Eureka on the web
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 →A dataset of 128 patent families on deep learning classification of space objects and debris, with filer spread and IPC composition.
228 published records collapse into 128 simple (DOCDB) families; the difference is the same invention filed in more than one country. VELLORE INST OF TECH CHENNAI is named on 3 of the 128 patent families in scope. One patent family can name several filers, so these figures are not slices of a divided whole and do not add up. In force = 77 of 228 records still granted and maintained at the data cut-off; the rest are pending, withdrawn, refused or lapsed.
This landscape draws on a search for records that combine deep learning or convolutional neural network methods with classification of space objects or debris. The search string sets no date limit, so the 128 simple patent families and 228 published records in scope reflect everything the string matches, not a fixed filing window.
Filer names here are organisations only, filings credited to individual inventors sit inside the totals and shares but are not shown as rows. No row has been rolled up to a parent corporate group, so related-looking names are counted and reported separately throughout this page.
Ordered by volume only, not by importance or claim strength. Counted in simple (DOCDB) patent families, so one invention filed in several countries counts once. Names come from Patsnap's standardised assignee field as recorded on each document; subsidiaries and group companies are not merged. The icon before each name is read from the name itself (words such as University, Institute or Ltd); the data carries no organisation-type field, so treat it as a reading aid rather than a classification. 2 rows in this table start with the same name (VELLORE INST OF TECH CHENNAI, VELLORE INSITUTE OF TECH). They are left as separate rows, so the largest-filer figure above is per legal entity, not per group. Rows that look related still cannot be added together: one family can name several of them. Counted one by one, those rows between them cover more than the largest single row does, so the largest single row is not necessarily the largest filer in this field. How much those rows cover together is not something this dataset can say: one family can name several of them, so their counts cannot be added. Every ranking statement on this page is per individual filer row, never per group. Filings held by named individuals rather than organisations are removed where they can be identified (12 here); that identification is not exhaustive, so an individually held filing can still appear below. Either way those filings stay inside the totals above. This table shows the top 30 of the 88 filers returned for this search; the data endpoint returns at most 100 entities, so even that 88 is not the complete set of filers. A single family can name more than one filer, so these counts overlap and do not add up to the total.
Pick a task. Every answer cites the patents behind it.
Two views of the same 128 families: when the underlying records were filed, and which IPC subclasses they carry.
Records climb from 2 in 2016 to a peak of 42 in 2025, with 28 recorded for 2026 so far. The years from 2022 are still filling in as later-filed records publish, so the most recent part of the line understates eventual totals and should not be read as a decline.
Counted in published records by earliest filing year, not de-duplicated into families. A filing is counted in the year it was filed, but it only becomes visible after it publishes: roughly 18 months for most routes, and up to 30 months where a PCT application enters national phase. 2022 onwards are therefore still filling in and are shaded; no growth figure on this page uses them. Because this series counts documents rather than inventions, a rise can mean more inventions or the same inventions filed in more countries; this dataset cannot separate the two.
G06N (computing based on AI models) appears on 47.7% of the 128 families and G06V (image/video recognition) on 43.8%, the two largest shares in the set. G06T (image data processing) and G06K (data recognition) follow at 29.7% and 15.6%. These shares add to more than 100% because a single family can carry more than one IPC subclass.
Shares are the percentage of the 128 patent families in scope. One patent family 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 space object classification using deep learning image recognition patent landscape and every answer comes back with the patent numbers behind it.
Try EurekaThe abstract describes a clinical support system with a processor and display that receives image data of multiple cells from a human or animal subject, splits it into per-cell subsets, and applies a trained deep learning neural network, built from convolutional layers and a bottleneck layer, to each subset.This is the record the database returns first under its default ordering, not a ranking by citations, grant status or relevance. It is shown to illustrate what one matching document claims, not what is typical of the field.


Selected as the first record the search returns under the database default order, then shown in full. It is not ranked by citations, claim breadth or commercial importance, and it is not a judgement about which filing in this space matters most.
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 →Three figures worth sitting with before drawing conclusions from the ranking table.
Three filers, VELLORE INST OF TECH CHENNAI, YOUNGS ENG CO LTD and NIMS UNIV RAJASTHAN JAIPUR, are each named on 3 of the 128 simple patent families (2.3%). The list contains 88 filers in total; 85 of them hold 2 or fewer families each.
G06N (computing based on AI models) sits on 47.7% of the 128 families and G06V (image/video recognition) on 43.8%. G06T (image data processing) follows at 29.7% and G06K (data recognition) at 15.6%. Shares exceed 100% in total because families can carry more than one subclass.
At retrieval, 77 of the 228 published records (34%) were granted and still in force. The remainder cover pending, withdrawn, refused, lapsed or undetermined status, which this dataset does not break out further.
Among 17 receiving offices returned, India shows 73 published records and the United States 56, followed by WIPO (PCT) at 26, Europe (EPO) at 24, China at 15 and Australia at 11. These are counted per office a record was filed in, so they sum to more than the family total.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to space object classification using deep learning image recognition patent landscape, with the prior art for and against each one.
The figures on this page describe what the search returned. Narrowing the question or pulling full claim sets is the next step for any of these threads.
A family count says nothing about claim scope. Pulling the granted claim set for any of the filers named here is the only way to see what is actually protected.
Open Eureka to read claims →G06N and G06V carry the largest shares of this set, but the trend shows the most recent years are still filling in. Re-running this search periodically will show how that composition moves as later filings publish.
Set up tracking in Eureka →Only 77 of 228 records were granted and in force at retrieval. Confirming current status on any one filing before acting on it avoids working from a lapsed or withdrawn document.
Verify status in Eureka →This search returns 128 simple patent families and 228 published records covering deep learning or convolutional neural network methods applied to classifying space objects or debris. The search string carries no date limit, so this count reflects everything matching the string rather than a fixed filing window. Because one invention can be filed in several countries, the 228 record count is higher than the 128 family count, and the two should not be used interchangeably.
No single filer holds the largest share: three filers, VELLORE INST OF TECH CHENNAI, YOUNGS ENG CO LTD and NIMS UNIV RAJASTHAN JAIPUR, are tied at 3 families each out of 128. The listed filers run to 88 entities, and 85 of them hold 2 or fewer families each, so filing activity in this set is spread across a long tail rather than concentrated at the top. Filer rows are not rolled up to corporate group, so a parent and its subsidiaries would appear as separate rows if both were present.
The largest IPC subclasses in this set are G06N (computing based on AI models), on 47.7% of the 128 families, and G06V (image/video recognition), on 43.8%. G06T (image data processing and generation) follows at 29.7% and G06K (data recognition and presentation) at 15.6%. These shares add to more than 100% because a single family can carry more than one IPC subclass, so they describe overlapping technology tags rather than a partition of the dataset.
At the retrieval date, 77 of the 228 published records (34%) were granted and still in force. The remaining records cover pending, withdrawn, refused, lapsed or undetermined status, and this dataset does not break that remainder down further. Legal status changes continuously, so any figure quoted here is a snapshot rather than a stable count, and it says nothing about the scope or validity of what is actually claimed.
The search returned 17 receiving offices in total. The largest by published record count are India at 73 and the United States at 56, followed by WIPO's PCT route at 26, the European Patent Office at 24, China at 15 and Australia at 11. These figures count where applications were filed, not where any filer is based, and they are counted per office so they do not sum to the total record count.
Go past this page: query the whole space object classification using deep learning image recognition patent landscape corpus yourself, in your own scope.
Every answer comes back with patent numbers you can open.
Search. Every figure on this page comes from one Patsnap search, run once and not edited afterwards. This is the exact search string:
TAC:(("deep learning" OR "convolutional neural network") AND classif* AND ("space object*" OR debris))
Reading the field codes: TAC searches the title, abstract and claims of each record, not the full description. The search string sets no date limit, so the scope is every matching record in the database regardless of when it was filed. It is not restricted to recent years. How this string was built matters for how far the counts can be pushed. It was generated in batch from a topic list rather than hand-crafted for this page, and the result set is not manually screened. A keyword search over title, abstract and claims both over-captures (unrelated filings that happen to use the same words) and under-captures (the same invention written in different words). This page cannot measure either effect, so read every figure here as what this particular string returned, not as what exists in the field. No subclass carries much of this set: the most common one is on 47.7% of the patent families and the set spans 75 subclasses in all. That can mean the technology genuinely sits across several areas, or that the search string is also matching adjacent work that happens to use the same words. This page cannot separate the two, and the result set is not screened by hand.
What is being counted. Two different units appear on this page and they are not interchangeable. The filer list and the technology split are counted in simple (DOCDB) patent families: one invention counts once however many countries it was filed in. The total, the jurisdiction split and the filing-year chart are counted in published records: the same invention counts once per publication. For this search that is 228 records and 128 families. Record counts reward filing in many jurisdictions; family counts do not. Shares are only ever taken against a denominator in the same unit, and each one names it.
Who the filers are. Names come from Patsnap's standardised assignee field, as recorded on the documents themselves. They are not rolled up to corporate group: a parent and its subsidiaries appear as separate entries, and a portfolio acquired from another company stays under the name on the document unless the assignment is reflected there. This list is meant to carry organisations, not people. Filings held by a named individual are identified by matching the assignee against the inventors on the same search, and, for names written in Chinese, Japanese or Korean characters, by the name carrying no organisational suffix; those are removed from the list (12 of them here). Neither test is exhaustive: the inventor list it matches against is itself capped, and a personal name written in Latin script with no organisational wording in it cannot be told apart from a short company name by rule. So an individually held filing can still appear in the list. Those filings stay inside every total and share on this page either way, so the listed entries can add up to less than the total. A record shown in full further down still names its assignee exactly as the patent office records it, including where that is a person: that name is how the record is identified and checked. What is left out of the ranking is people as ranked entries, not people as cited sources. The table shows the top 30 of the 88 filers returned, ordered by count alone; the data endpoint returns at most 100 entities, so even that 88 is not every filer. One patent family can name several filers and each is counted separately, so these rows are not slices of a divided whole: adding them together over-counts, and this page deliberately publishes no combined "top five" share for that reason. See every filer, individuals included
Dates. The year chart counts by earliest filing year, not publication year. A filing only becomes visible once it publishes: roughly 18 months for most routes, and up to 30 months where a PCT application enters national phase. Measured across these pages, the year four back from today still holds only about 89% of its eventual count. 2022 onwards are therefore shaded on the chart as still filling in. The data behind this page was retrieved on 2026-10-20.
Legal status. "In force" counts documents whose legal status at retrieval was granted and maintained (77 of 228). Everything else is pending, withdrawn, refused, lapsed or not determinable, and those states are not broken out here because the underlying codes do not separate them cleanly. Legal status changes continuously and is reported per document, not per family. Get the status breakdown in Eureka
Classification. The technology split uses IPC subclasses (four characters, for example H01M). A single record usually carries several, so the shares add up to more than 100% and a record can appear in more than one bar. Subclass is a coarse grouping; it describes the field a record was classified into, not what the claims cover. Break the classes down to subgroup level
Who wrote the words. The tables, charts, counts, shares and patent numbers on this page are produced directly from the search result by a program; none of them are written or estimated by a language model. The surrounding prose, headings, FAQ answers and the answers in the question panel are drafted by a language model that is given those same figures and is not permitted to introduce others. Prose can still mis-describe a correct figure, so where the two disagree, the tables and charts are the record.
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 limitations that apply to every figure above. Recent filings are under-counted, because an application is not public until roughly 18 months after filing and database loading adds further delay. The search string defines and constrains everything shown: records that use different terminology, or that sit outside the fields searched, are absent and cannot be counted. Applicant names, classification codes, legal-status codes and citation records are taken as recorded and may contain errors, duplicates, omissions or stale entries. Ordering by volume is not a measure of quality, scope, enforceability or commercial value, and a larger portfolio is not a stronger one. 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, validity or infringement search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. No attorney-client relationship is created by it. Any patent, commercial, or strategic decision should be verified independently against the primary records 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.
The question panel. The answers behind the suggested questions on this page were written when the page was generated, not in response to your click. The progress steps that play before an answer appears are an animation of how that analysis was done; nothing is being searched or computed at that moment. Typing your own question instead opens Patsnap Eureka, which does run it live.
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.
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