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Abstract

Indicators based on non-patent references (NPRs) are increasingly being used for measuring and assessing science–technology interactions. But NPRs in patent documents contain noise, as not all of them can be considered ‘scientific’. In this article, we introduce the results of a machine-learning algorithm that allows identifying scientific references in an automated manner. Using the obtained results, we analyze indicators based on NPRs, with a focus on the difference between NPR- and scientific non-patent references-based indicators. Differences between both indicators are significant and dependent on the considered patent system, the applicant country and the technological domain. These results signal the relevancy of delineating scientific references when using NPRs to assess the occurrence and impact of science–technology interactions.

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Abstract  

The measurement of textual patent similarities is crucial for important tasks in patent management, be it prior art analysis, infringement analysis, or patent mapping. In this paper the common theory of similarity measurement is applied to the field of patents, using solitary concepts as basic textual elements of patents. After unfolding the term ‘similarity’ in a content and formal oriented level and presenting a basic model of understanding, a segmented approach to the measurement of underlying variables, similarity coefficients, and the criteria-related profiles of their combinations is lined out. This leads to a guided way to the application of textual patent similarities, interesting both for theory and practice.

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Abstract

For certain tasks in patent management it makes sense to apply a quantitative measure of textual similarity between patents and/or parts thereof: be it the analysis of freedom to operate, the analysis of technology convergence, or the mapping of patents for strategic purposes. In this paper we intend to outline the process of measuring textual patent similarity on the basis of elements referred to as ‘combined concepts’. We are going to use this process in various operations leading to design decisions, and shall also provide guidance regarding these decisions. By way of two applications from patent management, namely the prioritization of patents and the analysis of convergence between two technological fields, we mean to demonstrate the crucial importance of design decisions in terms of patent analysis results.

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Abstract

Given that in terms of technology novel inventions are crucial factors for companies; this article contributes to the identification of inventions of high novelty in patent data. As companies are confronted with an information overflow, and having patents reviewed by experts is a time-consuming task, we introduce a new approach to the identification of inventions of high novelty: a specific form of semantic patent analysis. Subsequent to the introduction of the concept of novelty in patents, the classical method of semantic patent analysis will be adapted to support novelty measurement. By means of a case study from the automotive industry, we corroborate that semantic patent analysis is able to outperform available methods for the identification of inventions of high novelty. Accordingly, semantic patent information possesses the potential to enhance technology monitoring while reducing both costs and uncertainty in the identification of inventions of high novelty.

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