Surgical Robot Learning and Control Patents: Leaders & White Space 2026
- Filing has plateaued, not accelerated. Volume peaked at 3 families in 2022 and has not grown since, despite the field's commercial visibility.
- One family dominates citation influence. US20170325932A1, an additive-manufacturing device for biomaterials, has drawn 62 citations versus single digits for every other record in the set.
- Filers default to the US. Eight of eleven records route through the USPTO, with only a thin trickle via PCT and the EPO — a narrow geographic footprint for a surgical technology.
What this landscape covers
This dataset tracks patent families at the intersection of surgical robotics and learning-based control — filings that combine claims on robotic surgical systems with claims on imitation learning, autonomous task learning, or learning-based control methods. It is a narrow, emerging slice of a much larger surgical robotics field, and the family count reflects that: eleven records total, spanning 2017 through a partial 2026.
Because publication typically lags filing by around 18 months, the 2026 count understates real filing activity for that year. The pattern that matters is the plateau at the 2022 peak rather than the tail-end numbers themselves.
Filing trend and technology composition
Eleven patent families, one dominant early filer, and a technology mix that leans as much on additive manufacturing and implant hardware as on the control software itself.
A flat trajectory since 2022
Filings opened at 2 in 2017, rose to a peak of 3 in 2022, and have not exceeded that level since. This is a small, still-forming field rather than one in an active filing race.
Hardware and imaging carry as much weight as control
A61F (implants and prostheses) and B33Y (additive manufacturing) each account for 4 of the classified records, with B29C/B29L (plastics shaping) and G02B (optics), G06N (AI computing), and H04N (imaging/video) each at 3. Only one record classifies squarely under A61B, the core surgery/diagnosis subclass — a sign that much of the claimed value here sits in delivery hardware and sensing, not in the learning algorithm alone.
Shares are the percentage of the 11 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Surgical Robot Learning and Control with Eureka
This page is one run against one query. Ask Eureka your own question about surgical robot learning and control and every answer comes back with the patent numbers behind it.
Try EurekaThe most-cited records
Additive manufacturing device for biomaterials (US10888428B2)
An additive manufacturing device for biomaterials comprising a reservoir, a shaft, and a material delivery head, designed for intracorporeal additive manufacturing. Material is expelled via a mechanical transmission element such as a syringe, peristaltic, air pressure, or hydraulic pump, and the device carries an actuator joint that can be mechanically linked to a robotic surgical system.Filed by University of Notre Dame du Lac; granted 2021-01-12.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20170325932A1 | Additive manufacturing device for biomaterials | 62 |
| 2 | US10888428B2 | Additive manufacturing device for biomaterials | 4 |
| 3 | US20210085469A1 | Additive Manufacturing Device For Biomaterials | 3 |
| 4 | US12581202B2 | Methods and apparatuses for imaging under pulse-width modulated illumination | 1 |
Citation counts reward older filings that have had more time to accumulate references; read them as a measure of influence within this corpus, not of current commercial relevance.
Publication numbers are shown where the record carries one (4 of 4 rows); clicking a row searches Eureka by that number.
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Browse MCP servers →What the numbers actually indicate
Small family counts and a single high-citation outlier change how this data should be read compared with a mature, high-volume field.
No acceleration since the peak
Activity climbed from 2 families in 2017 to a high of 3 in 2022 and has since held flat or declined. For a field this narrow, that is consistent with a handful of research-heavy filers rather than a broad industry buildout.
Influence sits with one early filing
US20170325932A1 carries 62 citations, far ahead of the next-closest record at 4. Later filings on the same underlying device (US10888428B2, US20210085469A1) inherit little of that citation weight, which is typical when a foundational disclosure is followed by narrower continuations.
Filing is concentrated in the US
Eight records route through the USPTO, two through PCT, and one through the EPO. That footprint suggests protection strategy here is still domestic-first rather than built for global enforcement.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to surgical robot learning and control, with the prior art for and against each one.
Who is filing, and where the gaps sit
The assignee set is small and academic-leaning, with recent momentum thin enough that no single filer has established a durable lead in learning-based surgical control specifically.
Unify Medical Inc
Unify Medical Inc filed 1 family in the latest year, down 67% year-on-year — the only assignee in this set with recorded activity in the most recent period.
University of Notre Dame du Lac
Holder of the most-cited family in the set (via its additive-manufacturing device for biomaterials), but with no recorded filings in the latest year — activity here looks concentrated in an earlier filing window.
The Open University
Also shows no recorded filings in the latest year. Combined with Notre Dame, academic assignees account for a meaningful share of this small dataset, pointing to research-stage rather than product-stage claiming.
| Assignee | Recent year | YoY |
|---|---|---|
| UNIFY MEDICAL INC | 1 | -67% |
| University of Notre Dame du Lac | 0 | — |
| The Open University | 0 | — |
Where to take this analysis
Eleven families is a small enough set that manual review of each is practical before committing to a filing or freedom-to-operate position.
Map claims against the additive-manufacturing cluster
With A61F and B33Y each covering a third of the classified records, check whether a planned filing on delivery hardware overlaps the Notre Dame device family before drafting around it.
Explore claim charts in Eureka →Track the thin PCT/EPO footprint
Only three of eleven families extend beyond the US. If commercial plans include Europe or broader international coverage, this gap is worth confirming rather than assuming.
Run a jurisdiction gap check in Eureka →Common questions on this landscape
This dataset identifies 11 patent families published between 2017 and mid-2026 that combine surgical robotic system claims with learning-based control, imitation learning, or autonomous task learning claims. That is a small, emerging niche rather than a mature filing category. Because publication lags filing by roughly 18 months, the true count of filed-but-unpublished applications for 2025 and 2026 is likely higher than shown.
The set includes Unify Medical Inc, which filed in the most recent tracked year, alongside academic assignees University of Notre Dame du Lac and The Open University. No single filer holds a dominant share of the 11 families, and the University of Notre Dame family carries the highest citation count in the set. This is consistent with a field still centred on research institutions rather than a small number of commercial leaders.
Not currently by this measure: filings rose from 2 in 2017 to a peak of 3 in 2022, and have not exceeded that level since. That is a flat-to-declining trend on the visible data, though the most recent one to two years are understated because of normal publication lag. A practitioner should treat the plateau as provisional rather than a confirmed slowdown.
Start with US10888428B2 and its related family members (US20170325932A1, US20210085469A1), all describing an additive-manufacturing device for biomaterials with an actuator joint mechanically linkable to a robotic surgical system. This family carries the highest citation count in the dataset, which typically signals it is a frequently referenced baseline disclosure. Also review the A61F and B33Y-classified records generally, since implant and additive-manufacturing hardware account for a larger share of this dataset than the learning-control software itself.
The classified records lean heavily toward hardware (implants, additive manufacturing, plastics shaping) and imaging, with only one record classified under the core A61B surgery subclass. That imbalance suggests claim space around the learning and control methods themselves — task transfer via imitation learning, autonomous sub-task handoff, and learning-based haptic feedback — remains comparatively open. A first claim in these branches would need to tie the learning method to a specific surgical robotic system architecture rather than claiming the algorithm in the abstract.
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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.