Examine the Effectiveness of Patent Embedding-Based Company Comparison Method

Abstract

A company’s benchmarking strategy is significantly determined by how it measures technological similarity. Researchers have measured the technological similarity between companies using a vector composed of the classification codes of patents that each company owns. However, patent classification code-based company comparison methods do not consider the text in patents and thus may not find similar companies accurately. To solve this problem, this study suggests a patent embedding-based company comparison method. The suggested method uses a text embedding model to vectorize the text in patents and calculates technological similarity based on the embedding vector. We examine the effectiveness of the suggested method by comparing it with the conventional patent classification code-based method. From the validation results for 11,227 Korean companies listed in the Korea Data Analysis, Retrieval, and Transfer system (DART), we find that the suggested method effectively retrieves technologically similar companies.

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