SambaNova Systems, Inc. was founded in 2017 by Kunle Olukotun and is doing business in AI innovation which empowers organizations to deploy best-in-class solutions for computer vision, natural language processing, recommendation, and AI for science with confidence. As of October 2021, SambaNova Systems has a market valuation greater than $5 Billion.
SambaNova Systems has a total of 49 patents globally, out of which 7 has been granted. Of these 49 patents, 100% patents are active. USA is where SambaNova Systems has filed maximum number of patents, followed by Taiwan and Canada and it also seems reasonable as the biggest market for SambaNova Systems in the United States, it has generated an annual revenue of $10-$50 million. Parallelly, United States seems to be the main focused R&D center and is also the origin country of SambaNova Systems.
Do read about some of the most popular patents of SambaNova Systems which have been covered by us in this article and also you can find SambaNova Systems’s patents information, the worldwide patent filing activity and its patent filing trend over the years, and many other stats over SambaNova Systems’s patent portfolio.
How many patents does SambaNova Systems have?
SambaNova Systems has a total of 49 patents globally. These patents belong to 15 unique patent families. Out of 49 patents, 49 patents are active.
How many SambaNova Systems patents are Alive/Dead?
Worldwide Patents
Patent Families
How Many Patents did SambaNova Systems File Every Year?
Are you wondering why there is a drop in patent filing for the last two years? It is because a patent application can take up to 18 months to get published. Certainly, it doesn’t suggest a decrease in the patent filing.
| Years of Patent Filing or Grant | SambaNova Systems Application Filed | SambaNova Systems Patents Granted |
| 2021 | 2 | 3 |
| 2020 | 25 | 4 |
| 2019 | 20 | – |
| 2018 | 2 | – |
How Many Patents did SambaNova Systems File in Different Countries?
Countries in which SambaNova Systems Filed Patents
| Country | Patents |
| United States Of America | 19 |
| Taiwan | 12 |
| Canada | 3 |
| China | 1 |
Where is Research Center of SambaNova Systems Patents Located?
10 Best SambaNova Systems Patents
US20200257643A1 is the most popular patent in the SambaNova Systems portfolio. It has received 2 citations so far from companies like Samsung Electronics Co Ltd., Nvidia Corporation and Intel Corporation.
| Publication Number | Citation Count |
| US20200257643A1 | 2 |
| WO2020227671A1 | 1 |
| US11080227B2 | 0 |
| US11055141B2 | 0 |
| TWI719788B | 0 |
| TWI714448B | 0 |
| US10831507B2 | 0 |
| US10768899B2 | 0 |
| US10698853B1 | 0 |
| US20210271630A1 | 0 |
What Percentage of SambaNova Systems US Patent Applications were Granted?
SambaNova Systems (Excluding its subsidiaries) has filed 15 patent applications at USPTO so far (Excluding Design and PCT applications). Out of these, 4 have been granted leading to a grant rate of 100%.
Which Law Firms Filed Most US Patents for SambaNova Systems?
| Law Firm | Total Application | Success Rate |
| Haynes Beffel & Wolfeld Llp | 15 | 100.00% |
“We are at the cusp of a fairly large shift in the computer industry. It’s been driven by AI, but at a macro level, over the next 20-30 years, the change is going to be bigger than AI and machine learning,” said Rodrigo Liang, Co-founder and CEO, SambaNova Systems.
SambaNova Systems strives to create the next generation of computing via offering AI innovations to organizations around the world. It has also become world’s best funded AI startup.
List of SambaNova Systems Patents
| Publication Number | Title (English) |
| US11080227B2 | Compiler flow logic for reconfigurable architectures |
| US11055141B2 | Quiesce reconfigurable data processor |
| TWI719788B | Virtualization of a reconfigurable data processor |
| TWI714448B | Matrix normal/transpose read and a reconfigurable data processor including same |
| US10831507B2 | Configuration load of a reconfigurable data processor |
| US10768899B2 | Matrix normal/transpose read and a reconfigurable data processor including same |
| US10698853B1 | Virtualization of a reconfigurable data processor |
| US20210271630A1 | Compiler Flow Logic for Reconfigurable Architectures |
| US20210271519A1 | Quiesce reconfigurable data processor |
| TW202132972A | Look-up table with input offsetting |
| CN113272796A | Configuration offload of reconfigurable data processor |
| TW202127270A | Performance estimation-based resource allocation for reconfigurable architectures |
| TW202127269A | Efficient execution of operation unit graphs on reconfigurable architectures based on user specification |
| TW202127238A | Compiler flow logic for reconfigurable architectures |
| US20210216873A1 | Computationally efficient softmax loss gradient backpropagation |
| WO2021126530A1 | Computational units for element approximation |
| US20210182021A1 | Computational Units for Element Approximation |
| WO2021108328A1 | Computational units for batch normalization |
| TW202121163A | Computation units for functions based on lookup tables |
| US20210157550A1 | Computational Units for Batch Normalization |
| WO2021102000A1 | Look-up table with input offsetting |
| US20210149634A1 | Look-up table with input offsetting |
| TW202117547A | Virtualization of a reconfigurable data processor |
| TW202115593A | Sigmoid function in hardware and a reconfigurable data processor including same |
| TW202115575A | Quiesce reconfigurable data processor |
| WO2021067318A1 | Computation units for functions based on lookup tables |
| US20210096816A1 | Computation units for functions based on lookup tables |
| WO2021055234A1 | Efficient execution of operation unit graphs on reconfigurable architectures based on user specification |
| WO2021055233A1 | Performance estimation-based resource allocation for reconfigurable architectures |
| US20210081769A1 | Performance Estimation-Based Resource Allocation for Reconfigurable Architectures |
| US20210081691A1 | Efficient Execution of Operation Unit Graphs on Reconfigurable Architectures Based on User Specification |
| WO2021046274A1 | Sigmoid function in hardware and a reconfigurable data processor including same |
| US20210064568A1 | Sigmoid function in hardware and a reconfigurable data processor including same |
| US20210055940A1 | Efficient configuration of a reconfigurable data processor |
| WO2021026489A1 | Compiler flow logic for reconfigurable architectures |
| TW202103003A | Control flow barrier and reconfigurable data processor |
| WO2021007131A1 | Quiesce reconfigurable data processor |
| US20200356523A1 | Control flow barrier and reconfigurable data processor |
| WO2020227671A1 | Control flow barrier and reconfigurable data processor |
| TW202032383A | Configuration load and unload of a reconfigurable data processor |
| US20200257643A1 | Virtualization of a reconfigurable data processor |
| WO2020159775A1 | Matrix normal/transpose read and a reconfigurable data processor including same |
| WO2020142623A1 | Virtualization of a reconfigurable data processor |
| CA3125707A1 | Virtualization of a reconfigurable data processor |
| WO2020106769A1 | Configuration unload of a reconfigurable data processor |
| WO2020106768A1 | Configuration load of a reconfigurable data processor |
| CA3120684A1 | Configuration unload of a reconfigurable data processor |
| CA3120683A1 | Configuration load of a reconfigurable data processor |
| US20200159692A1 | Configuration unload of a reconfigurable data processor |