The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data

Big data is increasingly available in all areas of manufacturing and operations, which presents an opportunity for better decision making and discovery of the next generation of innovative technologies. Recently, there have been substantial developments in the field of patent analytics, which describes the science of analysing large amounts of patent information to discover trends. We define Intellectual Property Analytics (IPA) as the data science of analysing large amount of IP information, to discover relationships, trends and patterns for decision making. In this paper, we contribute to the ongoing discussion on the use of intellectual property analytics methods, i.e artificial intelligence methods, machine learning and deep learning approaches, to analyse intellectual property data. This literature review follows a narrative approach with search strategy, where we present the state-of-the-art in intellectual property analytics by reviewing 57 recent articles. The bibliographic information of the articles are analysed, followed by a discussion of the articles divided in four main categories: knowledge management, technology management, economic value, and extraction and effective management of information. We hope research scholars and industrial users, may find this review helpful when searching for the latest research efforts pertaining to intellectual property analytics.The authors would like to acknowledge support of the Engineering and Physical Sciences Research Council (EPSRC)

Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions

Artificial intelligence (AI) is a set of rapidly expanding disruptive technologies that are radically transforming various aspects related to people, business, society, and the environment. With the proliferation of digital computing devices and the emergence of big data, AI is increasingly offering significant opportunities for society and business organizations. The growing interest of scholars and practitioners in AI has resulted in the diversity of research topics explored in bulks of scholarly literature published in leading research outlets. This study aims to map the intellectual structure and evolution of the conceptual structure of overall AI research published in Technological Forecasting and Social Change (TF&SC). This study uses machine learning-based structural topic modeling (STM) to extract, report, and visualize the latent topics from the AI research literature. Further, the disciplinary patterns in the intellectual structure of AI research are examined with the additional objective of assessing the disciplinary impact of AI. The results of the topic modeling reveal eight key topics, out of which the topics concerning healthcare, circular economy and sustainable supply chain, adoption of AI by consumers, and AI for decision-making are showing a rising trend over the years. AI research has a significant influence on disciplines such as business, management, and accounting, social science, engineering, computer science, and mathematics. The study provides an insightful agenda for the future based on evidence-based research directions that would benefit future AI scholars to identify contemporary research issues and develop impactful research to solve complex societal problems

Characterizing the potential of being emerging generic technologies: A Bi-Layer Network Analytics-based Prediction Method

© 2019 17th International Conference on Scientometrics and Informetrics, ISSI 2019 - Proceedings. All rights reserved. Despite tremendous involvement of bibliometrics in profiling technological landscapes and identifying emerging topics, how to predict potential technological change is still unclear. This paper proposes a bi-layer network analytics-based prediction method to characterize the potential of being emerging generic technologies. Initially, based on the innovation literature, three technological characteristics are defined, and quantified by topological indicators in network analytics; a link prediction approach is applied for reconstructing the network with weighted missing links, and such reconstruction will also result in the change of related technological characteristics; the comparison between the two ranking lists of terms can help identify potential emerging generic technologies. A case study on predicting emerging generic technologies in information science demonstrates the feasibility and reliability of the proposed method

Standards as a driving force that influences emerging technological trajectories in the converging world of the Internet and things: An investigation of the M2M/IoT patent network

While standards are said to create windows of opportunity in facilitation of technological convergence, it is not clear how they affect technological trajectories and strategic choices of firms in the face of convergence and in the process of catch-up. There is little research on the relationship between standards and technological trajectories, particularly in the age of convergence. This paper investigates how standards shape the emerging M2M/IoT technological trajectory and influence convergence in terms of technological importance and diversity. We, firstly, found that standards are a driving force of technological convergence. The second finding is that 3GPP standards assume a crucial role in setting the boundary conditions of the M2M/IoT technological systems. Third, we identified strategic groups and strategic patents that centered around the M2M/IoT trajectory. Forth, standards serve as an important factor in the process of creating a new path for catch-up firms (e.g. Huawei). These findings make contributions to innovation and standards studies by empirically examining the relationship between technological trajectories and standards. Furthermore, they clearly cast light on ongoing cooperation and competition along the M2M/IoT trajectory, and offer practical implications for catch-up strategies

The Value of Failures in Pharmaceutical R&D

We build a cumulative innovation model in which both success and failure provide valuable information for future research. To test this learning mechanism, we use a dataset covering outcomes of world-wide R&D projects in the pharmaceutical industry, and proxy knowledge flows with forward citations received by patents associated with each project. Empirical results confirm theoretical predictions that patents associated with successfully completed projects (i.e., leading to drug launch on the market) receive more citations than those associated to failed (terminated) projects, which in turn are cited more often than patents lacking clinical or preclinical information. We therefore offer evidence of the value of failures as research inputs in (pharmaceutical) innovation

The Value of Failures in Pharmaceutical R&D

We build a cumulative innovation model in which both success and failure provide valuable information for future research. To test this learning mechanism, we use a dataset covering outcomes of world-wide R&D projects in the pharmaceutical industry, and proxy knowledge flows with forward citations received by patents associated with each project. Empirical results confirm theoretical predictions that patents associated with successfully completed projects (i.e., leading to drug launch on the market) receive more citations than those associated to failed (terminated) projects, which in turn are cited more often than patents lacking clinical or preclinical information. We therefore offer evidence of the value of failures as research inputs in (pharmaceutical) innovationR&D competition, patent policy, pharmaceutical industry

A taxonomy of innovation networks

In this discussion paper we develop a theory-based typology of innovation networks with a special focus on public-private collaboration. This taxonomy is theoretically based on the concept of life cycles which is transferred to the context of innovation networks as well as on the mode of network formation which can occur either spontaneous or planned. The taxonomy distinguishes six different types of networks and incorporates two plausible alternative developments that eventually lead to a similar network structure of the two types of networks. From this, important conclusions and recommendations for network actors and policy makers are drawn. --

Networks and navigation in the knowledge economy: Studies on the structural conditions and consequences of path-dependent and relational action

In the wake of a relational turn, economic geographers have begun to scrutinize the relationships and interactions between people and organizations as a driving force behind economic processes at both global and local scales. Through a focus on contingent contextuality and path dependence, relational economic geography and network thinking have provided the necessary conceptual toolbox for untangling the structural effects and drivers of these relationships and their spatial embeddedness. However, despite the conceptual richness of the relational approach, empirical studies have often fallen short of capturing its core tenets: First, there is a prevalence to focus on places, infrastructures, and similarities as aggregate proxies for actors and their socio-economic relationships as the unit of geographical network analysis; While often convenient, this approach misses out on the capacity of networks to represent spatially embedded social contexts as enablers or constraints of economic action. Second, while path dependence is at the heart of evolutionary approaches towards economic geography, few studies actually trace how path-dependent and interrelated innovation shapes the long-term emergence of fields. Relational processes are especially salient when outcomes are opaque, decisions are interdependent, and when formal rules and roles are weak or absent. In this thesis, I ask how actors navigate such contexts and investigate the structural conditions and consequences of their navigation efforts. In my pursuit of this question, I draw on literatures from sociology, economics, and organization studies and build on novel methods of network analysis capable of empirically capturing contextuality and path dependence to investigate relational processes at three levels of economic activity: The thesis first looks towards a localized and informal trade platform to demonstrate how consumers rely on their former transactions to navigate exchange uncertainty and how such an exchange system can become liable to personal lock-in. It then moves on to show how the geographically and organizationally diversified search for innovation opportunities structures the transfer of knowledge across a globalized and partially informal corporate scouting community. Finally, the thesis shows how the linkage of distinct knowledge domains drives the long-term emergence of heterogeneous technological fields. In its endeavor to trace these processes, the thesis contributes a set of distinct relational research designs that demonstrate how advances in methods and data can be employed to empirically exploit the conceptual richness of relational economic geography

International Energy R&D Spillovers and the Economics of Greenhouse Gas Atmospheric Stabilization

This paper explores how international knowledge flows affect the dynamics of the domestic R&D sector and the main economic and environmental variables. The analysis is performed using WITCH, a dynamic regional model of the world economy, in which energy technical change is endogenous. The focus is on disembodied energy R&D international spillovers. The knowledge pool from which regions draw foreign ideas differs between High Income and Low Income countries. Absorption capacity is also endogenous in the model. The basic questions are as follows. Do knowledge spillovers enhance energy technological innovation in different regions of the world? Does the speed of innovation increase? Or do free-riding incentives prevail and international spillovers crowd out domestic R&D efforts? What is the role of domestic absorption capacity and of policies designed to enhance it? The new specification of the WITCH model presented in this paper enables us to answer these questions. Our analysis shows that international knowledge spillovers tend to increase free-riding incentives and decrease the investments in energy R&D. We also analyze the implication of a policy mix in which climate policy is combined with a technology policy designed to enhance absorption capacity in developing countries. Significant positive impacts on the costs of stabilising GHG concentrations are singled out.Climate Policy, Energy R&D, International R&D Spillovers, Stabilization