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Computer Science, Software Engineering
Home automation technologies are a vital part of humanity, as they provide convenience in otherwise mundane and repetitive tasks. 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.
- patent analysis
- home automation networks
- patent classifications
1. Introduction
Home automation systems and networks aim to facilitate communication between devices by integrating technologies that accommodate automated procedures. The many applications of home automation networks leverage network technologies, sensors, controllers and devices to establish systems of interconnected devices and networks, e.g., security systems, home appliances, energy management systems and multiple similar architectures [1]. According to Sovacool and Del [2], home automation technologies offer many benefits that are related to finance, healthcare, security, education and entertainment, thus affecting several aspects of both daily life and industrial services. From the user perspective, the main perceptible and important benefits of home automation networks are related to (i) comfort (smart kitchen, TV), (ii) monitoring (sensors), (iii) therapy (automated delivery of treatments) (iv) support (robotic devices, mobility devices) and (v) consultancy (sensors) [3]. This fact, combined with the impressive rise of IoT and AI in the last decade, renders these technologies as catalysts in the progress and development of humanity, hence offering practical services and applications.
In order to provide novel solutions and produce competitive products, both researchers and industries are seeking to solve important problems and optimize the current approaches for innovative home automation technologies by employing a variety of scientific approaches and models, e.g., deep learning, statistics, graph theory, cryptography, heuristic algorithms [4]. These innovations, which can potentially lead to commercially exploitable outcomes, are usually covered by patent documents that protect the valuable legal and practical assets of individuals and organizations against competitors.
Regarding the industrial involvement, patented technologies can be used as a basis upon which to assess emerging technologies and key individuals, organizations and countries as they contain significant information and details on both trending and essential technologies and methodologies [5]. The process of analyzing and extracting knowledge from patent data constitutes the field of patent analysis and is used as a tool to cover multiple research goals related to trending and competitor analysis, technology forecasting and strategic planning. Evidently, the corresponding approaches leverage several data and text mining techniques for the completion of defined research goals [6], including natural language processing, cluster analysis and citation networks/graph theory.
2. Home Automation Networks
Home automation networks have recently grown rapidly and are now widely applied to improve different systems and appliances. Shuhaiber and Mashal [12] and Sin et al. [13] have revealed that the perceived usefulness and risks as well as the ease of use of these technologies are some of the most important factors towards accepting and using home automation networks. However, the efficiency and quality of home automation networks is measured by different indicators. According to Toschi et al. [14], home automation technologies should be evaluated based on their characteristics, such as communication and data type; their performance, such as complexity, rate, and processing power; as well as their various expenses, such as cost, energy consumption.
Zielonka et al. [4] focus their review on research reports and patents in order to identify the research trends of home automation applications. According to their findings, the popular and trending technologies aim to improve healthcare, e.g., eldercare; information security, e.g., cryptography and the blockchain; and energy systems, e.g., management of energy consumption. Other areas of interest include remote devices, communication systems and sensors.
The recent advances of machine learning and deep learning architectures have brought to the surface some new technologies and frameworks that employ such techniques in home automation services. Yu et al. [15] discuss the potential usage of deep learning in multiple applications and objectives that are relevant to home automation networks, indicating that these techniques improve existing machine learning approaches, e.g., naive Bayes and support vector machines. Their analysis shows that these applications are related to activity recognition and prediction, security as well as energy management while the utilized data structures are associated with sensors, images, videos and audio.
3. Patent Analaysis
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.
In particular, patent classifications have been effectively used to extract comprehensive knowledge from domain specific datasets. Jee et al. [20] have leveraged the available patent classifications, which are assigned to each patent, in order to identify promising technologies by six different perspectives, indicating their role in a technology area expressed by a representative patent dataset. In a different context, Park and Geum [21] assessed the relationships of the different technology areas using patent data and classifications, with a further goal of identifying potential opportunities from convergence networks. Similarly, Geum and Kim [22] have combined the information from patent classifications to establish a graph network to uncover core technologies and technological chances through cluster analysis. In addition, clustering applications that are based on patent classifications have been previously proposed as effective approaches in forecasting and evaluating promising and emerging technologies [7,8].
Regarding patent assignees, analyzing the patents and assets of a specific company can lead to the evaluation of its overall knowledge status, immediate competitors and relative strategic positioning. Suominen et al. [23] have explored the technologies of telecommunication industries through patent analysis in order to profile the different involved organizations and assess the potential connections between them. Likewise, by analyzing patent citations and the main properties of patent data, Daim et al. [24] evaluated the overall technology knowledge status of the different organizations, thus distinguishing those that hold significant inventions in the field of IoT, cybersecurity and blockchain. Additionally, Wang and Hsu [25] have established a topic strategy matrix and a topic–firm network to assess the activity and associations between topics and firms and to discover the core assignees and significant topics of smart manufacturing technologies.
This entry is adapted from the peer-reviewed paper 10.3390/computers12100211
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