Intellectual property customer counseling is an important public service that supports the creation of intellectual property rights and protection of the rights and interests of applicants and rights holders. To effectively support customers and secure the use of counseling content as a policy, counseling contents are classified according to certain criteria. Until 2020, it was professional counselors who directly classified these contents, but 2021 saw a shift toward automatic classification based on text analysis (TA) of the consultation texts. However, an investigation as to the distribution of counseling case classification over the past five years showed some differences between the 2018–2020 distribution, classified by professional counselors, and the 2021–2022 distribution, automatically classified by TA. Therefore, this study investigated how to improve the performance of the automatic classification system using BERT, a transformer-based AI model. After fine-tuning the BERT model, which was pre-trained using patent counseling text data and professional counselor classification values data, it was observed that the BERT’s automatic classification distribution was more similar to that of professional counselors than the classification distribution of the existing TA. These results show that the future application of the “Patent Consultation Classification BERT,” a tentative name for the model, to automatic patent consultation classification may yield a better performance than the current TA method. Furthermore, if the automatic classification results become more reliable through the use of this AI model, the purpose behind the policy for the automation of this procedure—namely easing the burden and improving the efficiency of professional counselors—may be achieved with improved continuity and stability. This may then enable a more accurate identification of the current status of patent customer counseling services and customer needs.