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Both public policy and business management are increasingly interested in how to manage trade secrets. One of the driving forces is the growing significance of data as an asset, as ‘oil of the 21st century'. Trade secrets are often seen as the major Intellectual Property (IP) tool for protecting data. There is also the understanding that the need to share data is increasing to allow for new types of innovation. This paper seeks to understand how data sharing practices and the use of trade secrets are evolving in the agricultural industries. Using explorative empirical data from four in-depth case studies, the paper develops a framework for data sharing practices, value sharing, and trade secrets use. We find that current data sharing practices pool around two scenarios, where data is not shared or shared only with limited partners (hence closed) and there are differences whether value created from the data is shared. We conclude that a nuanced view on the use of trade secrets in data sharing is mandated for both IP/data managers and scholars analysing the topic.

This paper explores the applicability of IP (Intellectual Property) valuation methods in the university TTOs context by using multi-criteria decision-making frameworks. The IP valuation methods are evaluated for their usage in university IP and Startup valuation, patent or patent-pending technologies, and different Technology Readiness Levels (TRLs). Data was collected through in-depth structured interviews and a Delphi survey with 17 experts. By descriptive analysis of responses and consensus rates, we proposed a combined multi-criteria decision-making model of Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for university Technology Transfer Offices (TTOs) to be utilized in selecting appropriate IP Valuation methods. Findings reveal that more than one method should be used to make well-supported estimates for patent value. DCF method is the most suggested method. Income methods are most favourable on University IP, Startup's IP, non–patented or patent-pending, TRL 1–6 technologies. Findings can help valuation practitioners, especially from TTOs, design roadmaps for IP valuation.

This paper introduces a new measure of patent value – Maintenance Renewal Score (MRSc) – reflecting assignee valuing the patent by paying successive renewal fees. We generate MRSc's for nanotechnology patents issued by the US Patent Office from 1999 through 2009, with US assignees and US inventors. Patenting increases over this period, coincident with increased US funding of nanotechnology R&D. We compare maintenance rates over the period, and against a comparison set of all 1999 USPTO grants to US inventors/assignees. We find differences in propensity to maintain the nanopatents by institution type, technological sector, and patent complexity.

Oppositions to patent grants are a means to attack competitors and protect a firm's own patent portfolio. Extant literature has analyzed the determinants of oppositions, while the drivers of opposition outcomes are less known. We study 290 EPO biotech patent oppositions filed in 2012–2019. There are three possible outcomes of opposition proceedings: patent revocation, patent amendment, and opposition rejection. We find that opponents who know the patent's technology are more likely to obtain a patent revocation, while opponents who compete in the product market with the patent owner are more likely to receive a rejection.

We use a unique database of digital, and cybersecurity hires from Swiss organizations and develop a method based on a temporal bi-partite network, which combines local and global indices through a Support Vector Machine. We predict the appearance and disappearance of job openings from one to six months horizons. We show that global indices yield the highest predictive power, although the local network does contribute to long-term forecasts. At the one-month horizon, the “area under the curve” and the “average precision” are 0.984 and 0.905, respectively. At the six-month horizon, they reach 0.864 and 0.543, respectively. Our study highlights the link between the skilled workforce and the digital revolution and the policy implications regarding intellectual property and technology forecasting.

To obtain patent protection, a patent must fulfill statutory patentability requirements examined by a patent office. Such examinations are mostly performed manually and are quite time-consuming. Therefore, we suggest a computer-based process for the assessment of patentability by means of a mathematical-logical approach comparing patents with semantic structures. In order to make such an assessment, we compare the feature combinations of patent claims with the pertinent prior art. For proof of concept, the process has been tested successfully on an US-application claiming a method for raising a crane boom which can be categorized as non-patentable with regard to the requirement of non-obviousness. The result is consistent with that of a USPTO patent examiner, which underpins that at least under certain conditions not only patent examiners but also applicants and third parties can assess the chance and scope of protection for claimed inventions and patent applications with regard to patentability by our process.

In this paper, we propose a responsible intellectual property (IP) strategy (R-IPS) framework based on five exploratory case studies of sustainable companies in energy, nutrition, consumer electronics, manufacturing and water treatment sectors. These companies responsibly use IP assets to create positive social and environmental impact (or reduce negative impact), and unlock new opportunities for financial (economic) gains. The extent to which firms today use IP rights such as patents and trademarks, and IP more broadly (including informal intellectual assets such as data and trade secrets, and contracts) as a strategic tool to facilitate sustainability remains to be understood better. More specifically, we need understanding of how companies could, or even should manage and use their IP more responsibly in these changing times during which transitions towards sustainable development are so thoroughly and urgently needed. The proposed framework defined using five dimensions namey deliberation/intention, sustainability IP alignment, flexibility and timing, inclusiveness, and co-creation can support managerial decision-making in formulating or re-designing IP strategies to increase organization's social and environmental impact, maximising their contributions (e.g. accelerating transitions) towards building sustainable economies.

The selection of industry partners for Research and Development (R&D) is a challenging task for many organizations. Present methods for partner-selection, based on patents, publications or company databases, do often fail for highly specialized SMEs. Our approach aims at calculating the technological similarity for partner discovery. We apply methods from Natural Language Processing (NLP) on companies’ website texts. We show that the deep-learning language model BERT outperforms other methods at this task. Tested against expert-proven ground truth, it achieves an F1-score up to 0.90. Our results imply that website texts are useful for the purpose of estimating the similarity between companies. We see great potential in the scalability of the semantic analysis of company website texts.

The purpose of this study was to investigate the development of biotechnologies related to the bioremediation of soil contaminated by agrochemicals using fungi through technological prospecting with patent mapping and analysis of scientific products. Due to the high complexity of biotechnologies involving microorganisms, it is observed that the state of the art is still in development, with several technological advantages already elucidated and important challenges to be overcome. Patent mapping revealed that the number of granted or pending patents showed a positive trend. In this context, a high number of patent documents were observed that describe processes and methods aimed at the preparation and application of fungi in soil bioremediation, as well as biotechnologies that use consortia of different strains of fungi or fungi and bacteria and advanced approaches involving genetic engineering. In short, it was found that inventions related to the investigated biotechnology have been protected mainly in China, the USA, Japan, and the European Union, with emphasis on the technological impact of patents in India. Finally, the findings indicated that soil bioremediation with the use of fungi presents a potential for development due to a series of technological and environmental aspects.