Predicting the life cycle of technologies from patent data

Abstract: Analysis of patent documents is one way to learn about trends in the evolutionof technologies. In this thesis, we propose a mixture of life cycle Poisson modelfor predicting the life cycle of technologies from patent count data. The aim is topredict the life cycle of technologies and determine the stage of the technology inthe development S-curve. The model is constructed from historical data on patentpublications of technologies and also from experts’ belief of life cycle of technologies. The methods used to estimate the model are based on Bayesian methods, inparticular we use a combination of Gibbs sampling and slice sampling to simulatefrom the posterior distribution of the model parameters. We apply the model on adataset of 123 technologies from the electricity sector. As a preliminary exploratorystep clustering analysis is also applied on the dataset. Finally we evaluate the modelhow it performs to predict the trend of life cycle of technologies based on differentbase years. Results reveal that the model is capable of predicting the life cycleof technologies based on its different stages. However, the predictions of expectedbehavior become more accurate when more data is used to construct the prediction.