Edge Vision Deep Learning Patents: Leaders & Filing Trends 2026
Deep Learning Deployment on Edge Vision Systems: Patents Mapped by Leader, Technology and Gap
Concentrated at the top. The five leading assignees hold 53.1% of all 292 records in scope, and the leading ten hold 60.3% — a long tail files just once or twice. Filings ramped from zero in 2017 to a peak of 77 in 2018, then settled into a steadier band. The complete-year comparison of 2021 to 2024 shows +10% growth — modest, sustained expansion rather than a rush. 2025 and 2026 figures will revise upward as later-filed applications publish.
- 1CEREBRAS SYSTEMS INC120
- 2VELLORE INSITUTE OF TECH12
- 3SAMSUNG ELECTRONICS CO LTD11
- 4GOOGLE LLC6
- 5INTEL CORP6
See the full deep learning deployment on edge vision systems 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 about this patent landscape
Who holds the most patents in deep learning deployment on edge vision systems?
The assignee ranking in this dataset covers 100 companies, with the leading assignee holding 120 records out of 292 total — a substantial share on its own. The top five combined hold 53.1% of all records, and the top ten hold 60.3%, so filing activity is concentrated among a small group of large technology and chip companies even though the full ranking runs a hundred entities deep. Past the leading ten, counts fall off quickly into single- and double-digit filers, including university research groups and individual inventors.
Is patent filing in edge AI vision deployment growing or slowing down?
Over the last fully comparable three-year window, 2021 to 2024, filings grew 10% — steady but not explosive growth. Filing peaked earlier, in 2018 at 77 records, and has settled into a more moderate pace since. Numbers for 2025 and 2026 in this dataset should be read as provisional and likely undercounted, because publication typically lags the actual filing date by around 18 months.
What’s the difference between the accelerator hardware claims and the vision-specific claims in this space?
Most records in this corpus classify under G06N (AI computing models generally, 78.4% of records) and G06F (general digital data processing, 43.8%), meaning the bulk of claim activity centers on the compute architecture that runs a model rather than the vision task itself. Only 11.0% of records fall under G06V (image and video recognition specifically) and 3.8% under G06T (image data processing). That gap suggests claims narrowly tied to vision-task performance, rather than general accelerator design, are comparatively less crowded.
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 Deep Learning Deployment on Edge Vision Systems covering 2015–2026, data cut-off 2026-07-31. 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.