USPTO uses machine learning to speed up the process of assigning patent applications to examiners.
Instead of remodeling its entire classification process, the U.S. Patent and Trademark Office (USPTO) by using Machin Learning (ML) shorten the time it takes to assign patent applications to examiners. Regarding using ML in the trademark and patent examination process, USPTO is still in the early stages of integrating machine learning and robotic process automation technologies.
This target supposed to be achieved by USPTO sending its top engineers to Google company to learn more about ML and TensorFlow application programming interfaces. The agency’s engineers who went to Google to learn more about TensorFlow application programming interfaces and machine learning are applying ML to patent search and classification processes using Python with TensorFlow.
TensorFlow is an end-to-end open-source machine learning platform for everyone. This platform can be used to train and inference deep neural networks, among other The USPTO patent examiners have trained to use TensorFlow feedback loops to evaluate how effective machine learning algorithms are for classifying patent applications.
He said USPTO is still in the early stages of integrating machine learning and robotic process automation technologies into its patent and trademark processes. The agency is already using the technologies for clerical and administrative processes. applications.
There is a growing list of federal civilian and defense agencies tapping into the potential of machine learning and USPTO recently join to this list. USPTO is employing vendors to help perform patent classifications and compare them against the agency’s algorithms. Allowing patent examiners to work with vendors lets the agency further improve the algorithms.
