Patent search and trend analysis

A patent is an intellectual property document that protects new inventions. It covers how things work, what they do, how they do it, what they are made of and how they are made. The owner of the granted patent application has the ability to take a legal action to stop others from making, using, importing or selling the invention without permission. While applying for a patent, the inventor has issues in identifying similar patents. Citations of related patents, which are referred to as the prior art, should be included while applying for a patent. We propose a system to develop a Patent Search Engine to identify related patents. We also propose a system to predict Business Trends by analyzing the patents. In our proposed system, we carry out a query independent clustering of patent documents to generate topic clusters using LDA. From these clusters, we retrieve query specific patents based on relevance thereby maximizing the query likelihood. Ranking is based on relevancy and recency which can be performed using BM25F algorithm. We analyze the Topic-Company trends and forecast the future of the technology which is based on the Time Series Algorithm - ARIMA. We evaluate the proposed methods on USPTO patent database. The experimental results show that the proposed techniques perform well as compared to the corresponding baseline methods.