Search results

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.

States there is a huge gap between recognizing there are potential profits in IP, and then realizing that potential, because the value potential for IP is not a new idea – consultants and analysts have been pounding on that message for decades. Stresses three phrases within, discipline, credibility, and shared expectations – are the result of an on‐going perspective and are used every day.

This paper presents an approach for performing knowledge discovery in texts through qualitative and quantitative analyses of high‐level textual characteristics. Instead of applying mining techniques on attribute values, terms or keywords extracted from texts, the discovery process works over conceptss identified in texts. Concepts represent real world events and objects, and they help the user to understand ideas, trends, thoughts, opinions and intentions present in texts. The approach combines a quasi‐automatic categorisation task (for qualitative analysis) with a mining process (for quantitative analysis). The goal is to find new and useful knowledge inside a textual collection through the use of mining techniques applied over concepts (representing text content). In this paper, an application of the approach to medical records of a psychiatric hospital is presented. The approach helps physicians to extract knowledge about patients and diseases. This knowledge may be used for epidemiological studies, for training professionals and it may be also used to support physicians to diagnose and evaluate diseases.

This study proposes an approach to solve the fundamental problem in using query-based methods (i.e. searching engines and patent retrieval tools) to screen patents of information and communication technology in construction (ICTC). The fundamental problem is that ICTC incorporates various techniques and thus cannot be simply represented by man-made queries. To investigate this concern, this study develops a binary classifier by utilizing deep learning and NLP techniques to automatically identify whether a patent is relevant to ICTC, thus accurately screening a corpus of ICTC patents.

This study employs NLP techniques to convert the textual data of patents into numerical vectors. Then, a supervised deep learning model is developed to learn the relations between the input vectors and outputs.

The validation results indicate that (1) the proposed approach has a better performance in screening ICTC patents than traditional machine learning methods; (2) besides the United States Patent and Trademark Office (USPTO) that provides structured and well-written patents, the approach could also accurately screen patents form Derwent Innovations Index (DIX), in which patents are written in different genres.

This study contributes a specific collection for ICTC patents, which is not provided by the patent offices.

The proposed approach contributes an alternative manner in gathering a corpus of patents for domains like ICTC that neither exists as a searchable classification in patent offices, nor is accurately represented by man-made queries.

A deep learning model with two layers of neurons is developed to learn the non-linear relations between the input features and outputs providing better performance than traditional machine learning models. This study uses advanced NLP techniques lemmatization and part-of-speech POS to process textual data of ICTC patents. This study contributes specific collection for ICTC patents which is not provided by the patent offices.

The purpose of this paper is the development of a patent-based supplier portfolio that can be used to evaluate and select suppliers on account of their technological competencies.

In addition to traditional approaches, the authors develop a supplier portfolio that characterizes suppliers according to the similarity between supplier's and OEM's technological competencies as well as their technological broadness. These variables are measured on the basis of patents, which constitute a valuable source of information in technology-driven industries. Contrary to existing binary measurement approaches, the authors’ portfolio uses semantic analyses to make use of the specific information provided in the patents' texts. The authors test this method in the field of gearings, which is a key driver for the automotive industry.

The authors identify six generic positions, characterizing specific risks for an OEM to become either technologically dependent or dependent on suppliers' production capacities. For each position the authors develop specific management strategies in face of the aforementioned risks. The approach helps OEMs navigate in the competitive landscape based on the most recent and publicly available information medium.

This work explicitly applies the construct of technological competencies to supplier evaluation and selection on the basis of portfolio approaches. Furthermore, the authors improve the use of patents for supplier evaluation in two respects: First, the authors analyze OEMs and upstream suppliers on an organizational level. Second, the authors utilize advanced semantic analysis to generate variables for the measurement of the criteria mentioned above.

This study aims to assess the technological capability of Chinese internet platforms (BAT: Baidu, Alibaba, Tencent) compared to US ones (GAFA: Google, Amazon, Facebook, Apple). More specifically, this study explores Baidu’s technological catching-up process with Google by analyzing their patent textual information.

The authors retrieved 26,383 Google patents and 6,695 Baidu patents from PATSTAT 2019 Spring version. The collected patent documents were vectorized using the Word2Vec model first, and then K-means clustering was applied to visualize the technological space of two firms. Finally, novel indicators were proposed to capture the technological catching-up process between Baidu and Google.

The results show that Baidu follows a trend of US rather than Chinese technology which suggests Baidu is aggressively seeking to catch up with US players in the process of its technological development. At the same time, the impact index of Baidu patents increases over time, reflecting its upgrading of technological competitiveness.

This study proposed a new method to analyze technology mapping and evolution based on patent text information. As both US and China are crucial players in the internet industry, it is vital for policymakers in third countries to understand the technological capacity and competitiveness of both countries to develop strategic partnerships effectively.

Exploiting a novel measure of innovation, the authors investigate whether independent directors improve innovation efficiency. This novel measure of innovation captures the extent to which the firm generates revenue from its research & development and is, therefore, more economically meaningful. The authors also use a text-based measure of innovation.

The authors rely on a quasi-natural experiment based on the passage of the Sarbanes-Oxley Act of 2002 that compelled certain firms to raise board independence. The difference-in-difference analysis is far less vulnerable to endogeneity and is more likely to show a causal influence, rather than a mere association.

The results show that more independent directors improve innovation efficiency significantly. Specifically, firms forced to raise board independence experienced a much higher increase in innovation than those not required to change their board composition. The authors also explore another novel measure of innovation, a text-based metric of innovation.

The research is original in several ways. First, the authors take advantage of an exogenous regulatory shock as a quasi-natural experiment. This approach is far less susceptible to endogeneity. Second, the authors use a novel measure of innovation efficiency, i.e. research quotient, which is more economically meaningful. Finally, the authors use a unique measure of innovation derived from powerful textual analysis.

The purpose of this paper is to evaluate innovations in intellectual property rights (IPR) databases, techniques and software tools, with an emphasis on selected new developments and their contribution towards achieving advantages for IPR management (IPRM) and wider social benefits. Several industry buzzwords are addressed, such as IPR-linked open data (IPR LOD) databases, blockchain and IPR-related techniques, acknowledged for their contribution in moving towards artificial intelligence (AI) in IPRM.

The evaluation, following an original framework developed by the authors, is based on a literature review, web analysis and interviews carried out with some of the top experts from IPR-savvy multinational companies.

The paper presents the patent databases landscape, classifying patent offices according to the format of data provided and depicting the state-of-art in the IPR LOD. An examination of existing IPR tools shows that they are not yet fully developed, with limited usability for IPRM. After reviewing the techniques, it is clear that the current state-of-the-art is insufficient to fully address AI in IPR. Uses of blockchain in IPR show that they are yet to be fully exploited on a larger scale.

A critical analysis of IPR tools, techniques and blockchain allows for the state-of-art to be assessed, and for their current and potential value with regard to the development of the economy and wider society to be considered. The paper also provides a novel classification of patent offices and an original IPR-linked open data landscape.

Content analysis, restricted within the limits of written textual documents (wtdca), is a field which is greatly in need of extensive interdisciplinary research. This would clarify certain concepts, especially those concerned with ‘text’, as a new central nucleus of semiotic research, and ‘content’, or the informative power of text. The objective reality (syntax) of the written document should be, in the cognitive process that all content analysis entails, interpreted (semantically and pragmatically) in an intersubjective manner with regard to the context, the analyst's knowledge base and the documentary objectives. The contributions of semiolinguistics (textual), logic (formal) and psychology (cognitive) are fundamental to the conduct of these activities. The criteria used to validate the results obtained complete the necessary conceptual reference panorama.

Identifying ideas for frugal innovations could substantially support engineers and managers in their work. Patents may be a promising source of ideas for frugal innovations. The purpose of this paper is to show how patents of this kind can be identified.

Prior work has developed a generic process model to identify frugal patent candidates. They use semantic analysis and a frugal thesaurus for searching and characterizing frugal patent candidates. The question is, whether and how this process model can be applied to a different industry successfully? The field of small household appliances is selected as a testbed because it is known for including frugal innovations while having a different business model than the industrial field in which the thesaurus was developed.

Applying the process model leads to a number of 22 strongly frugal patent candidates, which are manually evaluated based on three frugal characteristics. Due to this analysis, a high fraction of the candidates could be identified as frugal patents, which validates the process model. In addition, this paper outlines several options for enhancing the process model, e.g. by separating between a frugal search and a frugal evaluation thesaurus.

The process model to identify frugal patent candidates can be used by engineers and technology managers to find ideas for frugal innovations. This paper not only shows its applicability in the industry of small household appliances but gives the basis for its successful transfer to other industries.