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The large amounts of information produced daily by organizations and enterprises have led to the development of specialized software that can process high volumes of data. Given that the technologies and methodologies used to develop software are constantly changing, offering significant market opportunities, organizations turn to patenting their i...
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... works have focused on textual information (title), temporal trends (granting year) and longitudinal information (country). In respect to the RQs of Section 3.1 and the general focus of other works, the extracted features are presented in Table 2. The majority of features were directly extracted from selected fields of the dataset. ...Citations
... Due to its efficiency in extracting topics from textual information, LDA has been widely employed in many fields, including vehicular technologies [26,27], where Zhang 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]. ...
... Yang et al. [70] construct a comprehensive patent citation network leveraging direct, indirect, coupling and co-citation metrics, while Chakraborty et al. [71] use exponential random graph models to incorporate social parameters into a patent citation network. Finally, brokerage analysis [72], which exploits triadic relationships, has also been used in patent-to-patent networks [32,57,73]. ...
Patent analysis is a field that concerns the analysis of patent records, for the purpose of extracting insights and trends, and it is widely used in various fields. Despite the abundance of proprietary software employed for this purpose, there is currently a lack of easy-to-use and publicly available software that can offer simple and intuitive visualizations, while advocating for open science and scientific software development. In this study, we attempt to fill this gap by offering PatentInspector, an open-source, public tool that, by leveraging patent data from the United States Trademark and Patent Office, is able to produce descriptive analytics, thematic axes and citation network analysis. The use and interpretability of PatentInspector is illustrated through a use case on human resource management-related patents, highlighting its functionalities. The results indicate that PatentInspector is a practical resource for conducting patent analytics and can be used by individuals with a limited or no background in coding and software development.
... Researchers and organizations have acknowledged the value of patent analysis as the information included in patent documents represents an overview of the technologies that are developed for different domains and objectives. The existing research, i.e., patent analysis studies, covers a widespread area and different fields of interest, including electrical vehicles [16], artificial intelligence [17], security [18], software development [19], etc. In general, a patent record contains information concerning patent assignees, usually large companies; inventors; citations; descriptions, i.e., titles and abstracts; and patent classifications, i.e., specific categories and identifiers describing relevant technological fields. ...
Home automation technologies are a vital part of humanity, as they provide convenience in otherwise mundane and repetitive tasks. In recent years, given the development of the Internet of Things (IoT) and artificial intelligence (AI) sectors, these technologies have seen a tremendous rise, both in the methodologies utilized and in their industrial impact. Hence, many organizations and companies are securing commercial rights by patenting such technologies. In this study, we employ an analysis of 8482 home automation patents from the United States Patent and Trademark Office (USPTO) to extract thematic clusters and distinguish those that drive the market and those that have declined over the course of time. Moreover, we identify prevalent competitors per cluster and analyze the results under the spectrum of their market impact and objectives. The key findings indicate that home automation networks encompass a variety of technological areas and organizations with diverse interests.
The digital revolution in the Information and Communications Technology (ICT) sector necessitates advanced analytical tools to understand industry dynamics and support strategic decision-making. This article presents the development of a digitization Dashboard for industry-level analysis of the ICT sector. The study aims to fill the research gap in comprehensive industry-level analytical instruments and provide valuable insights for managers, policymakers, and industry stakeholders. The research questions focus on identifying technological advancements, understanding interconnections between technologies, and predicting industry growth.
A comprehensive literature review was conducted, covering various sectors related to ICT, digitization trends, and industry-level analysis. The review highlighted the need for a specialized Dashboard to integrate and visualize data across diverse technological domains within the ICT sector.
The methodology employed a hybrid approach using Design Science Research, combining quantitative data analysis with qualitative data for software development. Industry data, including patent analysis and technological trends, were collected, and processed during the analysis phase. Prototypes of the Dashboard were developed based on requirements from literature and industry standards in the design and development phase. The Dashboard underwent iterative improvements based on user feedback and usability testing.
The evaluation of the digitization Dashboard assessed its functionality, usability, and effectiveness in providing industry-level insights. The results demonstrate that the Dashboard offers valuable visual representations, trend analysis, and forecasting capabilities, empowering stakeholders to make informed decisions.
Limitations of the study include the reliance on qualitative data analysis, limiting the inclusion of quantitative insights, and the need for further validation of the Dashboard’s impact in real-world scenarios and diverse groups of users.
Future research should explore the integration of more machine learning techniques on patent data sources and user-centric evaluations to enhance the comprehensiveness and applicability of the digitization Dashboard. Continuous updates and expansions of the Dashboard functionalities are needed to accommodate emerging technological trends and evolving industry dynamics.
