Patents are an unused informational source in research and education. We stated that patent documents are an information source opening a wide variety of usage (monitoring, strategic positioning, technical document for innovation and state of the art) for a wide variety of users (researchers, Small and medium Enterprises (SME) and start-ups). Hence, patents are a key resource in education but difficult to read. The European Patent Database (EspaceNet) offers 100 million of demands in free access mode to their worldwide range database. We consider it as a technological encyclopedia: most of its content is free of rights. Patent2Net (P2N) is a free open-source patent analysis tool that offers the potential to extract patents from the worldwide database and to develop new data collections in very wide variety of domains (for instance: education, banana peel, rare earth, drones, or the list of last year demands (more than 1 Million documents) and so on). As an example, we propose to explore a huge set of patents using several data mining tools and visualization techniques (dynamic networks, textometry application, classification). We use several key information representations to explore the set: cartographies of a technology using mind maps, the network of actors or applicants, k-means clusters and many other features directly in concern with innovation, knowledge, and education.