The forecasting of development trends and the timely revealing of new technical (technological) fields are the key prerequisite for an effective development of modern economy. Only reliable results of technological analysis (forecast) allow identifying new technologies, understanding the evolution of entire industries, carrying out strategic investment planning at the state level, and also planning R&D correctly. The aim of this work is to justify one of the possible approaches to the classification of technical (technological) fields in terms of assessing their relevance, novelty and short-term prospects. This approach is based on patent analysis, in particular, on the study of the time series features of US invention patents (1976-2018) for more than seventy-three thousand main groups (subgroups) of the 17th edition of the International Patent Classification (IPC17). The United States Patent and Trademark Office (USPTO) has been selected as the primary source of information because it is one of the world’s largest and constantly updated patent resources, providing direct access to full-text descriptions. In the authors’ opinion, a feature analysis of the US patent issue dynamics at time intervals (1976-2015, 2009-2018 and 2016-2018) allows dividing the IPC groups (subgroups) into the following three main clusters: “unpromising”, “promising” and “breakthrough”. In terms of the timely revealing of new, previously unknown, technologies or solutions in the technical field, or of the steadily growing technological trends, the “breakthrough” and “promising” subgroups are of the greatest practical interest. The article presents the results of an empirical classification of 71,266 subgroups (with a non-zero number of the issued patents since 1976 to 2018) in eight sections of the IPC17. These data may be useful for developers, researchers and R&D planners in solving complex scientific and technical problems, as well as for making short-term forecast estimates of a specific technical (technological) field development.
Impact of operational details and temporal representations on investment planning in energy systems dominated by wind and solar
