Closed-Loop Adaptive DBS Patents: Who Leads, Where the Gaps Are 2026
- 70.0% of all filings sit with five assignees. 159 of 227 records in scope belong to the top five, with a long tail of single- or few-filing entrants below tenth place.
- Filing activity has plateaued, not declined. Growth from 2021 to 2024 sits at +3%, off a 2020 peak of 41 filings — later years understate themselves because publication lags filing by roughly 18 months.
- A61N electrotherapy claims dominate the class mix. 94.3% of the 227 records touch A61N, while AI-model computing (G06N) and healthcare informatics (G16H) remain thin, at 0.9% and 6.6% respectively.
Filing growth compares 2021 (39 records) with 2024 (40) — 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 227 records in scope (CR5), not by the ranked leaders only.
What the closed-loop adaptive DBS patent record shows
Closed-loop adaptive deep brain stimulation ties electrical delivery to a sensed physiological signal — a local field potential, a beta-band biomarker, or another marker of symptom fluctuation — so that stimulation adjusts in near-real time rather than running at a fixed setting. The patent record spans 227 records filed between 2015 and the current data cut-off, concentrated heavily around electrotherapy hardware and sensing rather than the software layer that interprets the signal.
Filing activity peaked in 2020 and has since held roughly steady rather than accelerating, which matches a field where the core sensing-and-stimulation architecture is largely settled and later filings refine control policy, artifact rejection and power management around it.
Filing trends and technology composition
Two views of the same 227-record dataset: how filing volume has moved year over year, and how records distribute across IPC subclasses. Because a single record can carry more than one IPC class, the composition shares add up to more than 100%.
A 2020 peak followed by a plateau
Filings ran from 3 in 2017 to a peak of 41 in 2020. Between 2021 (39) and 2024 (40), the last year that can be read as complete, volume grew only 3% — a plateau rather than a decline once the 18-month publication lag on 2025-2026 is taken into account.
Electrotherapy dominates; software-adjacent classes are thin
A61N (electrotherapy & radiation therapy) appears in 94.3% of the 227 records and A61B (diagnosis & surgery) in 47.1%. Healthcare informatics (G16H, 6.6%) and AI-model computing (G06N, 0.9%) are present but far smaller, indicating that most claim activity still centers on the stimulation and sensing hardware rather than the algorithmic control layer.
Shares are the percentage of the 227 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Closed-Loop Adaptive Deep Brain Stimulation with Eureka
This page is one run against one query. Ask Eureka your own question about closed-loop adaptive deep brain stimulation and every answer comes back with the patent numbers behind it.
Try EurekaThe most-cited records and a recent representative filing
WO2024227173A2 — Methods and systems for identifying gait biomarkers used to drive adaptive deep brain stimulation
The inventors discovered that neural oscillatory activities in the sensorimotor cortex region of the brain and the basal ganglia system of the brain are indicative of physiological gait events. The invention utilizes this discovery, along with recent advances in neural interfaces and machine learning techniques, to provide new and useful methods for identifying gait events directly from the neural activity of an individual. In particular, methods and systems are provided that produce trained classification models capable of accurately identifying left and right leg events during walking.Filed by The Regents of the University of California, October 2024 — illustrates the field's move toward gait and motor-event biomarkers as a stimulation trigger, beyond the beta-band standard.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20200030608A1 | Treatment for loss of control disorders | 113 |
| 2 | US20170173335A1 | System and Method to Managing Stimulation of Select A-Beta Fiber Components | 108 |
| 3 | US20170001016A1 | Methods of sensing cross-frequency coupling and neuromodulation | 107 |
| 4 | US20220386935A1 | Method and system for targeted and adaptive transcutaneous spinal cord stimulation | 27 |
| 5 | WO2021062345A1 | Method and system for targeted and adaptive transcutaneous spinal cord stimulation | 26 |
| 6 | US20200388397A1 | Energy-efficient on-chip classifier for detecting physiological conditions | 24 |
| 7 | US9555248B2 | System and method for tactile C-fiber stimulation | 23 |
| 8 | US20210196964A1 | Brain stimulation and sensing | 21 |
| 9 | US20210196958A1 | Brain stimulation and sensing | 19 |
| 10 | US10092758B2 | System and method for tactile C-fiber simulation | 18 |
Citation counts favour older filings simply because they have had more time to accumulate citations inside the searched corpus; read them as a signal of influence, not of current commercial weight.
Each row carries its publication number; clicking a row searches Eureka by that number.
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The dataset points to a field where core hardware claims are locked up by a small group of assignees, while the control-policy and software layers remain comparatively open.
Hardware claim space is largely occupied
With 159 of 227 records held by five assignees, new entrants attempting broad electrotherapy or sensing-hardware claims will be filing into dense prior art. The opportunity lies in narrower control-policy, artifact-rejection or power-management claims layered on top of established hardware.
A plateau, not a retreat
Volume held nearly flat between 2021 (39) and 2024 (40) after peaking at 41 in 2020. That reads as a maturing hardware base rather than declining interest, especially once the 18-month publication lag on the most recent years is factored in.
The algorithmic layer is thinly claimed
Only 2 of 227 records fall under G06N and another 2 under H05K, against 214 under A61N. Control policies and machine-learned biomarker detection sit well below the density of the underlying stimulation hardware, leaving room for narrowly drafted software and signal-processing claims.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to closed-loop adaptive deep brain stimulation, with the prior art for and against each one.
| Assignee | Co-assignee | Shared families |
|---|---|---|
| DE RIDDER DIRK | DIRK DE RIDDER | 7 |
| The Regents of the University of California | Chancellor, Masters and Scholars of the University of Oxford | 4 |
| The Regents of the University of California | STARR PHILIP | 4 |
| The Regents of the University of California | SMYTH CLAY | 4 |
| The Regents of the University of California | LITTLE SIMON J | 4 |
| The Regents of the University of California | DENISON TIMOTHY | 4 |
| The Regents of the University of California | ANJUM FAHIM | 3 |
| The Regents of the University of California | University of Washington | 2 |
Ten co-assignee pairs appear in the dataset, the strongest tied to individual inventor-institution relationships rather than broad industry consortia, suggesting collaboration here is still largely bilateral.
Assignee concentration and recent momentum
The ranking covers 34 companies across all 227 records in scope — not a top-50 or top-100 cut, the complete set the data endpoint returns. Filing sits heavily at the top: the leader holds 48 records, fifth place 18, and tenth place 6.
A single assignee well ahead of the field
The top-ranked assignee's 48 records sit far above fifth place at 18, setting a pace the rest of the ranked field has not matched. That gap is the clearest signal of where the deepest prior art sits.
A sharp drop-off after the top five
Record counts fall from 18 at fifth place to 6 at tenth, then thin into a long tail of single- and few-filing entrants. Top 10 combined account for 88.5% of all 227 records, leaving little volume outside the ranked leaders.
Recent-year activity has gone quiet across leaders
Several of the most active historical filers, including university and corporate assignees, show zero filings in the latest year with steep year-over-year drops. This is consistent with the publication lag rather than an actual pullback, but it means the most recent public signal from top filers is thin.
| Assignee | Recent year | YoY |
|---|---|---|
| NEWRONIKA | 0 | -100% |
| Medtronic Inc | 0 | -100% |
| Niche Biomedical Inc | 0 | -100% |
| Oxford University Innovation Ltd | 0 | — |
| DE RIDDER DIRK | 0 | — |
| The Regents of the University of California | 0 | -100% |
| RIDDER DIRK DE | 0 | — |
| DIRK DE RIDDER | 0 | — |
Where to take this analysis
The dataset frames where filing has concentrated; the next step is checking a specific claim or design concept against it directly.
Run a freedom-to-operate check
Test a specific control-policy or biomarker claim against the 227 records in scope before committing engineering time to it.
Check in EurekaTrack the leaders' recent filings
Several top assignees show a quiet latest year in this dataset; watch their filings as the 18-month publication lag closes.
Set up monitoring in EurekaCommon questions about closed-loop adaptive DBS patents
It refers to stimulation systems that adjust delivery in response to a sensed physiological signal, most commonly a local field potential or beta-band biomarker linked to symptom fluctuation, rather than delivering a fixed continuous pulse. The search underlying this dataset combines terms like adaptive deep brain stimulation and closed loop neurostimulation with technical markers such as control policy, stimulation artifact and battery saving. This keeps the scope to systems with a genuine sensing-to-stimulation feedback loop, distinct from open-loop DBS devices that lack that feedback path.
Filing is concentrated: the leading assignee holds 48 of the 227 records in scope, and the top five combined account for 70.0% of all records. Beyond tenth place, the field thins into a long tail of assignees with only a handful of filings each, including academic institutions and individual inventors. Anyone assessing freedom to operate should look closely at the leaders' portfolios first, since they cover the large majority of hardware and sensing claim space.
Filing volume peaked in 2020 at 41 records and has since held roughly steady, with growth of just 3% between 2021 and 2024 — the last year that can be read as complete. Figures for 2025 and 2026 look lower, but that reflects the typical 18-month lag between filing and publication rather than an actual drop in activity. Treat the recent years as still filling in, not as evidence of a slowdown.
The IPC composition shows heavy concentration in A61N electrotherapy hardware (94.3% of records) and A61B diagnosis and surgery (47.1%), but much thinner coverage in G16H healthcare informatics (6.6%) and G06N AI-model computing (0.9%). That gap points to under-claimed territory in the algorithmic control-policy layer: machine-learned biomarker detection, real-time artifact rejection, and adaptive duty-cycling for battery saving. A first claim in these areas would sit on comparatively sparse prior art relative to the core stimulation hardware.
The most-cited records in this dataset include filings on loss-of-control disorder treatment and A-beta fiber stimulation management, each cited over 100 times, alongside more recent transcutaneous spinal cord stimulation filings cited closer to 27 times. High citation counts favour older filings simply because they have had longer to accumulate citations within the searched corpus, so they are a better signal of foundational influence than of current commercial relevance. Newer, less-cited filings, like the 2024 gait-biomarker application, can still represent the direction the field is actually moving.
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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.
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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.