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The agent works the prompt against patents and technical literature, citing every source.
Run your analysis now →A 9-family dataset on shared-representation multi-task learning networks, spanning filers, IPC classes and filing offices.
11 published records collapse into 9 simple (DOCDB) families; the difference is the same invention filed in more than one country. In force = 6 of 11 records still granted and maintained at the data cut-off; the rest are pending, withdrawn, refused or lapsed.
This page tracks patent filings matching the search string ("multi-task learning" OR "multi task learning") AND ("shared representation" OR "shared encoder") AND ("neural network") across title, abstract and claims. The search carries no date limit, so every matching record regardless of filing date is in scope, and the result set resolves to 9 simple patent families drawn from 11 published records. At retrieval, 6 of the 11 records (55%) were granted and still in force.
This is a small pool: 9 families is not enough to support claims about concentration, white space or trajectory. Treat the figures on this page as a starting point for a targeted search, not a map of the field.
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. Filings held by named individuals rather than organisations are removed where they can be identified (17 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 7 of the 7 filers returned for this search; the data endpoint returns at most 100 entities, so even that 7 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.
The figures below are counted two ways, published records for the filing trend and receiving offices, simple patent families for the filer table and IPC composition, and the two units are not interchangeable.
The series runs from 2019 (1 record) to 2026 (1 record), with a peak of 3 records in 2024. Records from 2022 onward are still filling in as later filings publish, so the recent end of the line understates eventual totals and should not be read as a slowdown or a peak already passed.
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 6 of 9 families (66.7%), the highest share of any subclass. G06T (image data processing) follows at 3 of 9 (33.3%), with G06F, G06V and G16H each on 2 of 9 (22.2%). Families can carry more than one IPC class, so these shares add up to more than 100%.
Shares are the percentage of the 9 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 shared representation network for multi task learning patent landscape and every answer comes back with the patent numbers behind it.
Try EurekaThe abstract describes an MTL neural network architecture for predicting disease traits from whole-genome data. The model uses a shared latent genomic representation, with each trait predicted from that shared representation via a task-specific hidden layer; the abstract reports higher predictive performance than single-task learning models for both cancer and non-cancer diseases.This is the first record returned under the database's default ordering, not the most cited, newest or broadest filing in the set, and nothing about the field should be inferred from it alone.


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 →These cards report figures from the dataset as counted. None of them should be read as a ranking of strength, strategy or field importance.
ZHEJIANG LAB, 42DOT INC, XI AN JIAOTONG UNIV, INTERNATIONAL BUSINESS MACHINE CORPORATION, HUAQIAO UNIVERSITY and two further entities are each named on 1 of the 9 simple patent families in scope (11.1%). No single filer is named on more than one family, so there is no top-ranked entry to point to.
All 7 of the 7 listed filers hold 2 or fewer simple patent families each. The listed rows are organisations only; filings held by named individuals are excluded from the table but remain inside the 9-family total.
G06N (computing based on AI models) is present on 6 of the 9 simple patent families (66.7%), ahead of G06T (image data processing) at 3 of 9 (33.3%). Because a family can carry more than one IPC class, these shares are not mutually exclusive and do not sum to 100%.
The 11 published records were filed across three offices: United States (6), China (3) and India (2). This counts where each record was filed, not where any filer is based, and the figures sum to the record total rather than the family total.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to shared representation network for multi task learning patent landscape, with the prior art for and against each one.
The figures above describe what this search returned. Going further means reading claims directly and widening or narrowing the search string to test whether the picture holds.
Family and IPC counts cannot establish claim scope. Before drawing any conclusion about what a filing covers, read the granted claims of the specific record in question.
Open Patsnap Eureka →A 9-family result is sensitive to wording. Running adjacent phrasing for shared encoders or multi-task heads may surface records this exact string did not catch.
Refine the search in Patsnap Eureka →This search returns 9 simple patent families, represented by 11 published records, with no date limit applied. That means every matching record regardless of filing date is included, not a fixed recent window. Because the pool is small, it should be treated as a starting point for a targeted search rather than a complete census of filings using shared representations for multi-task learning.
Seven filers are returned by this search, listed as organisations only (filings held by named individuals are excluded from the table but remain in the 9-family total). Seven of these filers are tied at the top with 1 family each, including Zhejiang Lab, 42dot Inc, Xi'an Jiaotong University, IBM and Huaqiao University, so there is no single filer ahead of the rest. Filer names are not rolled up to corporate group, so a parent and its subsidiaries would appear as separate rows if both were present.
The IPC composition is led by G06N (computing based on AI models), present on 6 of the 9 simple patent families (66.7%). G06T (image data processing and generation) follows at 3 of 9 families (33.3%), with G06F, G06V and G16H each on 2 of 9 (22.2%). Because a single family can be classified under more than one subclass, these shares add up to more than 100% and should not be read as a breakdown of the whole.
The published-record series runs from 2019 (1 record) through 2026 (1 record), with a peak of 3 records in 2024. Records from 2022 onward are still filling in as later filings publish, so the most recent years understate their eventual totals and no slowdown or acceleration can be read from them. With only a handful of records per year, year-to-year movement in this series is not a reliable trend signal.
The abstract of US20240379240A1, filed by The Board of Regents of the University of Oklahoma, describes a multi-task learning neural network that predicts multiple disease traits from a shared latent genomic representation, with each trait predicted via a task-specific hidden layer. This is a description of what the abstract discusses, not a reading of the granted claims. An abstract and a family count cannot establish claim scope or infringement risk; that requires reading the granted claims and prosecution history directly.
Go past this page: query the whole shared representation network for multi task learning 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:(("multi-task learning" OR "multi task learning") AND ("shared representation" OR "shared encoder") AND ("neural network"))
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. One subclass (G06N) carries 66.7% of the patent families, so this search returned a classification-tight set.
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 11 records and 9 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 (17 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 7 of the 7 filers returned, ordered by count alone; the data endpoint returns at most 100 entities, so even that 7 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 2026-10-22.
Legal status. "In force" counts documents whose legal status at retrieval was granted and maintained (6 of 11). 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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