The fourth industrial revolution (4IR) is likely to have a substantial impact on the economy. Companies need to build up capabilities to implement new technologies, and automation may make some occupations obsolete. However, where, when, and how the change will happen remain to be determined. Robust empirical indicators of technological progress linked to occupations can help to illuminate this change. With this aim, we provide such an indicator based on patent data. Using natural language processing, we calculate patent exposure scores for more than 900 occupations, which represent the technological progress related to them. To provide a lens on the impact of the 4IR, we differentiate between traditional and 4IR patent exposure. Our method differs from previous approaches in that it both accounts for the diversity of task-level patent exposures within an occupation and reflects work activities more accurately. We find that exposure to 4IR patents differs from traditional patent exposure. Manual tasks, and accordingly occupations such as construction and production, are exposed mainly to traditional (non-4IR) patents but have low exposure to 4IR patents. The analysis suggests that 4IR technologies may have a negative impact on job growth; this impact appears 10 to 20 years after patent filing. Further, we compared the 4IR exposure to other automation and AI exposure scores. Whereas many measures refer to theoretical automation potential, our patent-based indicator reflects actual technology diffusion. Our work not only allows analyses of the impact of 4IR technologies as a whole, but also provides exposure scores for more than 300 technology fields, such as AI and smart office technologies. Finally, the work provides a general mapping of patents to tasks and occupations, which enables future researchers to construct individual exposure measures.

The Industry 4.0 literature has exponentially grown in the past decade. We aim to understand how this literature has evolved and propose future research opportunities. We focus on four smart dimensions of Industry 4.0: Smart Manufacturing, Smart Products and Services, Smart Supply Chain, and Smart Working. We perform a machine learning-based systematic literature review. Our analysis included 4,973 papers published from 2011 to 2020. We conducted a chronological network analysis considering the growth of these four dimensions and the connections between them. We also analyzed keywords and the main journals publishing on these four smart dimensions. We show that the literature has mainly been devoted to the study of Smart Manufacturing, although attention to the other smart dimensions has been growing in recent years. Smart Working is the less explored dimension, with many opportunities for future research. We show that research opportunities are concentrated in the interfaces between the different smart dimensions. Our findings support the vision of Industry 4.0 as a concept transcending the Smart Manufacturing field, thus creating opportunities for synergies with other related fields. Scholars can use our findings to understand the orientation of journals and gaps that can be fulfilled by future research.

The fourth industrial revolution is rapidly changing the manufacturing landscape. Due to the growing research and fast evolution in this field, no clear definitions of these concepts yet exist. This work provides a clear description of technological trends and gaps. We introduce a novel method to create a map of Industry 4.0 technologies, using natural language processing to extract technology terms from 14,667 research articles and applying network analysis. We identified eight clusters of Industry 4.0 technologies, which served as the basis for our analysis. Our results show that Industrial Internet of Things (IIoT) technologies have become the center of the Industry 4.0 technology map. This is in line with the initial definitions of Industry 4.0, which centered on IIoT. Given the recent growth in the importance of artificial intelligence (AI), we suggest accounting for AI's fundamental role in Industry 4.0 and understanding the fourth industrial revolution as an AI-powered natural collaboration between humans and machines. This article introduces a novel approach for literature reviews, and the results highlight trends and research gaps to guide future work and help these actors reap the benefits of digital transformations.

This volume presents the proceedings of the 1st Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech 2019) co-located with the SEMANTiCS 2019 conference, held in Karlsruhe, Germany. It is a first in series of workshops that aims to establish a long-term collaboration and a two-way communication channel between the IP industry and academia from relevant fields to foster the usage of semantic technologies for answering research questions related to patent text mining and patent analytics as well as adopt them in working applications.