Product functional information based automatic patent classification: method and experimental studies

Abstract

In order to effectively extract the hidden information from the patent texts and to further provide this information to support the product innovation design process, this paper proposed an automatic patent classification method based on the functional basis and Naive Bayes theory. The functions of products are regarded as the innovation attributes, and the function co-reference relations of the patents in different areas are established. Patent classification methods are proposed based on the functions of products and the general steps of the patent classification process are proposed. In addition, three training methods are studied in the experiments, including multi-classification fully supervised training, multiple dichotomous supervised training and semi-supervised training. Through comparing and analyzing the experimental results, a patent text classifier is developed. In summary, this paper provides a general idea and the relevant technologies on how to build a patent knowledge space by automatically extracting and expanding the patent texts.

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