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The agent works the prompt against patents and technical literature, citing every source.
Run your analysis now →Filing growth compares 2021 (41 records) with 2024 (99) — 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 647 records in scope (CR5), not by the ranked leaders only.
This dataset tracks 647 patent families filed against semantic segmentation, scene understanding and instance segmentation, narrowed to filings whose claims or descriptions touch encoder-decoder architectures, receptive field design, boundary refinement, annotation cost reduction, or domain adaptation. The IPC scope centres on G06V10, G06T7 and G06V20 — image and video recognition, image data processing, and scene/object recognition respectively. Coverage runs from 2015 through the 2026 data cut-off, though the most recent year is necessarily undercounted: publication typically lags filing by around 18 months, so 2026 and much of 2025 will fill in as records publish.
The composition skews toward core computer-vision IPC classes rather than adjacent hardware: G06V and G06N dominate, with G06T close behind, while control (G05D), radar/positioning (G01S) and optics (G02B) each carry a small minority of records. That pattern says the claim activity here is concentrated in software-side recognition and processing methods, not in the sensor or control hardware that consumes their output.
Pick a task. Every answer cites the patents behind it.
Two views of the same 647-family dataset: how filing volume has moved year over year, and how the underlying IPC classifications distribute across the classification scheme.
Filings rose from 21 in 2017 to a peak of 144 in 2023, with the 2022 midpoint at 102 — growth that has since flattened or reversed. Because publication lags filing by roughly 18 months, the apparent 2026 drop-off to 13 partly reflects records not yet published rather than a hard stop in filing activity; even allowing for that lag, the shape shows a matured filing wave rather than an accelerating one.
G06V (543 records) and G06N (395) lead the IPC composition, with G06T (301) close behind; G06K, G06F and the smaller hardware-adjacent classes (G05D, G01S, G02B) each account for a modest share. The concentration in recognition and AI-model classes over control or optics classes indicates most claim activity sits in software methods rather than the physical systems that deploy them.
Shares are the percentage of the 647 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
This page is one run against one query. Ask Eureka your own question about semantic segmentation and scene understanding and every answer comes back with the patent numbers behind it.
Try EurekaFiled by Zhejiang University, this record describes a two-stage domain-adaptation pipeline: first reducing inter-domain differences between a labelled source domain and an unlabelled target domain, then running an iterative intra-domain adaptation step that sorts pseudo-label segmentation results by confidence and retrains on the most credible subset. The method targets remote-sensing imagery specifically, where labelled data is scarce and domain shift between sensors or geographies is common.Published 2024-10-03 — illustrates the annotation-cost and domain-adaptation branch of this landscape rather than defining its outer boundary.


| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20190057507A1 | System and method for semantic segmentation of images | 356 |
| 2 | AU2020103901A4 | Image Semantic Segmentation Method Based on Deep Full Convolutional Network and Conditional Random Field | 266 |
| 3 | US20210089807A1 | System and method for boundary aware semantic segmentation | 216 |
| 4 | US20180053056A1 | Augmented reality display device with deep learning sensors | 193 |
| 5 | US20190108639A1 | Systems and Methods for Semantic Segmentation of 3D Point Clouds | 192 |
| 6 | US20170262735A1 | Training constrained deconvolutional networks for road scene semantic segmentation | 158 |
| 7 | US20180260956A1 | System and method for semantic segmentation using hybrid dilated convolution (HDC) | 154 |
| 8 | US20190147582A1 | Adversarial learning of photorealistic post-processing of simulation with privileged information | 146 |
| 9 | US9953236B1 | System and method for semantic segmentation using dense upsampling convolution (DUC) | 146 |
| 10 | US20160358337A1 | Image semantic segmentation | 146 |
Citation counts inside a searched corpus favour older, earlier-published records — treat this as a signal of influence on the field's terminology and framing, not of current commercial relevance.
Each row carries its publication number; clicking a row searches Eureka by that number.
When you want the answer in the next five minutes.
The agent works the prompt against patents and technical literature, citing every source.
Run your analysis now →When it has to run inside your own pipeline.
Patent search, landscape analysis and assignee resolution as MCP tools. Drop them into any agent framework, or call REST directly.
Browse MCP servers →Read together, the trend, the geography and the citation table point to a field whose foundational claims are largely staked, with activity now concentrated in refinement and domain-specific adaptation.
Filings rose steadily from 21 in 2017 to 144 in 2023 before declining. Combined with the 18-month publication lag, this suggests the core architectural claims — encoder-decoder variants, receptive-field tuning — were staked out earlier in the window, and later filings increasingly target refinements or domain-specific applications.
China's receiving office accounts for 304 records against 195 for the United States, with Europe, WIPO/PCT, Australia and India trailing well behind. Any clearance or monitoring exercise that stops at USPTO and EPO records is screening against the smaller of the two largest filing pools.
The most-cited record in this corpus, a US filing on semantic segmentation of images, carries 356 citations — well ahead of the next entries. High citation counts here reflect early publication and broad terminology, not necessarily current enforceability or commercial weight.
Only eight co-assignee pairs appear across the dataset, and the strongest link — a joint industry-university pairing — recurs just three times. Co-development is the exception here, not the norm; most families trace to a single assignee.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to semantic segmentation and scene understanding, with the prior art for and against each one.
The assignee list spans large electronics manufacturers, autonomous-vehicle specialists and Chinese universities, but the momentum data suggests a pause across the highest-activity names rather than a single leader pulling further ahead.
Every one of the six assignees with the strongest recent-year momentum recorded zero filings in the most recent year captured. Given the 18-month publication lag this likely overstates a genuine slowdown, but it is consistent with a field where the leading players are consolidating rather than expanding their claim positions.
The assignee base mixes university filers such as Zhejiang University with global electronics names and autonomous-vehicle-focused entities. That mix suggests both applied commercial development and continued academic-side foundational work are still active, even where commercial filing has slowed.
The strongest co-assignee link in the dataset pairs a German automotive-technology firm with a Chinese university, recurring three times — modest in absolute terms but notably more frequent than any other pairing. It points to at least one active, structured industry-academic channel rather than purely incidental co-filing.
| Assignee | Recent year | YoY |
|---|---|---|
| Adobe Inc. | 0 | — |
| Magic Leap, Inc. | 0 | — |
| Zhejiang University | 0 | — |
| Samsung Electronics Co., Ltd. (South Korea) | 0 | — |
| Zoox, Inc. | 0 | — |
| Intel Corporation | 0 | — |
| Robert Bosch GmbH (Germany) | 0 | — |
| Tencent Technology (Shenzhen) Co., Ltd. | 0 | — |
The dataset points to specific next steps depending on whether the goal is clearance, competitive tracking, or identifying open claim space.
With 304 of 647 records receiving in China against 195 in the United States, any freedom-to-operate review limited to USPTO and EPO records is working from the smaller filing pool. Direct screening of Chinese-language filings and their English-language equivalents is necessary for a complete picture.
Explore filings in Eureka →Because publication lags filing by roughly 18 months, the apparent decline from the 2023 peak of 144 filings is partly an artefact of records not yet public. Re-checking the 2024-2026 trend in twelve to eighteen months will give a truer read on whether the field is genuinely cooling.
Track filing trends in Eureka →Domain adaptation across sensor types and annotation-cost reduction via pseudo-labelling both show thinner density than core encoder-decoder claims. A targeted claim search in these branches, rather than the crowded core, is more likely to surface genuine white space.
Search white space in Eureka →This dataset tracks 647 patent families published between 2015 and the 2026 data cut-off, scoped to filings whose claims or descriptions cover encoder-decoder architectures, receptive field design, boundary refinement, annotation cost, or domain adaptation, within the G06V10, G06T7 and G06V20 IPC classes. That figure counts families, which is a fairer measure than raw document counts because it neutralises repeat continuation filings and multi-jurisdiction duplicates of the same invention. Because publication lags filing by roughly 18 months, the true count for 2024-2026 filings will continue to rise as more records publish.
China is the leading receiving office in this dataset with 304 records, ahead of the United States at 195, and well ahead of Europe, WIPO/PCT, Australia and India, which each account for a small fraction of the total. This matters for competitive tracking and clearance work alike: a review that only checks USPTO and EPO filings is screening against the second-largest pool, not the largest. Firms operating or selling into China should treat Chinese-language filings as a primary rather than supplementary search target.
No — filings rose from 21 in 2017 to a peak of 144 in 2023, then declined, with the 2022 midpoint at 102 suggesting the growth phase had already begun flattening before the peak. The drop shown for 2025 and 2026 is partly an artefact of the roughly 18-month gap between filing and publication, so the most recent two years will fill in somewhat as more records publish. Even accounting for that lag, the overall shape is a matured filing wave rather than an accelerating one, consistent with core architectural claims having been staked out earlier in the window.
The dataset's assignee ranking spans global electronics and AI firms, autonomous-vehicle specialists, and Chinese universities, with filing density concentrated in a relatively small number of names against a long tail of single-filing entrants. Notably, the six assignees showing the strongest recent-year momentum in aggregate recorded zero filings in the most recent tracked year, which — allowing for publication lag — suggests consolidation of existing claim positions rather than active expansion at the moment. Co-filing is uncommon: only eight co-assignee pairs appear across all 647 families, with the strongest recurring just three times.
Filing density is heaviest around core encoder-decoder architectures and boundary-refinement methods, classified primarily under G06V and G06T. Thinner coverage appears in cross-sensor domain adaptation, point-cloud instance boundary refinement, annotation-cost reduction through pseudo-labelling, and remote-sensing-specific cross-domain segmentation — branches that show up in the dataset but with fewer dedicated filings than the core architectural claims. High density in the core does not mean those methods are settled science, only that the claim space there is already occupied; the adjacent branches are where a first claim is more likely to clear without heavy prior art.
Go past this page: query the whole semantic segmentation and scene understanding corpus yourself, in your own scope.
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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.