Latent Defect Detection Patents: Who Leads, Where the Gaps Are 2026
Latent Defect and Outlier Detection Patents: Filing Trends and Who Owns the Claim Space
One filer holds 25 of 66 records and the top five together account for 78.8% of all records in scope — this field is concentrated, not fragmented. Filings were at zero in 2017, climbed to a peak of 15 in 2022, then dropped to 1 by 2024 — a -91% change across that span. Treat 2025 and 2026 counts as incomplete rather than a continued decline, since publication typically lags filing by around 18 months.
- 1KLA CORP25
- 2ESSENLIX CORP10
- 3TEST ACUITY SOLUTIONS INC AZ US7
- 4PRESIDENT & FELLOWS OF HARVARD COLLEGE5
- 5TEST ADVANTAGE5
See the full latent defect and outlier detection 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 field
Who holds the most patents in latent defect and outlier detection for semiconductors?
One assignee leads the ranked field with 25 of the 66 records in scope, well ahead of the rest of the ranking. The top five assignees combined hold 78.8% of all 66 records, and the top ten reach 98.5%, so the field is concentrated rather than fragmented. Any competitive or freedom-to-operate review should prioritise the leading assignee’s portfolio before a broader prior-art sweep.
Is patent filing in this space growing or shrinking?
Filings rose to a peak of 15 in 2022, then fell to 1 by 2024, a -91% drop across that three-year span. That decline should not be extended into 2025 or 2026, because publication typically lags filing by roughly 18 months and those most recent years are still filling in. Treat 2024 as the most recent complete year for trend purposes.
What technology areas do these patents actually claim?
Over half of the 66 records (53.0%) carry an IPC code in G01R, covering electric and magnetic measurement, and a quarter (25.8%) sit in H01L, semiconductor devices — the classical electrical-test core. Machine-learning framing shows up in G06N (12.1%) and image-based methods in G06T (9.1%), indicating an AI overlay on top of the statistical core rather than a replacement for it. A record can carry several IPC codes, so these shares add up to more than the total record count.
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 Latent Defect and Outlier Detection 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.