SMR Operation AI/ML Patent Snapshot 2026
Patent activity applying AI and machine learning to small modular reactor (SMR) operation is at a very early stage, with just 4 patent families in scope and activity concentrated in academic institutions across China and India. Northwestern Polytechnical University holds the leading position, and virtually all filings cluster in core AI model computing, signalling that this field remains largely open for industrial entrants.
Academic institutions dominate a nascent, highly concentrated field
Northwestern Polytechnical University leads the applicant ranking with 2 patent records, ahead of a long tail of individual academic contributors each holding 1 patent record. The top five filers account for 46% of the combined total across the ranked applicants visible in this query.
The field is extremely concentrated: the leading institution holds twice as many records as any other named applicant, and the remaining filers are spread across a mix of Indian engineering colleges and one Chinese university. There is no visible industrial or commercial assignee in the current corpus.
| # | Applicant | Patent records | Share |
|---|---|---|---|
| 1 | Northwestern Polytechnical University | 2 | |
| 2 | Mr. Sreenarayanan N.M., Galgotias University | 1 | |
| 3 | Dr. Ganga Sharma, Galgotias University | 1 | |
| 4 | Ms. Neetu Garg, Maharaja Agrasen Institute of Technology | 1 | |
| 5 | Dr. Sampath Kumar K., Galgotias University | 1 | |
| 6 | Ms. Kajol Dahiya, Maharaja Agrasen Institute of Technology | 1 |
| # | Applicant | Patent records | Share |
|---|---|---|---|
| 7 | Dr. Jeba Shiney O., Chandigarh University | 1 | |
| 8 | Dr. Deepak Kumar Goyal, Vaish College of Engineering | 1 | |
| 9 | MS PRIYA PORWAL GL BAJAJ INST OF TECH & MANAGEMENT | 1 | |
| 10 | MS ISHA GUPTA HMR INST OF TECH & MANAGEMENT | 1 | |
| 11 | Changchun University | 1 | |
| 12 | Ms. Sneha Mishra, Noida International University | 1 |
The absence of industry participants implies that core SMR-specific AI/ML methods have not yet been claimed by reactor vendors or energy companies, leaving the technical ground substantially open for early movers with domain-specific expertise.
Filing data for the most recent 18–24 months is subject to publication lag and may understate actual activity; the corpus should be treated as a lower-bound snapshot of current filing momentum. Longer-window growth, applicant concentration, and technology-route coverage are therefore more reliable signals than the latest-year bar alone.
Sparse filing history with a recent cluster; AI computing dominates the technology mix
The annual trend and technology composition charts together reveal both the immaturity of this field and its near-exclusive focus on general AI model computing methods rather than SMR-specific algorithmic approaches.
Annual filing trend
No filings appear before 2022; a single record was filed in 2022, followed by a gap in 2023–2024, and then three records in 2025. The 2025 cluster may be partially undercounted due to publication lag, so it should be read as a floor rather than a ceiling. The overall pattern is consistent with a pre-commercialisation field just beginning to attract academic attention.
↗ Hover for values · click a bar to ask EurekaTechnology composition
Computing based on AI models (G06N) covers all 4 records in scope, confirming that current work targets foundational ML methods. Radar, sonar and positioning (G01S) appears as a secondary branch with 2 records, driven by Northwestern Polytechnical University’s emphasis on sensing and positioning. Electric digital data processing (G06F), data recognition and presentation (G06K), and image data processing (G06T) each account for 1 record, suggesting exploratory work across perception and data pipelines.
↗ Hover for values · click a bar to ask EurekaHighly cited patent families surfaced by the query
Citation-heavy patent families returned by the query. Use this section as citation context, not as a curated list of the most topic-specific patents.
一种吸附增强甲烷水蒸气重整氢气产率预测方法
本发明提出了一种吸附增强甲烷水蒸气重整氢气产率预测方法，旨在解决现有吸附增强甲烷水蒸气重整（SE‑SMR）氢气产率预测方法存在的问题，通过创新的Transformer‑Bagging模型，采用K折交叉验证方法对模型进行训练后，能更准确地预测SE‑SMR过程中的氢气产率，降低实验成本和时间消耗，解决现有机器学习方法在预测时，无法有效捕捉输入参数间非线性关系、非稳态工况下预测精度欠佳的问题。为SE‑SMR制氢工艺的优化和实际生产提供可靠的技术支持，具有显著的经济价值和良好的工程适用性。 (excerpt from the patent abstract)
Ranked by total forward citations. Citation counts favour older and broadly cited patent families, and broad or adjacent patents may appear when they match the search scope. Treat this section as citation context, not as a curated list of the most topic-specific patents. Some patent titles may be shown in their original, non-English language where an accurate translation could not be guaranteed.
Assignee snapshot from the current evidence set
The applicants below are visible in this query result. Because the evidence set is relatively small, read this section as a directional snapshot rather than a full competitive ranking.
Northwestern Polytechnical University
Northwestern Polytechnical University holds 2 patent records, the largest share among all named filers. Its technology focus spans radar and positioning methods (G01S 13, G01S 7) combined with neural computing (G06N 3), indicating work on AI-assisted sensing and situational awareness that could be relevant to reactor monitoring or autonomous navigation within SMR environments. Momentum data is not available for this applicant in the recent-period breakdown.
patent records: 2Galgotias University consortium
Multiple individual researchers affiliated with Galgotias University together account for several of the remaining patent records, each focusing on data recognition (G06K 9), AI model computing (G06N 3), and image data processing (G06T 7). All named individual contributors from this consortium are flagged as new entrants in the recent period, suggesting a coordinated academic effort launched in 2025. The shared technical profile across these co-applicants implies a single collaborative filing campaign rather than independent parallel programs.
patent records: 1 per named authorFrequently asked questions
The evidence identifies 4 patent families in scope. This is a very small corpus, consistent with an emergent field that has attracted attention primarily from academic institutions in China and India since 2022.
Northwestern Polytechnical University is the leading filer with 2 patent records, ahead of all other named applicants, each of whom holds 1 patent record.
China accounts for 3 patent records and India for 1. No filings have been identified in the United States, Europe, or other major nuclear energy markets based on the current evidence.
Computing based on AI models (G06N) covers all 4 patent records in scope. Secondary branches include radar, sonar and positioning (G01S) with 2 records, and electric digital data processing (G06F), data recognition and presentation (G06K), and image data processing (G06T) each with 1 record.
Based on the current evidence, all named assignees are universities or engineering colleges. No reactor vendors, energy companies, or commercial technology firms appear in the applicant ranking.
Control system optimisation (G05B) and predictive maintenance or anomaly detection (G01M/G06F) are both absent from the current corpus despite being technically adjacent and commercially relevant to SMR operation. These represent under-served branches where entry-level prior art is thin, though they should be validated with a broader search before committing to a filing strategy.
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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.
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