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http://dx.doi.org/10.7471/ikeee.2022.26.4.545

A patent application filing forecasting method based on the bidirectional LSTM  

Seungwan, Choi (R&D Team, Saeron S&I)
Kwangsoo, Kim (Dept. of Electronic Engineering, Hanbat National University)
Sooyeong, Kwak (Dept. of Electronic Engineering, Hanbat National University)
Publication Information
Journal of IKEEE / v.26, no.4, 2022 , pp. 545-552 More about this Journal
Abstract
The number of patent application filing for a specific technology has a good relation with the technology's life cycle and future industry development on that area. So industry and governments are highly interested in forecasting the number of patent application filing in order to take appropriate preparations in advance. In this paper, a new method based on the bidirectional long short-term memory(LSTM), a kind of recurrent neural network(RNN), is proposed to improve the forecasting accuracy compared to related methods. Compared with the Bass model which is one of conventional diffusion modeling methods, the proposed method shows the 16% higher performance with the Korean patent filing data on the five selected technology areas.
Keywords
Patent analysis; Forecasting emerging technology; Deep learning; Bidirectional LSTM Neural Network; Bass Model;
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