H2O has a total of 22 patents globally, out of which 3 have been granted. Of these 22 patents, more than 77% patents are active. The United States of America is where H2O has filed the maximum number of patents, followed by China and Europe. Parallelly, the United States of America seems to be the main focused R&D centre and also is the origin country of H2O.
H2O was founded in 2012. The company H2O.ai creates software applications. In addition to offering open-source deep learning platforms for apps and data products, the company also delivers solutions for marketing mix modeling, risk and fraud analysis, advertising technology, and consumer intelligence.
Do read about some of the most popular patents of H2O which have been covered by us in this article and also you can find H2O patents information, the worldwide patent filing activity and its patent filing trend over the years, and many other stats over H2O patent portfolio.
How many patents do the Founder and CEO of H2O have?
The Founders Cliff Click, Satish Ambati (Also CEO) have 1, 7 patents.
How many patents does H2O have?
H2O has a total of 22 patents globally. These patents belong to 5 unique patent families. Out of 22 patents, 17 patents are active.
How Many Patents did H2O 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.
| Year of Patents Filing or Grant | H2O Applications Filed | H2O Patents Granted |
| 2022 | 1 | 3 |
| 2020 | 1 | – |
| 2019 | 10 | – |
| 2018 | 10 | – |
How many H2O patents are Alive/Dead?
Worldwide Patents
How Many Patents did H2O File in Different Countries?
Countries in which H2O Filed Patents
| Country | Patent |
| United States of America | 7 |
| China | 4 |
| Europe | 4 |
| Singapore | 2 |
Where are Research Centers of H2O Patents Located?
The Research Center of H2O Patents is the United States of America.
List of H2O patents
| H2O Patent | Title |
| US20220374746A1 | Model Interpretation |
| US11475372B2 | Evolved Machine Learning Models |
| US11416751B2 | Time-Based Ensemble Machine Learning Model |
| US11386342B2 | Model Interpretation |
| US20200250477A1 | Anomalous Behavior Detection |
| US20190325335A1 | Model Interpretation |
| US20180293462A1 | Embedded Predictive Machine Learning Models |
| EP3782079A4 | Model Interpretation |
| EP3776387A4 | Evolved Machine Learning Models |
| EP3602420A4 | Embedded Predictive Machine Learning Models |
| EP3602317A4 | Time-Based Ensemble Machine Learning Model |
| CN112243514A | Evolved Machine Learning Models |
| CN112154459A | Model Interpretation |
| CN110520874A | Time-Based Ensemble Machine Learning Model |
| CN110520872A | Embedded Predictive Machine Learning Models |
| WO2020159681A1 | Anomalous Behavior Detection |
| WO2018183816A8 | Embedded Predictive Machine Learning Models |
| WO2019204072A1 | Model Interpretation |
| WO2019190696A1 | Evolved Machine Learning Models |
| WO2018183473A1 | Time-Based Ensemble Machine Learning Model |
| SG11202009597XA | Evolved Machine Learning Models |
| SG11202009599SA | Model Interpretation |