Trend Recognition of Technology Convergence Based on Hidden Markov Model
[Purpose/significance]Identifying the state of technology convergence(TC)and grasping the development trend of technol-ogy fusion is helpful in conducting innovation activities and benefiting from emerging technology.[Method/process]This study ana-lyzed the PATSTAT patent data to identify the technology convergence in 35 technological fields.Specifically,Hidden Markov Model(HMM)was adopted to estimate the hidden states of technology convergence based on the observations of complementary technology convergence and substitutability technology convergence.[Result/conclusion]The analysis results show that the hidden status of the technology convergence state has four types,namely closed,low-span,high-span and open.Over time,technological fields exhibit the characteristics of switching between different states.The transition from low-span state to high-span state or open state is the most common,reflecting the trend of integrating multiple technologies.[Innovation/limitation]The study innovatively adopts the HMM model to depict the state of technology convergence from a dynamic perspective and track its long-term trend.However,the actual ap-plication of the model needs to be further constructed according to the scene,and relevant exploration can be further conducted in sub-sequent studies.
NETL
NSTL
万方数据
