Research and Development of Hepatitis B Drugs: An Analysis Based on Technology Flows Measured by Patent Citations

Using Patent Technology Networks to Observe Neurocomputing Technology Hotspots and Development Trends

In recent years, development in the fields of big data and artificial intelligence has given rise to interest among scholars in neurocomputing-related applications. Neurocomputing has relatively widespread applications because it is a critical technology in numerous fields. However, most studies on neurocomputing have focused on improving related algorithms or application fields; they have failed to highlight the main technology hotspots and development trends from a comprehensive viewpoint. To fill the research gap, this study adopts a new viewpoint and employs technological fields as its main subject. Neurocomputing patents are subjected to network analysis to construct a neurocomputing technology hotspot. The results reveal that the neurocomputing technology hotspots are algorithms, methods or devices for reading or recognizing printed or written characters or patterns, and digital storage characterized by the use of particular electric or magnetic storage elements. Furthermore, the technology hotspots are discovered to not be clustered around particular fields but, rather, are multidisciplinary. The applications that combine neurocomputing with digital storage are currently undergoing the most extensive development. Finally, patentee analysis reveal that neurocomputing technology is mainly being developed by information technology corporations, thereby indicating the market development potential of neurocomputing technology. This study constructs a technology hotspot network model to elucidate the trend in development of neurocomputing technology, and the findings may serve as a reference for industries planning to promote emerging technologies.

Research and development of anti-Parkinson's drugs: an analysis from the perspective of technology flows measured by patent citations

INTRODUCTION

By 2020, nearly one million people will live with Parkinson's disease (PD) in the U.S. This disorder has a significant impact on patients' quality of life and is a burden on families and society. Protracted efforts have been made to treat the disease. Cumulative technological innovations are encapsulated by patents, and patent citations have been used to analyze technology diffusion processes in R&D, which is essential to identifying technology evolution trends and providing a review of PD treatment from the perspective of technology flows.

AREAS COVERED

A patent citation network was utilized to analyze technology flows. Patents related to anti-PD drugs were collected from the U.S. Patent and Trademark Office (U.S. PTO) database. A total of 1,231 patents and 2,995 internal citations granted between 1988 and 2017 were included and analyzed.

EXPERT OPINION

To launch drugs with greater efficiency and safety, approaches such as long-acting sustained release, controlled osmotic release, and other novel drug delivery systems should be emphasized. Multi-target agents could effectively reduce side effects in mono-drug therapy and are worth further exploration. Investors should keep an eye on alpha-synuclein-related therapy, gene therapy, and other experimental therapies that might trigger a historic revolution in the treatment domain.

Forecasting new product diffusion using both patent citation and web search traffic

Accurate demand forecasting for new technology products is a key factor in the success of a business. We propose a way to forecasting a new product's diffusion through technology diffusion and interest diffusion. Technology diffusion and interest diffusion are measured by the volume of patent citations and web search traffic, respectively. We apply the proposed method to forecast the sales of hybrid cars and industrial robots in the US market. The results show that that technology diffusion, as represented by patent citations, can explain long-term sales for hybrid cars and industrial robots. On the other hand, interest diffusion, as represented by web search traffic, can help to improve the predictability of market sales of hybrid cars in the short-term. However, interest diffusion is difficult to explain the sales of industrial robots due to the different market characteristics. Finding indicates our proposed model can relatively well explain the diffusion of consumer goods.