Solution #1

Automated CPC/IPC Classifier of Patents

PatClassifier aims to assist patent experts to classify patents effectively and accurately under the hierarchical Cooperative Patent Classification (CPC) System. Patent classification is a complex, time-consuming, and expensive task, because patent text is extremely complicated, long, and difficult to understand. Currently, a huge number of patents are manually classified by domain experts in the patent offices based on their knowledge and experiences. Our solution will simplify and streamline the complex patent classification process.

Main Features

AI-Powered Solution

Our deep-learning based patent classifier automatically categorizes patent documents into 656 subclasses under the hierarchical Cooperative Patent Classification (CPC) scheme. Using Neural Network based text classification methodology, our classifier was trained in a large volume of patent documents written in English.

Classifier tailored to the Characteristics of Patent Documents

Patent documents are usually very long and most of words appearing in a patent are used very rarely. Patents are structured documents composed of several sections, and each section of a patent is differently organized in size and words. Considering these characteristics of patent documents, we have experimented diverse data processing techniques tailored for automatic patent classification.

Multi-label Classifier

Our classifier assigns multiple CPC codes to a patent. In principal, one patent could have more than one category at the same time, and there is no fixed number of categories to be assigned to each patent. Currently, our classifier is designed to display minimum five possible CPC codes.

Create Account

Once an account is created, you can test our PatClassifier with 100 examples for free.

  • 100 example/month