Tractable has a total of 49 patents globally, out of which 8 have been granted. Of these 49 patents, more than 57% patents are active. The United States of America is where Tractable has filed the maximum number of patents, followed by United Kingdom and Australia. Parallelly, United Kingdom seems to be the main focused R&D centre and also is the origin country of Tractable.
Tractable was founded in 2014 by Adrien Cohen, Alexandre Dalyac and Razvan Ranca. Tractable AG develops software solutions. Tractable Ltd. provides online solutions. The Company focuses on insurance, medical imaging, and preventive maintenance services. Tractable renders services in the United States and the United Kingdom.
Do read about some of the most popular patents of Tractable which have been covered by us in this article and also you can find Tractable patents information, the worldwide patent filing activity and its patent filing trend over the years, and many other stats over Tractable patent portfolio.
How many patents does the Founder and CEO of Tractable have?
The founders Adrien Cohen have 0 patents, Alex Dalyac have 0 patents and Razvan Ranca has 36 patents and CEO Alex Dalyac has 0 patents.
How many patents does Tractable have?
Tractable has a total of 49 patents globally. These patents belong to 4 unique patent families. Out of 49 patents, 28 patents are active.
How Many Patents did Tractable 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 149 months to get published. Certainly, it doesn’t suggest a decrease in the patent filing.
|Year of Patents Filing or Grant||Tractable Applications Filed||Tractable Patents Granted|
How many Tractable patents are Alive/Dead?
How Many Patents did Tractable File in Different Countries?
Countries in which Tractable Filed Patents
|United States Of America||15|
|Hong Kong (S.A.R.)||1|
Where are Research Centers of Tractable Patents Located?
The Research Center of all the Tractable Patents is the United Kingdom.
Best Tractable Patents
US20180300576A1 is the most popular patent in the Tractable portfolio. It has received 55 citations so far from companies like IBM, Alibaba Group and LG.
List of Tractable Patents –
|US11386543B2||Universal Car Damage Determination With Make/Model Invariance|
|US11361426B2||Paint Blending Determination|
|US11257203B2||Inconsistent Damage Determination|
|US11257204B2||Detailed Damage Determination With Image Segmentation|
|US11250554B2||Repair/Replace And Labour Hours Determination|
|US11244438B2||Auxiliary Parts Damage Determination|
|US20180300576A1||Semi-Automatic Labelling Of Datasets|
|US20210272168A1||Method Of Determining Painting Requirements For A Damage Vehicle|
|US20210272270A1||Method Of Determining Repair Operations For A Damaged Vehicle Including Using Domain Confusion Loss Techniques|
|US20210271930A1||Method Of Determining Damage To Parts Of A Vehicle|
|US20210272271A1||Method Of Determining Damage To Parts Of A Vehicle|
|US20220245786A1||Inconsistent Damage Determination|
|US20220164945A1||Repair/Replace And Labour Hours Determination|
|US20220156915A1||Auxiliary Parts Damage Determination|
|US20220138860A1||Remote Vehicle Damage Assessment|
|EP3357002A1||Semi-Automatic Labelling Of Datasets|
|CN108885700A||Semi-Automatic Labelling Of Datasets|
|WO2017055878A1||Semi-Automatic Labelling Of Datasets|
|WO2021136939A8||Method Of Determining Repair Operations For A Damaged Vehicle|
|WO2021136940A1||Method Of Determining Damage To Auxiliary Parts Of A Vehicle|
|WO2021136938A1||Method Of Determining Repair Operations For A Damaged Vehicle Including Using Domain Confusion Loss Techniques|
|WO2021136937A1||Method Of Determining Damage To Parts Of A Vehicle|
|WO2021136942A1||Method Of Determining Damage To Parts Of A Vehicle|
|WO2021136944A1||Method Of Universal Automated Verification Of Vehicle Damage|
|WO2021136936A1||Method Of Determining Damage To Parts Of A Vehicle|
|WO2021136943A1||Method Of Determining Painting Requirements For A Damage Vehicle|
|WO2021136945A1||Vehicle Damage State Determination Method|
|WO2021136947A1||Vehicle Damage State Determination Method|
|WO2021136946A1||Paint Blending Determination Method|
|WO2021136941A1||Method Of Determining Inconsistent Damage To Parts Of A Vehicle|
|WO2022094621A1||Remote Vehicle Damage Assessment|
|GB201517462D0||Semi-Automatic Labelling Of Datasets|
|GB201517463D0||Semi-Automatic Labelling Of Datasets|
|GB201517467D0||Semi-Automatic Labelling Of Datasets|
|GB202017464D0||Remote Vehicle Damage Assessment|
|GB202016723D0||Method Of Universal Automated Verification Of Vehicle Damage|
|GB202007465D0||Estimated Vehicle Repair Work Review Method And System|
|GB202000077D0||Paint Blending Determination Method|
|GB202000076D0||Vehicle Damage State Determination Method|
|AU2016332947B2||Semi-Automatic Labelling Of Datasets|
|AU2021204966A1||Method Of Determining Painting Requirements For A Damage Vehicle|
|AU2021204872A1||Method Of Determining Damage To Parts Of A Vehicle|
|AU2021204866A1||Method Of Determining Repair Operations For A Damaged Vehicle|
|AU2022202268A1||Semi-Automatic Labelling Of Datasets|
|JP7048499B2||Semi-Automatic Labeling Of Data Set|
|JP2022091875A||Semi-Automatic Labeling Of Datasets|
|IN201827015506A||Semi Automatic Labelling Of Datasets|
|KR1020180118596A||Semi-Automatic Labelling Of Datasets|
|HK1261719A||Semi-Automatic Labelling Of Datasets|