Patent Analytics Based on Feature Vector Space Model: A Case of IoT

The number of approved patents worldwide increases rapidly each year, which requires new patent analytics to efficiently mine the valuable information attached to these patents. Vector space model (VSM) represents documents as high-dimensional vectors, where each dimension corresponds to a unique term. While originally proposed for information retrieval systems, VSM has also seen wide applications in patent analytics, and used as a fundamental tool to map patent documents to structured data. However, VSM method suffers from several limitations when applied to patent analysis tasks, such as loss of sentence-level semantics and curse-of-dimensionality problems. In order to address the above limitations, we propose a patent analytics based on feature vector space model (FVSM), where the FVSM is constructed by mapping patent documents to feature vectors extracted by convolutional neural networks (CNN). The applications of FVSM for three typical patent analysis tasks, i.e., patents similarity comparison, patent clustering, and patent map generation are discussed. A case study using patents related to Internet of Things (IoT) technology is illustrated to demonstrate the performance and effectiveness of FVSM. The proposed FVSM can be adopted by other patent analysis studies to replace VSM, based on which various big data learning tasks can be performed

Generating Information Relation Matrix Using Semantic Patent Mining for Technology Planning: A Case of Nano-Sensor

For the purposes of technology planning and research and development strategy development, we present a semi-automated method that extracts text information from patent data, uses natural language processing to extract the key technical information of the patent, and then visualizes this information in a matrix form. We tried to support qualitative analysis of patent contents by extracting functions, components, and contexts, which are the most important information about inventions. We validated the method by applying it to patent data related to nanosensors. The matrix can emphasize technical information that have not been exploited in patents, and thereby identify development opportunities.111Ysciescopu

Using ontologies to map between research data and policymakers’ presumptions: the experience of the KNOWMAK project

Understanding knowledge co-creation in key emerging areas of European research is critical for policy makers wishing to analyze impact and make strategic decisions. However, purely data-driven methods for characterising policy topics have limitations relating to the broad nature of such topics and the differences in language and topic structure between the political language and scientific and technological outputs. In this paper, we discuss the use of ontologies and semantic technologies as a means to bridge the linguistic and conceptual gap between policy questions and data sources for characterising European knowledge production. Our experience suggests that the integration between advanced techniques for language processing and expert assessment at critical junctures in the process is key for the success of this endeavour

Functional technology foresight. A novel methodology to identify emerging technologies

The speed and complexity of the technology evolution faced by modern societies need new approaches to the analysis and understanding of the world. Indeed, an exclusive focus on technological goals can miss to recognize all the stakeholders of a technology and address real user needs; moreover, on the one hand low signals are becoming more and more important in fast evolving markets, on the other hand the excess of hype, fashions, or vested interests sometimes deeply alter indicators. However, the so called Big Data promise to be a huge low cost set of valuable information, available and affordable to all (SMEs included). But, analyzing them is not trivial especially if we deal with academic papers and patents. To tackle these issues, the present paper proposes to apply a powerful methodological tool called Functional Analysis to the Technology Foresight process. Actually the rigorous study of the functions, that an artefact should perform to satisfy the user needs, provides a universal and thus unifying point of view, which is able to correlate the user perspective on the product with its technical features. Functional reasoning has been applied to (i) detect possible patterns of development, spotting missing elements and highlighting strengths as well as potential sources of failure; (ii) to enhance traditional bibliometric tools such as the analysis of S-curves and (iii), integrated with a natural language processing analysis toolchain, tailored for patent documents, to identify emerging technologies. The paper describes the functional approach to technology foresight activity, presents how to integrate it with text mining algorithms and experts’ domain knowledge, and finally discusses its benefits in the context of Technology Foresight also from an economic point of view, showing that oresight is affordable also for Small and Medium Enterprises

Patent Keyword Extraction Algorithm Based on Distributed Representation for Patent Classification

Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction algorithms are based on discrete bag-of-words type of word representation of the text. In this paper, we propose a patent keyword extraction algorithm (PKEA) based on the distributed Skip-gram model for patent classification. We also develop a set of quantitative performance measures for keyword extraction evaluation based on information gain and cross-validation, based on Support Vector Machine (SVM) classification, which are valuable when human-annotated keywords are not available. We used a standard benchmark dataset and a homemade patent dataset to evaluate the performance of PKEA. Our patent dataset includes 2500 patents from five distinct technological fields related to autonomous cars (GPS systems, lidar systems, object recognition systems, radar systems, and vehicle control systems). We compared our method with Frequency, Term Frequency-Inverse Document Frequency (TF-IDF), TextRank and Rapid Automatic Keyword Extraction (RAKE). The experimental results show that our proposed algorithm provides a promising way to extract keywords from patent texts for patent classification