Machine Learning Patents: Who Leads, Where the Gaps Are 2026
Machine Learning Patents: Filing Trends, Leading Assignees and Where the Claim Space Is Still Open
14.7% of the field sits with a handful of filers. the top five assignees combined account for 100,848 of 685,824 records in scope, with a long tail of single- and low-filing entrants behind them. Annual filings ran from 460 in 2017 to a peak of 3,499 in 2023. The 2021-to-2024 span, the last window unaffected by publication lag, moved from 2,871 to 2,948 records — a +3% increase rather than a decline. Counts for 2025 and 2026 are still filling in and should not be read as a slowdown.
- 1SAMSUNG ELECTRONICS CO LTD4.7%
- 2QUALCOMM INC3.6%
- 3GOOGLE LLC2.4%
- 4MICROSOFT TECHNOLOGY LICENSING LLC2.1%
- 5INTERNATIONAL BUSINESS MACHINE CORPORATION1.9%
See the full machine learning patent landscape analysis in Eureka
- The complete ranking, not just the top five
- Every IPC branch with its share of the corpus
- The most-cited records, and where claim space is still thin
Common questions on the machine learning patent landscape
Who holds the most machine learning patents?
The assignee ranking for this corpus is led by a single company with 32,436 records, more than double the fifth-place figure of 13,113. The top five assignees combined hold 14.7% of all 685,824 records in scope, and the top ten hold 20.5%. That leaves roughly four-fifths of filings spread across a long tail of companies filing at much lower volume, so leadership at the top does not translate into control of the whole field.
Is machine learning patent filing still growing in 2026?
Filing volume grew modestly rather than exploding or collapsing: the 2021-to-2024 window, the last stretch that can be treated as complete, moved from 2,871 to 2,948 records, a +3% increase. The peak year on record so far is 2023 at 3,499 filings. Figures for 2025 and 2026 look lower only because publication typically lags actual filing by around 18 months, not because filing activity has genuinely dropped.
Which IPC classes cover machine learning patents?
G06N, the class for computing arrangements based on AI models, is the densest at 1.8% of all 685,824 records in scope, followed by G06F electric digital data processing at 0.9%. Image and recognition-related classes such as G06V, G06T and G06K each account for roughly 0.3-0.4% of records. Application-specific classes like G16H healthcare informatics and G06Q business-process data handling trail at around 0.3%, indicating those verticals are less densely claimed than the core computing classes.
Disclaimer. This analysis is based on Patsnap Eureka data drawn from a limited snapshot of global patent records and is provided for general information and reference only. Patent data carries inherent limitations — recent filings are under-counted because of publication lag, counts may be on a record or family basis, classification and applicant-name data may contain errors or duplicates, and the underlying search query defines the scope shown — so the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing here is an exhaustive prior-art, novelty, freedom-to-operate or validity search, nor does it constitute legal, financial or professional advice, and it should not be relied upon as such. Verify independently and review with qualified patent and legal professionals before acting on it.
Method: Filing trend and technology composition. Derived from a Patsnap search on Machine Learning Patent Landscape covering 2015–2026, data cut-off 2026-08-31. Counts reflect published records only and shift as new filings publish. Every share divides by all records in scope. Data: Patsnap Eureka. See the full landscape report.