Uncertainty Calibration Patents: Who Leads, Where the Gaps Are 2026
Filing growth compares 2021 (1,182 records) with 2024 (785) — 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 130,117 records in scope (CR5), not by the ranked leaders only.
What this landscape covers
The search string pulls any record that combines uncertainty with calibration, or that names confidence calibration or model calibration directly. That casts a wide net: it catches classical analyte-sensor calibration patents from the diagnostics world alongside uncertainty calibration of deep neural networks — the kind of work behind well-calibrated confidence scores in modern AI systems. Both are legitimate hits on the same underlying problem — how does a system state how sure it is, and how is that self-assessment tested and corrected — but they come from very different engineering traditions.
That breadth shows up directly in the IPC composition: material analysis and testing, diagnosis and surgery, and radar/positioning classes each carry more records than the AI-model-specific G06N subclass. Anyone using this dataset to scope an AI-safety filing needs to filter mentally for the sensor-calibration prior art that dominates the raw counts, rather than treat every record as software calibration.
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Filing trend and technology composition
Two views of the same 130,117 records: how filing activity has moved year over year, and which IPC subclasses carry the work.
Filing trend, 2017–2026
Filings rose from 943 in 2017 to a peak of 1,182 in 2021, then declined to 785 by 2024 — a 34% drop over that three-year span. 2025 and 2026 figures are still incomplete because publication typically lags filing by around 18 months, so the most recent years should not be read as a continued decline yet.
IPC subclass composition
G01N (material analysis and testing) leads at 2.3% of all records, ahead of G06F (electric digital data processing) at 1.9% and A61B (diagnosis and surgery) at 1.7%. G06N, the subclass most associated with AI models specifically, sits further down at 0.8% of records — a reminder that calibration as a patenting concern is still mostly rooted in physical sensing and measurement rather than in AI model behaviour.
Shares are the percentage of the 130,117 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on AI Safety, Evaluation & Assurance: Uncertainty Calibration Patent Landscape with Eureka
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Try EurekaRepresentative filing and most-cited prior art
Methods and apparatus to obtain well-calibrated uncertainty in deep neural networks (US20210117760A1, Intel Corporation)
Discloses a differentiable accuracy-versus-uncertainty loss function used to train a machine learning model, combined with a post-hoc calibrator that applies temperature scaling to improve uncertainty calibration under distributional shift.Filed by Intel, published 2021-04-22 — one of the clearer examples in this dataset of calibration claimed specifically against neural network behaviour rather than physical sensor output.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US6931327B2 | System and methods for processing analyte sensor data | 1,854 |
| 2 | US6338790B1 | Small volume in vitro analyte sensor with diffusible or non-leachable redox mediator | 1,578 |
| 3 | US20050027463A1 | System and methods for processing analyte sensor data | 1,573 |
| 4 | US6275717B1 | Device and method of calibrating and testing a sensor for in vivo measurement of an analyte | 1,563 |
| 5 | US20050027180A1 | System and methods for processing analyte sensor data | 1,430 |
| 6 | US7276029B2 | System and methods for processing analyte sensor data | 1,365 |
| 7 | US20080021666A1 | System and methods for processing analyte sensor data | 1,349 |
| 8 | US20050027181A1 | System and methods for processing analyte sensor data | 1,261 |
| 9 | US20090012379A1 | System and methods for processing analyte sensor data | 1,195 |
| 10 | US20040193413A1 | Architecture for controlling a computer using hand gestures | 1,135 |
Citation counts reflect influence within the searched corpus built up over time, and structurally favour older filings — they are not a measure of which patents matter most today.
Each row carries its publication number; clicking a row searches Eureka by that number.
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Browse MCP servers →What the numbers mean for filing strategy
Four read-outs from the ranking, the trend and the class composition, translated into what they imply for a team deciding where to file next.
Fragmented ownership, not a gatekept field
The leader holds 3,059 records and the top 10 combined reach only 11,096 — 8.5% of all records in scope. That is a long tail behind a modest lead, not a field controlled by two or three incumbents.
Post-peak decline, but recent years are still filling in
Filings peaked at 1,182 in 2021 and had fallen to 785 by 2024. Because publication lags filing by roughly 18 months, 2025 and 2026 figures will rise as more records publish, so the near-term picture is incomplete rather than definitively falling further.
Sensor calibration still outweighs AI-model calibration
G01N (material analysis and testing) is the single largest IPC subclass at 2.3% of all records, with A61B and G01S close behind. G06N, the subclass tied most directly to AI computing models, trails at 0.8%, which means AI-specific calibration claims are competing in a much thinner slice of prior art than the aggregate numbers suggest.
US filing dominates, PCT and EPO trail
The United States receiving office accounts for by far the largest share of records at 10,914, ahead of EPO at 3,602 and WIPO/PCT at 2,846. Teams benchmarking freedom-to-operate should weight US prior art most heavily, then check EPO and PCT filings for divergent claim scope.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to ai safety, evaluation & assurance: uncertainty calibration patent landscape, with the prior art for and against each one.
Where to take this analysis
The aggregate numbers set the scope; the next step is narrowing to the claims and assignees that actually matter for a specific filing decision.
Separate sensor calibration from model calibration
Re-run the IPC breakdown filtered to G06N and G06F records specifically to see how much of the AI-relevant prior art survives once sensor and measurement patents are excluded.
Explore the technology in EurekaTrack the post-peak assignees
Identify which of the ranked leaders kept filing after the 2021 peak and which pulled back, to see who is still actively building a calibration portfolio.
Run an assignee analysis in EurekaPressure-test claim scope against US20210117760A1
Use the representative Intel filing as a benchmark to map how narrowly or broadly neural-network calibration claims are being drafted across the dataset.
Compare claims in EurekaCommon questions about this landscape
Any record matching uncertainty combined with calibration, or naming confidence calibration or model calibration directly, is included. In practice this pulls in classical sensor and diagnostic calibration patents (analyte sensors, radar, dimensional measurement) alongside AI-specific work on calibrating model confidence scores. The two traditions solve a related problem — quantifying and correcting a system's stated certainty — but come from different engineering fields, so filtering by IPC subclass matters when narrowing to AI applications specifically.
Filings rose from 943 in 2017 to a peak of 1,182 in 2021, then fell to 785 by 2024, a 34% decline over that three-year span. Figures for 2025 and 2026 are still incomplete because publication typically lags filing by around 18 months, so the apparent recent drop should not yet be read as the field cooling further. 2024 is the most recent year that can be treated as a complete data point.
The ranking covers 100 companies, with the leader holding 3,059 records and the tenth-ranked holder at 649. The top 10 combined account for 11,096 records, only 8.5% of all 130,117 records in scope, which means ownership is fragmented rather than concentrated in a handful of players. A long tail of smaller filers holds the bulk of the remaining activity.
Sensor and measurement calibration dominates the raw IPC counts: G01N (material analysis and testing) leads at 2.3% of records, with A61B (diagnosis and surgery) and G01S (radar, sonar and positioning) also ahead of anything AI-specific. G06N, the subclass tied to AI computing models, sits at 0.8% of records. A team focused specifically on AI model calibration should treat the aggregate dataset as broader than its target and filter down to G06N and G06F before drawing conclusions about that narrower space.
The Intel filing discloses a differentiable accuracy-versus-uncertainty loss function used during training, plus a post-hoc temperature-scaling calibrator applied afterward to improve calibration under distributional shift. It is a useful representative record because it claims calibration against neural network behaviour specifically, rather than against a physical sensor's output, which is the more common pattern in this dataset. Anyone drafting claims in AI model calibration should benchmark scope against filings of this kind rather than against the sensor-calibration prior art that numerically dominates the corpus.
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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.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.
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.