Query Optimization Patents: Who Leads, Where the Gaps Are 2026
Query Optimization and Cardinality Estimation Patents
Concentrated at the top. The five most active filers account for 62.6% of all 4,102 records in scope, with a long tail of single-digit filers behind them. Filings ran from 190 in 2017 up to a peak of 245 in 2019, then settled into a still-active pace; the complete-year comparison shows 176 filings in 2021 rising to 216 in 2024, a 23% increase. The 2025 and 2026 figures will keep revising upward as publication catches up with filing, so they should not be read as a slowdown.
- 1INTERNATIONAL BUSINESS MACHINE CORPORATION20.4%
- 2ORACLE INT CORP16.5%
- 3MICROSOFT TECHNOLOGY LICENSING LLC11.5%
- 4SAP SE10.9%
- 5SYBASE INC3.4%
See the full query optimization and cardinality estimation 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 landscape
Who holds the most patents in query optimization and cardinality estimation?
The ranking in this dataset covers 100 companies, and one assignee leads clearly with 835 records, well ahead of the fifth-place figure of 140 and tenth-place figure of 63. The top five filers together hold 62.6% of all 4,102 records in scope, and the top ten hold 72.4%. This is a field with a dominant leader and a long tail of smaller filers rather than an even split across many companies.
Is the query optimization patent field still growing?
Yes, on complete-year data: filings rose from 176 in 2021 to 216 in 2024, a 23% increase over that span. The field’s single busiest year so far was 2019 at 245 filings, so growth has not returned to that peak, but the 2021-2024 trend shows sustained activity rather than decline. Figures for 2025 and 2026 are still incomplete because publication typically lags filing by about 18 months.
What technology areas do these patents cover beyond core databases?
98.5% of the 4,102 records sit in G06F, the electric digital data processing classification, confirming that most filings are framed as classical database and query-planning mechanics. Smaller but notable shares touch G06N (AI-model computing, 5.0%) and H04L (digital information transmission, 3.3%), pointing to a minority but identifiable strand of machine-learning-assisted optimization and distributed execution work. Because records can carry multiple IPC classes, these shares add up to more than 100% and should not be read as mutually exclusive categories.
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 Query Optimization and Cardinality Estimation 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.