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This paper proposes a method that can reliably monitor the adoption of existing technology by term frequency-inverse document frequency (TF-IDF) and K-means clustering using cited patents. TF-IDF and K-means clustering can extract patent information when the number of patents is sufficiently large. When the number of patents is too small for TF-IDF and K-means clustering to be reliable, the method considers patents that were cited by the originally set of patents. The mixed set of citing patents and cited patents is the new subject of analysis. As a case study, we have focused in agricultural tractor in which new technologies were adopted to achieve automated driving. TF-IDF and K-means clustering alone failed to monitor the adoption of new technology but the proposed method successfully monitored it. We anticipate that our method can ensure the reliability of patent monitoring even when the number of patents is small.
Nam et al. (2017) studied this question.