Nanomedicines have made a great breakthrough in the diagnosis, treatment and prevention of disease, but there are still significant challenges to make a leap from the scientific discovery to the practical and clinical success. Herein, we contribute to establish effective strategies that can bridge the gaps by an integrated and balanced analysis of patent literatures on the nanomedicine. Since most of successful commercialization of technologies stems from patents, it is important to comprehensively analyze the patent information. Unfortunately, many previous studies of patent analyses for technological trends and strategies are dependent on only quantitative analyses using structured data such as keyword networks and indicators, thus, these studies are difficult to offer detailed technological key components and trends on scientific perspectives. To address these problems, the present study performs qualitative and quantitative analysis using both structured and unstructured patent data, and provides evolving detailed trends in nanomedicine fields from a scientific point of view. Furthermore, upcoming advances in strategies regarding clinical needs, commercial events and strategies of potential leading groups in nanomedicine could be catalogued. Our analytical method and the results of several meaningful patterns and trends were applied to forecast the future practical issue of nanomedicines.
Keywords: Patent analysis | Structured and unstructured data | Nanotechnology | Nanomedicine | Material science | Surface chemistry | Pharmaceutics | Medical science | Patent indicator

Artificial intelligence (AI) is playing a major role in the new paradigm shift occurring across the technological landscape. After a series of alternate seasons starting in the 60s, AI is now experiencing a new spring. Nevertheless, although it is spreading throughout our economies and societies in multiple ways, the absence of standardised classifications prevents us from obtaining a measure of its pervasiveness. In addition, AI cannot be identified as part of a specific sector, but rather as a transversal technology because the fields in which it is applied do not have precise boundaries. In this work, we address the need for a deeper understanding of this complex phenomenon by investigating economic agents’ involvement in industrial activities aimed to supply AI-related goods and services, and AI-related R&D processes in the form of patents and publications. In order to conduct this extensive analysis, we use a complex systems approach through the agent-artifact space model, which identifies the core dimensions that should be considered. Therefore, by considering the geographic location of the involved agents and their organisation types (i.e., firms, governmental institutions, and research institutes), we (i) provide an overview of the worldwide presence of agents, (ii) investigate the patterns in which AI technological subdomains subsist and scatter in different parts of the system, and (iii) reveal the size, composition, and topology of the AI R&D collaboration network. Based on a unique data collection of multiple micro-based data sources and supported by a methodological framework for the analysis of techno-economic segments (TES), we capture the state of AI in the worldwide landscape in the period 2009–2018. As expected, we find that major roles are played by the US, China, and the EU28. Nevertheless, by measuring the system, we unveil elements that provide new, crucial information to support more conscious discussions in the process of policy design and implementation.
Keywords: Artificial intelligence | Complex systems | Agent-artifact space | Natural language processing | Semantic analysis | Network analysis

Crowdfunding has proliferated over the past decade, creating various platforms on which new entrepreneurs signal their value to potential backers in order to attract funding. The boons of crowdfunding have blinded both practitioners and scholars alike, leaving potential negative unintended consequences underexplored. Copycats, for example, are a potential problem for crowdfunding ventures. In this article, we examine the consequences of signaling to these ill-intentioned, opportunistic audiences on crowdfunding platforms and propose a solution to aid novice entrepreneurs in making a more informed decision on platform selection. Specifically, we provide guidance on when to select an open, semi-open, or closed crowdfunding platform to reduce the impact of copycats. We conclude with prescriptive advice to novice entrepreneurs.
KEYWORDS : Crowdfunding | Entrepreneurship | Copycats | Patents | Speed to market | Inimitability

This paper proposes a novel channel through which innovation externalities can affect firm performance. I find cross-sectional evidence that the positive innovation outputs of customer firms increase their supplier profitability as measured by firm ROE. This result is robust to the inclusion of industry fixed effect, a control for both supplier and customer characteristics, such as ROE, advertisement expenditure, capital expenditure, firm age, industry concentration, institutional ownership, dividend yield, and a control for industry spillover or geographical spillover. To identify the causal effect of customer innovation outputs on supplier performance, I study an exogenous shock—State Street Bank and Trust Company v. Signature Financial Group, Inc.—and find that an increase in granted customer patents causes an improvement in future supplier performance. This effect is mainly driven by the demand channel and the knowledge diffusion channel from customers to suppliers.
keywords: Innovation| Patent| Spillover| Customer/supplier link

The different stages of the life cycle of a drug – ‘prenatal’ stage, birth of a drug, rapid growth, maturity and stability, decline, and status before ‘death’ – are reflected in the three following databases: journal articles (PubMed— www.ncbi.nlm.nih.gov/pubmed); patents (US Patent Office—http://partfl1. uspto.gov/netahtml/PTO/search-adv.htlm); and approved drugs (FDA – www.accessdata.fda.gov/scripts/cder/drugsatfda/index/cfm). These data bases are huge, from authoritative sources, correctly classified, and they properly link different datasets. Analysis of such data can uncover hidden patterns important for the assessment of drug status and may also yield some predictions regarding its future prospects. Drug-related, publication-based academic bibliographic records are especially numerous and support the development of various scientometric indices. In combination with information from other types of databases, they can outline various trends in pharmacol ogy. Scientometric indices can be classified into those indicating a change in the status of a drug, and those assessing the chances for success, or even drug discontinuation. Here, we present big data analytics on publication-based academic interest in two segments: (i) description of scientometric indices and (ii) their applications for the assessment of the status of a drug.