Artificial intelligence for patent prior art searching

This study explored how artificial intelligence (AI) could assist patent examiners as part of the prior art search process. The proof-of-concept allowed experimentation with different AI techniques to suggest search terms, retrieve most relevant documents, rank them and visualise their content. The study suggested that AI is less effective in formulating search queries but can reduce the time and cost of the process of sifting through a large number of patents. The study highlighted the importance of the humanin-the-loop approach and the need for better tools for human-centred decision and performance support in prior art searching.     

Leveraging patent landscape analysis and IP competitive intelligence for competitive advantage

Patent landscape and the accompanying IP competitive intelligence involves understanding and anticipating the competitive environment within which a company operates. More specifically, IP competitive intelligence highlights emerging IP risks, provides patent portfolio benchmarking, monitors competitor technology development efforts, and predicts commercialization of technology.This paper provides a framework for patent landscape and IP competitive intelligence as driven by strategic intent. This paper advocates the benefits of both “quantitative” statistical analysis and “qualitative” human intelligence for IP competitive intelligence. Moreover, this paper defines four Levels of IP analysis with pruned examples for effective competitive intelligence.     

Multi-cross-industry innovation patents in the USA - A combination of PATSTAT and Orbis search

Knowledge and technologies from different fields will undoubtedly be combined in order to develop the products of the future. Remarkable examples thereof can already be found in the fields of interconnected „smart“ products and natural care products. Few companies have access to the entire range of available knowledge; most are required to obtain this knowledge from other companies or research institutes. One way to acquire the requisite knowledge is through co-operation. When at least three companies from different industries are cooperating for this purpose, we speak of multi-cross-industry innovation. This kind of innovation is reflected in many cases of patenting. For a deeper understanding, we identify multi-cross-industry innovation patents in the leading market of the USA, using a combined search on PATSTAT and Orbis in the period from 1980 to 2015. We apply a time series analysis, an applicant analysis, a priority country analysis, an analysis of co-operation structure, and an analysis of the application domains to the data. Our results show an increase in the occurrence of multi-cross-industry innovation patents. The major players involved in this are Japanese companies, which apply for nearly 90% of all multi-cross-industry innovation patents. Multi-cross-industry innovation covers a broad range of application domains, from electronics to material sciences.     

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.     

Artificial intelligence in fifth generation computers

Artificial intelligence forms an intrinsic aspect of the competence of fifth generation computers in view of the need for them to be primarily knowledge processing systems. We analyse in this paper how this need for knowledge processing arises. In the main body of the paper various issues that arise in knowledge representation and use are discussed. The point of departure for these discussions is the recent work on expert systems. We point out the difference between tacit and propositionizable knowledge and argue the need for modelling tacit knowledge also in fifth generation computers.     

Analysis of the patent cooperation network in global artificial intelligence technologies based on the assignees

Studying the distribution of the patent cooperation networks from the perspective of assignees provides a very important reference to improve the analysis of the market situation, master the layout of industrial technology and seek partners or mergers and acquisitions. This study uses the Derwent patent database and the patent metric approach to investigate the cooperative network structure of the assignees. The overall patent output in the artificial intelligence field on a global scale exhibited a rapid growth, and the proportion of cooperative patents significantly increased; the cooperation structure between the assignees was loose, and the innovation efficiency was low.     

The impact of automation and artificial intelligence on worker well-being

Discourse surrounding the future of work often treats technological substitution of workers as a cause for concern, but complementarity as a good. However, while automation and artificial intelligence may improve productivity or wages for those who remain employed, they may also have mixed or negative impacts on worker well-being. This study considers five hypothetical channels through which automation may impact worker well-being: influencing worker freedom, sense of meaning, cognitive load, external monitoring, and insecurity. We apply a measure of automation risk to a set of 402 occupations to assess whether automation predicts impacts on worker well-being along the dimensions of job satisfaction, stress, health, and insecurity. Findings based on a 2002–2018 dataset from the General Social Survey reveal that workers facing automation risk appear to experience less stress, but also worse health, and minimal or negative impacts on job satisfaction. These impacts are more concentrated on workers facing the highest levels of automation risk. This article encourages new research directions by revealing important heterogeneous effects of technological complementarity. We recommend that firms, policymakers, and researchers not conceive of technological complementarity as a uniform good, and instead direct more attention to mixed well-being impacts of automation and artificial intelligence on workers.     

Improving the Iranian Industrial Property System (IPS): A comparative case study of Iran and South Korea

This study analyzes the Industrial Property System (IPS) of Iran and proposes solutions for improving the system. Documentary research and a comparative study of the South Korea IPS are the main sources of information. The results indicate that although Iran has taken important steps towards improving its IPS, there are lingering problems. The organizational set-up of the IPS needs restructuring. Moreover, intellectual property (IP) management professionals and policy makers will benefit from IP training programs. Finally, IP policy making and IPS reform should be based on the needs of the industry.     

An analysis of the effects of artificial intelligence on electric vehicle technology innovation using patent data

This study empirically analyzes the effects of artificial intelligence (AI) on electric vehicle technology innovation by employing a machine learning-based text mining model and the international patent classification (IPC) co-occurrence network analysis, using patent data filed from 1980 to 2017. Based on artificial intelligence algorithms classified, the study demonstrates the dynamic changing pattern of the convergence of artificial intelligence and electric vehicle technology and reveals how artificial intelligence has affected electric vehicle technology innovation over time. This study reveals that artificial intelligence accelerates the automation of electric vehicle driving, and that artificial intelligence algorithms that are widely used in electric vehicles have changed over time, and that technology areas of electric vehicles that AI affects also have been changed.     

The trends in CRISPR research: A patent and literature study with a focus on India

The clustered regularly interspaced short palindromic repeat (CRISPR)-Cas systems are currently in the spotlight of Biological research. The system has introduced a revolutionary gene-editing technique that changed the face of healthcare, agriculture, gene therapy, cosmetic surgery, and much more. The current study focuses on the patent and non-patent literature data on CRISPR and aims to find out the global research scenario. Apart from the patent landscape analysis, the current study analyzed the worldwide research activity and top players of the field. A special emphasis has been given to understand the CRISPR-related intellectual property scenario in India. Extensive analysis shows that the United States dominates the CRISPR research and market. The main focus of CRISPR research in India is agriculture.     

Artificial intelligence,systemic risks,and sustainability

Automated decision making and predictive analytics through artificial intelligence, in combination with rapid progress in technologies such as sensor technology and robotics are likely to change the way individuals, communities, governments and private actors perceive and respond to climate and ecological change. Methods based on various forms of artificial intelligence are already today being applied in a number of research fields related to climate change and environmental monitoring. Investments into applications of these technologies in agriculture, forestry and the extraction of marine resources also seem to be increasing rapidly. Despite a growing interest in, and deployment of AI-technologies in domains critical for sustainability, few have explored possible systemic risks in depth. This article offers a global overview of the progress of such technologies in sectors with high impact potential for sustainability like farming, forestry and the extraction of marine resources. We also identify possible systemic risks in these domains including a) algorithmic bias and allocative harms; b) unequal access and benefits; c) cascading failures and external disruptions, and d) trade-offs between efficiency and resilience. We explore these emerging risks, identify critical questions, and discuss the limitations of current governance mechanisms in addressing AI sustainability risks in these sectors.     

Strategic patenting in the upstream oil and gas industry: Assessing the impact of the pre-salt discovery on patent applications in Brazil

For most of its history, Brazil needed to import oil to complement its internal production to supply the internal demand. However, in 2007, the Brazilian Federal Government announced the discovery of huge hydrocarbon resources in the pre-salt layer of the country's Southeastern coast. This study examines the impact of this discovery accomplished by Petróleo Brasileiro S.A. (Petrobras) on patent applications in Brazil associated with upstream oil and gas technologies. Then, this article provides empirical evidence that the pre-salt discovery significantly affected patent strategizing of Multi-Nationals Companies (MNCs) operating in the upstream oil and gas industry, thereby generating a boom in patent filings in Brazil from the official pre-salt announcement onwards.     

Collective action on artificial intelligence: A primer and review

Progress on artificial intelligence (AI) requires collective action: the actions of two or more individuals or agents that in some way combine to achieve a result. Collective action is needed to increase the capabilities of AI systems and to make their impacts safer and more beneficial for the world. In recent years, a sizable but disparate literature has taken interest in AI collective action, though this literature is generally poorly grounded in the broader social science study of collective action. This paper presents a primer on fundamental concepts of collective action as they pertain to AI and a review of the AI collective action literature. The paper emphasizes (a) different types of collective action situations, such as when acting in the collective interest is or is not in individuals’ self-interest, (b) AI race scenarios, including near-term corporate and military competition and long-term races to develop advanced AI, and (c) solutions to collective action problems, including government regulations, private markets, and community self-organizing. The paper serves to bring an interdisciplinary readership up to speed on the important topic of AI collective action.     

A conceptual framework for combining artificial intelligence and optimization in engineering design

Research in artificial intelligence and optimization (OR) has had significant impact on the formulation and solution of computational methods in engineering design. This paper presents a conceptual framework for understanding a more powerful technology that is evolving from a combination of these approaches. The paper first proposes generalized representations of engineering design models that involve quantitative and qualitative aspects. Second, it presents a general classification of AI and OR models in terms of model attributes, in order to establish mappings with generic solution techniques. Third, the requirements of solution methods are discussed, as well as several schemes for the integration of AI and optimization to identify future research directions. Several specific approaches are included to illustrate various ways in which AI and optimization can be combined for tackling computational design models.     

Patenting patterns in Artificial Intelligence: Identifying national and international breeding grounds

This paper identifies countries at the forefront of Artificial Intelligence (AI) development and proposes two novel patent-based indicators to differentiate structural differences in the patterns of intellectual property (IP) protection observed for AI across countries. In particular, we consider (i) the extent to which countries specialise in AI and are relevant markets for corresponding IP protection (‘National Breeding Ground’); and (ii) the extent to which countries attract AI from abroad for IP protection and extend the protection of their AI-related IP to foreign markets (‘International Breeding Ground’). Our investigation confirms prior findings regarding substantial changes in the technological leadership in AI, besides drastic changes in the relevance of AI techniques over time. Particularly, we find that National and International Breeding Grounds overlap only partially. China and the US can be characterised as dominant National Breeding Grounds. Australia and selected European countries, but primarily the US, are major International Breeding Grounds. We conclude that China promotes AI development with a major focus on IP protection in its domestic market, whereas the US sustains its AI progress in the international context as well. This might indicate a considerable bifurcation in the structural patterns of IP protection in global AI development.     

The role of artificial intelligence in business transformation: A case of pharmaceutical companies

Artificial intelligence (AI) is of great interest to researchers and practitioners as a means of achieving the necessary progress in the pharmaceutical industry. However, the role of AI and ways of transforming companies are not well studied. The purpose of the paper is to identify exactly how AI affects the key and support business processes of pharmaceutical companies. We offer a qualitative interview study of five large, five medium, and five small pharmaceutical companies. Based on scarce literature on the role of AI in the pharmaceutical industry, we considered which business processes are subject to transform within it and how they do so. We determine that small pharma companies significantly change research and development, master data management, analysis and reporting, and human resource business processes under the influence of AI. Large pharma companies use AI to transform production, sales, marketing, and analysis business processes. In turn, medium-sized companies are in the middle and individually transform their business processes depending on their specialization.     

Artificial intelligence adoption in the physical sciences,natural sciences,life sciences,social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021

Analysing historical patterns of artificial intelligence (AI) adoption can inform decisions about AI capability uplift, but research to date has provided a limited view of AI adoption across different fields of research. In this study we examine worldwide adoption of AI technology within 333 fields of research during 1960–2021. We do this by using bibliometric analysis with 137 million peer-reviewed publications captured in The Lens database. We define AI using a list of 214 phrases developed by expert working groups at the Organisation for Economic Cooperation and Development (OECD). We found that 3.1 million of the 137 million peer-reviewed research publications during the entire period were AI-related, with a surge in AI adoption across practically all research fields (physical science, natural science, life science, social science and the arts and humanities) in recent years. The diffusion of AI beyond computer science was early, rapid and widespread. In 1960 14% of 333 research fields were related to AI (many in computer science), but this increased to cover over half of all research fields by 1972, over 80% by 1986 and over 98% in current times. We note AI has experienced boom-bust cycles historically; the AI “springs” and “winters”. We conclude that the context of the current surge appears different, and that interdisciplinary AI application is likely to be sustained.     

Coopetition and innovation in high-tech firms: What we can learn from analysis of the semiconductor industry's patents

This study investigates coopetition among the 16 semiconductor firms that figured among the top 10 by revenue from 2009 to 2019 using patent data obtained from the Derwent World Patents Index™ (DWPI), considering records available for the selected firms published and indexed up to July 26, 2020. Only 1791 (0.17%) records from a total of more than 1.1 million have two or more of these competing firms as assignees (i.e., they are records for joint patents involving these firms), indicating the existence of coopetition in this scenario. These joint patents demonstrate coopetition between firms from different countries and in the main areas in which their patents are classified, indicating that they may coopete in those areas. Furthermore, mergers and acquisitions and joint ventures may influence coopetition and innovation, resulting in joint patents. Finally, a framework that consolidates the main findings is presented to guide future research. We contribute to the coopetition literature with novel inputs. From a managerial perspective, the findings can be used to build strategies to better exploit the potential of patents.