# PatentInspector: An Open-Source Tool for Applied Patent Analysis and Information Extraction
## 1 Introduction  
In this era of technological and entrepreneurial progress, an increasing number of companies seek to safeguard their intellectual property. Specifically, the number of annual patent applications has almost tripled in the last two decades, according to a study conducted by the World Intellectual Property Organization (WIPO) [1], rendering patent documents more valuable than ever before. Patents are widely considered as a safe choice for large companies and organizations to secure commercial rights, avoid litigation actions and retain their competitive advantage [2]. The scope and importance of patenting is made clear when considering the large number of patent offices around the world, responsible for receiving, evaluating and granting patent applications. Such offices, with the most prominent ones being the United States Trademark and Patent Office (USPTO), the European Patent Office (EPO) and the China National Intellectual Property Administration (CNIPA), handle the difficult task of processing and analyzing patent documents, examining their objectives and their validity. This wealth of information has led to the emergence of patent analysis (PA), as a promising scientific domain that leverages data from patent offices to extract valuable results [3]. In brief, PA is a field that covers the study of patent documents utilizing proven methodologies and techniques comprising text mining, machine learning and data visualization [4; 5]. The results of PA have numerous applications that can be exploited in differentsections within an organization or a business, including R&D management, human resources, mergers and acquisition, company evaluation and competitive intelligence [6]. In addition, PA offers a plethora of opportunities for the extraction of meaningful insights through the application of advanced approaches, such as topic modeling, network analysis and machine learning. While PA offers valuable insights, it is a time-consuming multi-stage process that requires specific skills to be conducted. Patent documents must be collected from various sources, leveraging APIs offered by the patent offices, if applicable, or by using high-level programming languages and databases. After collecting the documents, they must be preprocessed and filtered to meet certain criteria depending on the research goals and examined domain and, finally, be analyzed using a set of methodologies. While this process may seem simple for a seasoned researcher or an individual with a background in programming, databases and data engineering, there are groups of users, such as industrial actors and business stakeholders, that may not possess these types of skills or knowledge and require PA to be streamlined, automated and free of prior knowledge. Hence, in recent years, tools that automate the process of PA have emerged and have been utilized within organizations [4], due to the excessive volume of patent documents and the inherent complexity in analyzing them. These tools frequently offer the possibility of identifying and collecting related documents, filtering them based on established criteria and applying PA methodologies. Some of these tools are also offered for advanced scientific purposes and enable researchers from multiple disciplines to overcome the obstacles of PA and easily process patent entries. However, while PA tools do exist and are in use, to the best of our knowledge, very few of them are available as free, accessible and open-source solutions, with the majority of tools being either proprietary or requiring payment after a short free trial. In addition, the existing open-source PA tools are somewhat complex to navigate, requiring a level of scientific knowledge. Thus, the lack of a flexible, open-source and public PA tool that can cater to the needs of multiple target groups for research purposes is a clear gap in the domain of PA software.