Koichi Kamijo, International Professional University of Technology in Tokyo, Japan
We propose a model to improve estimation accuracy of the future sales volume, focusing on pharmaceutical products, from their patents. Our approach is based on an analysis of patents obtained in the early development stages of the products. The development of pharmaceuticals often takes a long time (up to several decades in some cases), and the costs are huge, even exceeding one billion USD for just one product. Therefore, it is strongly desirable to estimate future sales volume at an early stage. One piece of information potentially useful for the estimation is the brand, i.e., the name of the developing company. Our model learns the sales volume and words used in multiple patent specifications and also focuses on the extent to which “seasonal” words are used. Experiments showed that our model much improved the accurately of the sales volume estimation compared with the case of just estimating from its brand name.
Sales Estimation, Pharmaceuticals, Patents, Natural Language Processing, Deep Learning.