Abstract: The article differentiates the patent industrialization probability with a quantitative analysis from the perspective of patent citation. The method of mathematical statistics is used to measure the importance and values of patents in the technology development process. The principle of identification and evaluation of patent industrialization is demonstrated. Time-independence, irreducibility and irreversibility of a patent citation network enable it to meet the conditions for convergence of Markov Chain. So Markov Chain is adopted to model the process of random browse in a patent citation network. The correspondence between a patent citation network and Markov Chain is established. And the calculation of patent industrialization probability is translated to calculate the stable distribution of the transfer matrix of Markov Chain. The iterative algorithm for computing the largest eigenvalues and eigenvectors of the transfer matrix is devised. The article collects 235 patents related to electron emitter manufacturing which were granted between 1976 and 2006 from the database of United States Patent and Trademark Office. The annual distribution of the patents is analyzed. The article queries from the patent data set of National Bureau of Economic Research to get 592 patent citations and uses the network analysis software Pajek to build a patent citation network as the experimental data. The experimental results show that, the proposed model is effective. It is consistent with the actual situations of patent industrialization, and can be used as the instructions of patent industrialization.

Key words: patent industrialization, Markov Chain, patent citation network, transfer matrix, electron emitter manufacturing