# PatentInspector: An Open-Source Tool for Applied Patent Analysis and Information Extraction
## 2 Background Information on Patent Analysis and Tools
### Patent Analysis Literature  
Descriptive/Exploratory Analytics: Several studies have leveraged descriptive statistics to portray the temporal, geographical or technological development of patents in various fields. The results of these studies are either descriptive information about patents (e.g., most prominent organizations) or insights from multivariate methods that explain the relationships between multiple variables. [PERSON] et al. [13] focus on the IoT domain and explore its trends and dynamics on a country and assignee level, pinpointing the USA and China as prominent countries and Huawei and Qualcomm as the main assignees. [PERSON] et al. [14] and [PERSON] and [PERSON] [15] explore the AI domain with knowledge-based methodologies and discover the main technologies and investors in AI trends. In the context of software engineering, [PERSON] et al. [16] perform a large-scale analysis on patents from the USPTO to discover the geographical, organizational and technological distributions. Similar analyses have also been conducted in the fields of low-carbon technologies [17], RFID concepts [18], augmented reality [19], nanoscience [20] and photovoltaics [21], indicating that PA as a practice can be efficiently used in multiple application domains and yield practical results. Additional studies have also attempted to combine the use of PA with bibliometrics, enhancing the insights of PA with knowledge derived from the research literature and bibliometric indicators [22; 23; 24]. Topic Modeling: Apart from leveraging descriptive statistics and exploratory analysis on patent data, several studies have employed algorithms on patent data that extract topics and thematic axes, pinpointing promising technologies and objectives. Among them, the Latent Dirichlet Allocation (LDA) algorithm, proposed by [PERSON] et al. [25], is by far the most popular when it comes to extracting topics in PA. Due to its efficiency in extracting topics from textual information, LDA has been widely employed in many fields, including vehicular technologies [26; 27], where [PERSON] et al. [27] leveraged a variation of LDA, namely the structural topic modeling (STM) algorithm [28], which has also been employed in [29] for the profiling of hydrogen technologies. Other fields include smart manufacturing [30], sustainable city development [31], data-oriented software [32] and telecommunication patents [33], with the latter reviewing assignee hotspots, based on the extracted topics. Hotspots are particularly important as they emphasize prime investors and technologies and they have also been investigated in a plethora of studies [34; 35; 36; 37]. Patent roadmaps, which comprise emerging or trending technologies that pave the road for future patent applications, are also an important part of topic modeling studies. [PERSON] et al. [38]. propose a patent development map with a case study in 3D printing,using LDA, while [PERSON] et al. [39] apply the same process in solar cell technologies. [PERSON] et al. [40] explore the Blockchain sector to assess technological maturity and forecast trending topics, while a large case study of patents in Australia [41] presents a methodology with semantic information that estimates development for specific topics, with a tailored case study. Finally, [PERSON] et al. [42] leverage CPC clusters in telemedicine patents to evaluate the development of the field. It should be mentioned that topic modeling has also been employed in studies that explore the profiles of firms, along with their knowledge portfolios [43], and the identification of disruptive technologies that may alter the structure of the market [44], with a case study on photovoltaics. Citation Networks: Patent citation networks have also been proven to be highly important, based on the related literature, as they portray the interrelations between patent records and uncover the most influential patents or technologies.