a machine learning component on that waveform data with labelled asynchrony events to refine detection going forward.Filed by Koninklijke Philips N.V., published 2023-06-15.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20200261674A1 | Clinical Decision Support System for Patient-Ventilator Asynchrony Detection and Management | 26 |
| 2 | WO2019094736A1 | Clinical decision support system for patient-ventilator asynchrony detection and management | 23 |
| 3 | US20210407676A1 | Patient ventilator asynchrony detection | 7 |
| 4 | US20200405987A1 | Clinical decision support system for patient-ventilator asynchrony detection and management | 7 |
| 5 | US10874811B2 | Clinical decision support system for patient-ventilator asynchrony detection and management | 4 |
| 6 | US20210220587A1 | Clinical Decision Support System for Patient-Ventilator Asynchrony Detection and Management | 3 |
| 7 | AU2018366291B2 | Clinical decision support system for patient-ventilator asynchrony detection and management | 2 |
| 8 | US12073945B2 | Patient ventilator asynchrony detection | 1 |
| 9 | US11000656B2 | Clinical decision support system for patient-ventilator asynchrony detection and management | 1 |
Citation counts reflect prior filing dates more than current relevance — treat them as a measure of influence within this corpus, not of present-day importance.
Publication numbers are shown where the record carries one (9 of 9 rows); clicking a row searches Eureka by that number.
Put your own technology through the same analysis
Eureka on the web
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 →MCP server & REST API
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 →What the numbers mean for R&D and IP strategy
Three findings a search of the raw documents alone would not surface quickly.
No fragmented competitive field
Every record in scope belongs to the ranked leader group of three companies. That means freedom-to-operate work here is a short exercise in reading a handful of families closely rather than triaging dozens of unrelated filers.
Volume cooled after the 2018 peak
Filing hit 8 records in 2018, its high point, then settled lower. The -25% move from 2021 to 2024 is the only growth figure that can be trusted; 2025 and 2026 are still filling in under normal publication lag.
AI-native claims are still the minority path
Most filings pair a physical ventilation mechanism (A61M, 90.5%) with an informatics layer (G16H, 76.2%). Only about a quarter of records claim AI-model computing directly, leaving room for detection methods built natively around learned models rather than rule-based waveform thresholds.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to patient-ventilator synchrony, with the prior art for and against each one.
Who holds the claim space
Three assignees account for the entire ranked field, with recent-year filing activity now concentrated in just one of them.
The dominant filer by volume
The top-ranked assignee holds 13 of the 21 records in scope, making it the reference point for any freedom-to-operate review in this space. Its filings span both the physical ventilator mechanism and the informatics layer.
Activity has shifted toward one filer
In the most recent year captured, only one of the three ranked assignees shows filing activity, with the other two at zero. That does not necessarily mean the others have exited the space — the latest year is still incomplete under normal publication lag.
A closed, not an open, competitive set
This is the entire ranked assignee list the dataset returns for the topic — not a top-50 cut. New entrants would be filing into a field where the incumbents already hold both the mechanical and the informatics claim territory.
| Assignee | Recent year | YoY |
|---|---|---|
| ZOLL Innovation AB | 1 | — |
| Autonomous Healthcare, Inc. | 0 | — |
| Koninklijke Philips N.V. | 0 | — |
Where to take this next
The dataset points to specific follow-up work depending on whether you are scoping freedom-to-operate or looking for open claim territory.
Run a claims-level FTO check on the leader's portfolio
With one assignee holding 13 of 21 records, a detailed claims read of that portfolio will do more for a freedom-to-operate assessment than a broad prior-art sweep.
Explore assignee portfolios in EurekaTest draft claims against the G06N minority set
Only 23.8% of records claim AI-model computing directly, which suggests machine-learning-native detection methods may still have open claim territory relative to the denser A61M and G16H layers.
Draft and stress-test claims in EurekaCommon questions on patient-ventilator synchrony patents
The dataset returns a ranked group of three assignees covering all 21 records in scope, with no additional fragmented filers beyond that group. One assignee holds 13 of the 21 records, making it the largest single holder by a wide margin. This is a small, closed field rather than one with a long tail of independent entrants, so competitive tracking here means watching three portfolios closely rather than scanning dozens.
Filing peaked in 2018 at 8 records and has since settled lower, with a -25% change from 2021 (4 records) to 2024 (3 records) over the last window that can be treated as complete. Data for 2025 and 2026 is still incomplete because publication typically lags actual filing by around 18 months, so those years should not yet be read as a further decline. The honest read is a cooling-off from an early peak rather than a collapse.
Most records combine two layers: a physical ventilation mechanism, classified under A61M and present in 90.5% of the 21 records, and a healthcare informatics or decision-support layer, classified under G16H and present in 76.2%. A smaller share, 23.8%, also claims AI-model computing under G06N, indicating that machine-learning-native detection is a minority but present approach. Because a single record can carry more than one classification, these shares add up to more than 100%, which is expected.
This Philips filing covers a medical device that receives ventilation waveform data during mechanical ventilation and runs an asynchrony monitoring method: it detects initial asynchrony events during a training period from patient-specific measurements, then trains a machine learning component on that waveform data using labelled events from the training period to refine future detection. The distinguishing element is the diaphragmatic-ultrasound-informed, patient-specific training step feeding a machine learning detector, rather than a fixed rule-based threshold. Anyone building a learning-based asynchrony detector that trains on a per-patient calibration period should read this claim set closely.
The clearest gap sits in the minority G06N share: only 23.8% of the 21 records engage AI-model computing directly, against 90.5% for the core mechanical claims and 76.2% for informatics, suggesting learning-native detection methods are less densely claimed than the mechanism and data layers around them. Sub-areas such as neural-trigger cycling-off criteria, diaphragmatic ultrasound feedback loops and clinical-outcome correlation modules show thinner claim density than the core A61M and G16H territory. A first claim in these areas would need to specify the sensing input, the training or calibration step, and the specific asynchrony event type it resolves, to sit clear of the leader's existing decision-support claims.
Research Patient-Ventilator Synchrony in depth with Eureka
Go past this page: query the whole patient-ventilator synchrony corpus yourself, in your own scope.
Every answer comes back with patent numbers you can open.
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.