Micro-LED AI/ML Defect Repair Patent Snapshot 2026
The application of AI and machine learning to Micro-LED defect detection and repair is a nascent field with only 5 patent families in scope, all filed in China and concentrated among academic institutions. Activity emerged only in 2023, marking this as an early-formation stage where foundational positions remain almost entirely open.
Academic institutions lead a highly concentrated, early-stage field
Guangdong University of Technology holds the largest position with 2 patent families, placing it first in the applicant ranking. The remaining four filers — Foshan Longwei Intelligent Equipment, Xiamen University, Tan Kah Kee Innovation Laboratory, and Xi’an Jiaotong University — each hold 1 patent family.
The top five filers account for 100% of the ranked applicants visible in this query’ combined total, indicating maximum concentration with no secondary tier and no commercial incumbents yet present.
| # | Applicant | Patent families | Share |
|---|---|---|---|
| 1 | Guangdong University of Technology | 2 | |
| 2 | Foshan Longwei Intelligent Equipment Co., Ltd. | 1 | |
| 3 | Xiamen University | 1 | |
| 4 | Tan Kah Kee Innovation Laboratory | 1 | |
| 5 | Xi’an Jiaotong University | 1 |
The dominance of universities and one innovation laboratory signals that this intersection of Micro-LED manufacturing and AI-driven inspection remains primarily a research problem, with industrial applicants largely absent from the patent record to date.
All filing activity falls within the past three years; given standard publication lags of 18–24 months, the true extent of current R&D activity is likely broader than the corpus reflects. Longer-window growth, applicant concentration, and technology-route coverage are therefore more reliable signals than the latest-year bar alone.
Filing activity emerged in 2023 and remains concentrated in image-processing AI branches
The annual filing trend reveals a field that did not register any patent activity before 2023, while the technology composition shows a consistent focus on image data processing and AI model branches across all applicants.
Annual filing trend
Zero filings are recorded from 2017 through 2022. Activity began in 2023 with 2 families, dipped to 1 in 2024, and returned to 2 in 2025. The 2026 count of zero reflects publication lag rather than a real cessation of activity, and should not be interpreted as a decline.
↗ Hover for values · click a bar to ask EurekaTechnology composition
All 5 patent families are classified under G06T (image data processing and generation). Four families also appear under G06N (computing based on AI models) and G06V (image and video recognition), reflecting a consistent technical approach centred on vision-based AI inspection rather than hardware repair mechanisms.
↗ 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.
一种基于深度学习的MicroLED缺陷实时检测方法
本发明涉及缺陷检测领域，更具体地说，它涉及一种基于深度学习的MicroLED缺陷实时检测方法，其技术方案要点是：S1、通过自动扫描系统采集MicroLED图像，对所述图像制作标签文件并进行数据增强；S2、将增强后的数据用于训练端到端的MicroLED缺陷检测深度神经网络中，评估并保存最优模型参数；S3、将未标注的MicroLED图像输入到检测网络中，得到MicroLED缺陷的预测坐标与类别信息。本发明优点在于建立了一个端到端的缺陷检测深度神经网络，克服了传统算法低准确率、低召回率、低鲁棒性等难题，能够在多种尺度下精确预测出MicroLED图形中存在的缺陷的类别与位置，实现MicroLED低区分度缺陷的实时检测。 (excerpt from the patent abstract)
| # | Patent | Citations |
|---|---|---|
| 1 | 一种MicroLED直显模组外观缺陷的检测方法 | 2 |
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.
Guangdong University of Technology
Holds 2 patent families, the largest position in the corpus. Its technical emphasis spans G06T 7 (image data processing) and G06V 10 (image/video recognition), each with 2 family-level classifications, supplemented by G06N 3 (AI models). Momentum is flagged as a new entrant, indicating no filing activity in the prior three-year window before its recent submissions.
families: 2Foshan Longwei Intelligent Equipment Co., Ltd.
Holds 1 patent family with a technical focus on G06N 3 (AI models) and G06T 7 (image data processing). As the only industrial equipment company in the ranking, it is the sole non-academic filer. Momentum is also flagged as a new entrant, consistent with the field’s 2023 emergence. Its presence signals early interest from the equipment-maker segment.
families: 1Frequently asked questions
The PatSnap Eureka corpus contains 5 patent families in scope for this topic as of the latest data pull.
Guangdong University of Technology leads with 2 patent families, followed by four filers — Foshan Longwei Intelligent Equipment, Xiamen University, Tan Kah Kee Innovation Laboratory, and Xi’an Jiaotong University — each holding 1 patent family.
No families are recorded before 2023. The first filings appeared in 2023, with activity continuing in 2024 and 2025. The 2026 count reflects publication lag and should not be read as a halt in activity.
Only one industrial applicant — Foshan Longwei Intelligent Equipment — appears in the ranking, with 1 patent family. No consumer electronics brands, display panel makers, or major equipment suppliers are represented in the current corpus.
All 5 patent families are filed exclusively in China. No US, European, Korean, Japanese, or PCT filings are recorded in this corpus.
G06T (image data processing and generation), G06N (computing based on AI models), and G06V (image and video recognition) account for all classified families. Hardware-oriented branches such as H01L or G05B are absent from the current record.
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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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