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This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.

This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.

The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.

The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.

The systematic stealing of patents, copyright ownership and industrial secrets remain a complex and costly dilemma for corporate interests worldwide. Analyses the role which organizations in the Pacific Region have played, both in the cause and control of these abuses. Western manufacturers, artists and writers have charged that interests in Japan, Korea, Singapore and other nations have repeatedly copied software, musical compositions, patents and other intellectual products and that government punishment against perpetrators of these acts has been virtually non‐existent. Further, industrial spying remains a serious threat to the viability of numerous emerging companies lacking sophisticated preventive measures. These companies are vulnerable to legal manoeuvres which have effectively negated any attempt to protect one′s own creations. Concludes with the identification of typologies of these violations so that readers can implement strategic, preventive measures.

The purpose of this paper is to develop an approach that can detect abnormal deviations in the time series models for technology forecasting. The detected modifications provide a basis for understanding the determinants and impact of the corresponding change.

The proposed approach is based on monitoring residual values (the difference between the observation and the forecasted value) continuously using statistical control charts (SCCs). The residuals that are out of the expected limits are considered an alert indicating a remarkable change. To demonstrate the use of the proposed approach, a time series model was fitted to a number of TV-related patent counts. Subsequently, model residuals were used to determine the limits of the SCCs.

A number of patents granted in the year 2012 violated the upper control limit. A further analysis has shown that there is a linkage between the abnormal patent counts and the emergence of LCD TVs.

Change in technology may dramatically affect the accuracy of a forecasting model. The need for a parameter update indicates a significant change (emergence or death of a technology) in the technological environment. This may lead to the revision of managerial actions in R&D plans and investment decisions.

The proposed methodology brings a novel approach for abnormal data detection and provides a basis for understanding the determinants and impact of the corresponding change.

The purpose of this study is to identify and characterize the role of both original equipment manufacturer (OEM) and module supplier (MS) knowledge in the smartphone industry. In particular, this study aims to evaluate which of the two actors possesses the knowledge that has the greatest impact on the market satisfaction.

This study explores and combines the concepts of modularity and knowledge management by investigating the patent portfolio of 16 leading smartphone OEMs and 144 MSs. The applied methodology is based on the content analysis of patent data to extract information on both OEM’s and MS’s component knowledge.

The results show that, although its components are purchased from external MSs, the OEM should preserve both a general and specific concentration of component knowledge, as well as on the end product, to achieve a greater market satisfaction. Moreover, a positive direct relationship was found for the MS between the general concentration of component knowledge and the market satisfaction.

The novelty of this study is to segment the knowledge of both the OEM and the MS on multiple levels. To the best of the authors’ knowledge, this is one of the first studies that investigates the end product and component knowledge of both actors by filtering patent data using text-mining techniques. The originality of this work is to intercept the relationship between the different shades of knowledge of each actor and the market satisfaction.

Radio frequency identification (RFID) is a technology for tracking objects that is expected to be widely adopted in very near future. A reader device sends probes to a set of RFID tags, which then respond to the request. A tag is recognized only when it is the only one to respond to the probe. Only reader has collision detection capability. The problem considered here is to minimize the number of probes necessary for reading all the tags, assuming that the number of tags is known in advance.

Well known binary and n‐ary partitioning algorithms can be applied to solve the problem for the case of known number of tags. A new randomized hybrid tag identification protocol has been proposed, which combines the two partitioning algorithms into a more efficient one. The new scheme optimizes the binary partition protocol for small values of n (e.g. n=2, 3, 4). The hybrid scheme then applies n‐ary partition protocol on the whole set, followed by binary partition on the tags that caused collision.

It is analytically proved that the expected number of time slots in the hybrid algorithm with known number of users is less than 2.20 n. Performance of these algorithms was also evaluated experimentally, and an improvement from en to approximately 2.15 n was obtained.

The algorithm shown here is efficient both by theory and practice and outperforms existing ones.

Many abstract journals include patent specifications in their coverage thereby providing references to the most up‐to‐date sources of information which might otherwise be ignored by technicians in smaller enterprises and by students. A survey of some of these journals shows that while many do provide good coverage and indexing, the selection criteria for the patent specifications are sometimes questionable and bibliographic details are inaccurate or misleading. Standards for the identification of patent documents have been developed by the World Intellectual Property Organization and are in world‐wide use by patent offices. Their adoption by abstract journals is strongly recommended.

The past few decades have witnessed a phenomenal progress in our understanding of employee mobility as a critical driver and consequence of various outcomes for individuals, organizations, industries, and economies. In the process, researchers have tackled several important issues in conducting empirical research on employee mobility. This chapter provides a critical discussion of the extant literature focusing on five broad areas: identification of mobility, timing of mobility, outcomes of mobility and their operationalization, model identification, and other related issues. In doing so, this article identifies some of the empirical choices and methodologies adopted in prior mobility studies, evaluates those practices, and suggests areas of improvements for the practice. It is hoped that future studies will benefit from this chapter's insight by building on the best practices from the literature while continuously and successfully tackling the issues that have been challenging the researchers on this increasingly important topic of scholarly inquiry.

This study aims to find technologically important patent identification methods and indicators early and efficiently to grasp the technical qualitative level of patents, which are output indicators of research and development (R&D) results.

This paper reports on two methods for distinguishing important patents and the indicators obtained from those methods. One of the discrimination methods is Heckman's two-step estimation procedure. The second method is to find the centrality of each patent by network analysis of the citation relationship between publications and to find the importance from the magnitude of the centrality value.

In Heckman's analysis, the number of citations within three years after publication and the applicant's right acquisition/maintenance motivation index had positive effects on patent importance. The discriminative indicators of important patents by network analysis were degree centrality, mediation centrality, proximity centrality and transit values in the aggregated subnetworks. These two analytical methods are in a relationship that can complement each other's shortcomings. To efficiently evaluate the qualitative importance of patents, it is recommended to use these two methods together.

The indicators of important technical patents might change depending on the technical field. Future studies can apply this research to multiple technical fields to improve robustness and to construct an algorithm that can efficiently evaluate the quality of patents.

This study's results can be useful for grasping the patent position of the company or competitors numerically and for quantitatively evaluating the quality of R&D activities. Furthermore, it is possible to streamline the routine for an exploratory search of a huge number of patents. For example, it could be useful for detecting changes in the paradigm of specific technical knowledge, evolving the genealogy of technical knowledge and creating patent maps for new R&D. These methods greatly increase the effectiveness of technical knowledge information, which is the basis of R&D. In addition, the results of this study can help in evaluating patented assets.

This study confirmed the development process of technical knowledge. It is a fact that sharing, sympathy and mutual trust for technical issues and technical values are created among professional engineers and researchers inside and outside the organization, and their preferences and interactions develop and expand technical knowledge. Understanding the process of development and the evolution of this technical knowledge gives hints, such as expanding the discretionary power of engineers and researchers regarding corporate secrets, or reviewing the balance between control and independence, to solve Japanese management problems, which are often closed and monetized in R&D activities.

This study presents a scoring of the technical significance of patents by combining the two analytical methods. In addition, there are proposals as a method for detecting changes in the genealogy and paradigm of technical knowledge. As an analysis method, it is a new proposal that has never existed before.

In recent years patents have become a very popular data source for forecasting technological changes. However, since a vast amount of patents are “worthless” (Moore, 2005), there is a need to identify the promising ones. For this purpose, previous approaches have mainly used bibliographic data, thus neglecting the benefits of textual data, such as instant accessibility at patent disclosure. To leverage these benefits, this study aims to develop an approach that uses textual patent data for predicting promising patents.

For the identification of promising patents, the authors propose a novel approach which combines link prediction with textual patent data. Thereby the authors are able to predict the emergence of hitherto unmentioned bigrams. By mapping these future bigrams to recent patents, the authors are able to distinguish between promising and nonpromising patents. To validate this approach, the authors apply the methodology to the case example of camera technology.

The authors identify stochastic gradient descent as a suitable algorithm with both a receiver operating characteristic area under curve score and a positive predictive value of 78%, which outperforms chance by a factor of two. In addition, the authors present promising camera patents for diverse application fields, such as cameras for surgical systems, cameras for rearview vision systems in vehicles or light amplification by stimulated emission of radiation detection and ranging cameras for three-dimensional imaging.

This study contributes in at least three directions to scholarship. First, the authors introduce a novel approach by combining link prediction with textual patent analysis and, in this way, leverage the benefits of both worlds. Second, the authors add to all theories that regard novel technologies as a recombination of existing technologies in presenting word combinations from textual data as a suitable instrument for revealing recombination in patents. And third, the approach can be used by scholars as a complementary or even integrative tool with conventional forecasting methods like the Delphi technique or Scenario planning.

At least three practical implications arise from the study. First, incumbent firms of a technology branch can use this approach as an early-warning system to identify technological change and to identify opportunities related to their company’s technological competence and provide inspiration for new ideas. Second, companies seeking to tap into new markets may also be interested in the approach as managers could anticipate whether their company’s technological competences are in line with upcoming trends. Third, the approach may be used as a supportive tool for various purposes, such as investment decisions or technology life cycle analysis.

The approach introduces textual patent data as suitable means for forecasting activities. As the statistical validation reveals, the promising patents identified by the approach are cited significantly more often than patents with less promising prospects.

The purpose of this paper is to propose a quantitative method for identifying multiple and hierarchical knowledge trajectories within a specific technological domain (TD).

The proposed method as a patent-based data-driven approach is basically based on patent classification systems and patent citation information. Specifically, the method first analyzes hierarchical structure under a specific TD based on patent co-classification and hierarchical relationships between patent classifications. Then, main paths for each sub-TD and overall-TD are generated by knowledge persistence-based main path approach. The all generated main paths at different level are integrated into the hierarchical main paths.

This paper conducted an empirical analysis by using Genome sequencing technology. The results show that the proposed method automatically identifies three sub-TDs, which are major functionalities in the TD, and generates the hierarchical main paths. The generated main paths show knowledge flows across different sub-TDs and the changing trends in dominant sub-TD over time.

To the best of the authors’ knowledge, the proposed method is the first attempt to automatically generate multiple hierarchical main paths using patent data. The generated main paths objectively show not only knowledge trajectories for each sub-TD but also interactive knowledge flows among sub-TDs. Therefore, the method is definitely helpful to reduce manual work for TD decomposition and useful to understand major trajectories for TD.