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The agent works the prompt against patents and technical literature, citing every source.
Run your analysis now →A 2-family dataset on tensor network contraction acceleration patents, with filer and filing data as a starting point for deeper search.
2 published records collapse into 2 simple (DOCDB) families; the difference is the same invention filed in more than one country.
This page reports the results of a single search for tensor network contraction algorithm acceleration, built around a search string requiring both a tensor-network contraction concept and a term for acceleration or hardware. The search string sets no date limit, so the records returned span whatever filing dates exist in the index for this exact term combination, not a chosen window of years. G06N (computing based on AI models) appears on both families in scope, with G06F and H03K each appearing on one.
Only 2 simple patent families and 2 published records matched this search string. That is a small pool by any measure, and the figures on this page describe only what this particular search returned, they are a starting point for a more targeted search rather than a map of a technology 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 (1 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 1 of the 1 filers returned for this search; the data endpoint returns at most 100 entities, so even that 1 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 cover every record this search string returned: 2 published records across 2 receiving offices, and 2 simple patent families split across three IPC subclasses.
Published records by earliest filing year show 2 records in 2025 and 0 so far in 2026. Filings from 2022 onward are still filling in as later publications arrive, so recent years understate the eventual count and no trend judgement can be drawn from two data points.
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 both of the 2 simple patent families in scope (100.0%). G06F (electric digital data processing) and H03K (pulse technique & logic circuits) each appear on 1 of the 2 families (50.0%). Because a single family can carry more than one classification, these shares add up to more than 100%.
Shares are the percentage of the 2 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 tensor network contraction algorithm acceleration patent landscape and every answer comes back with the patent numbers behind it.
Try EurekaThe abstract describes a method for training a transformer model on a resource-constrained device. It encodes input tokens into hidden representations using tensor-compressed embedding tables stored in tensor-train-matrix (TTM) format, performs forward propagation through tensor-compressed layers with tensor-train (TT) decomposed weight matrices without reconstructing dense weight matrices, computes gradients of loss from the model outputs, and backpropagates.This is the first record returned under the database's default ordering, not a ranking by citations, novelty or breadth. What the granted claims actually require has to be read from the claim set itself, not inferred from this abstract.


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 from this search, reported as counted, no ranking or market read should be drawn from a 2-family pool.
This search returned 2 simple patent families and 2 published records in total. Any share or count quoted elsewhere on this page is a fraction of that same small total, not of a wider field.
RGT UNIV OF CALIFORNIA is named on 1 of the 2 simple patent families in scope, a 50.0% share. The filer list returned by this search contains organisations only; filings held by named individuals are removed from the list but remain inside the total and share figures.
G06N (computing based on AI models) appears on both of the 2 simple patent families in scope. G06F and H03K each appear on 1 of the 2 families. Because one family can carry several IPC subclasses, these shares add up to more than 100% and cannot be summed into a single figure.
The search returned 2 receiving offices in total: Canada with 1 published record and WIPO (PCT) with 1 published record. This counts where applications were filed, not where any filer is based.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to tensor network contraction algorithm acceleration patent landscape, with the prior art for and against each one.
A 2-family result is a narrow starting point. Broadening or adjusting the search terms, and reading the full claim sets of any record of interest, is the next step before drawing conclusions.
An abstract and a family count cannot establish what a patent's claims cover. Pull the granted claim set for any record of interest before forming a view on scope or freedom to operate.
Open the record in EurekaA 2-family result from this exact term combination likely misses adjacent phrasing used elsewhere in tensor-network or tensor-decomposition filings. Running variant terms is the way to test that.
Refine the search in EurekaThis specific search string returned 2 simple patent families and 2 published records, with no date limit applied. That is a small, narrow result and it should be read as what this exact term combination matched, not as a full count of all patenting activity touching tensor network contraction. A broader or differently-worded search run on the same underlying index could return a different set.
Within this dataset, RGT UNIV OF CALIFORNIA is named on 1 of the 2 simple patent families in scope, a 50.0% share. The filer list returned by the search contains organisations only, so filings held by named individuals are counted in the totals but not shown as named rows. With only 2 families in the entire dataset, this single-row figure describes this search result rather than any wider pattern of ownership.
The abstract for WO2026128915A1 describes a method for training a transformer model on a resource-constrained device using tensor-compressed embedding tables in tensor-train-matrix format and tensor-train decomposed weight matrices, avoiding reconstruction of dense weight matrices during forward propagation. It was returned as the first record under the database's default ordering, not selected for citation count, novelty or breadth. What its granted claims actually require, and how far they reach, can only be determined by reading the claim set itself rather than the abstract.
No growth rate is shown on this page. The filing trend here covers only two data points, 2 published records in 2025 and 0 so far in 2026, which is too thin a series to read any trajectory from. Filings from 2022 onward are also still filling in as later publications arrive, so even a complete series over more years would understate recent activity until it has had time to publish fully.
In this dataset, G06N (computing based on AI models) appears on both of the 2 simple patent families in scope, a 100.0% share. G06F (electric digital data processing) and H03K (pulse technique & logic circuits) each appear on 1 of the 2 families, a 50.0% share apiece. Because a single family can carry more than one IPC subclass, these shares add up to more than 100% and are each measured against the same 2-family total rather than against each other.
The search returned 2 receiving offices across the 2 published records in scope: Canada with 1 record and WIPO (PCT) with 1 record. This reflects where the applications were filed, not where any filer is based, the dataset carries no nationality or location information about any filer. Receiving-office counts here are measured in published records, which sum to the record total rather than the family total.
Go past this page: query the whole tensor network contraction algorithm acceleration 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:(("tensor network" AND contraction) AND (algorithm OR computation) AND (accelerat* OR hardware))
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 100.0% 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 2 records and 2 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 (1 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 1 of the 1 filers returned, ordered by count alone; the data endpoint returns at most 100 entities, so even that 1 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 2026-10-15.
Legal status. Legal status was not available for this search, so no in-force figure is shown. 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.
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