Bibliometric analysis of patent infringement retrieval model based on self-organizing map neural network algorithm

Abstract

Purpose

The purpose of this paper is to quickly retrieve the same or similar patents in a large patent database.

Design/methodology/approach

The research is carried out through the analysis of the issue of patent examination, the type of patent infringement search and theories related to patent infringement determination and text mining.

Findings

The results show that the model improves the speed of patent search. It can quickly, accurately and comprehensively retrieve the same or equivalent patents as the imported patent claims.

Research limitations/implications

The patent infringement detection mainly focuses on the measurement of patent similarity in the implementation method. It is not mature, and there is still much room for improvement in research.

Practical implications

The model improves the efficiency of patent infringement detection, increases the accuracy of detection and protects the interests of patent stakeholders.

Originality/value

This study has great significance for improving the efficiency of patent examiners.

Citation

Zhu, D. (2020), "Bibliometric analysis of patent infringement retrieval model based on self-organizing map neural network algorithm", Library Hi Tech, Vol. 38 No. 2, pp. 479-491. https://doi.org/10.1108/LHT-12-2018-0201