Community Detection and Growth Potential Prediction from Patent Citation Networks

The scoring of patents is useful for technology management analysis. Therefore, a necessity of developing citation network clustering and prediction of future citations for practical patent scoring arises. In this paper, we propose a community detection method using the Node2vec. And in order to analyze growth potential we compare three ''time series analysis methods'', the Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of our experiments, we could find common technical points from those clusters by Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model was higher than that of other models.Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other author

The Productivity Impact of Skills in English Manufacturing, 2001: Evidence from Plant-Level Matched Data

Microeconomic analyses of productivity for the UK have generally not been able to control for the quality of the labour input, primarily due to data availability, and yet the supply of suitably skilled labour is thought to be a major contributing factor to productivity levels. This paper combines the Annual Respondents Database with the Employers’ Skills Survey for 2001, which allows for a more detailed analysis of the role of skills in determining plant level productivity. Using an augmented Cobb-Douglas production function, the analysis shows that plants experiencing skills shortages were generally less productive than those who did not perceive a skills gap, having controlled for industry and regional effects. In more detail, the analysis reveals some interesting results: the impact that skills gaps have on productivity vary by industry, and higher qualifications do not always result in higher productivity, although innovative plants are seen to be on average 5 per cent more productive, as a result of their more qualified workforce.

Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks

Modeling and forecasting forward citations to a patent is a central task for the discovery of emerging technologies and for measuring the pulse of inventive progress. Conventional methods for forecasting these forward citations cast the problem as analysis of temporal point processes which rely on the conditional intensity of previously received citations. Recent approaches model the conditional intensity as a chain of recurrent neural networks to capture memory dependency in hopes of reducing the restrictions of the parametric form of the intensity function. For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent. In this paper, we propose a sequence-to-sequence model which employs an attention-of-attention mechanism to capture the dependencies of these multiple time sequences. Furthermore, the proposed model is able to forecast both the timestamp and the category of a patent's next citation. Extensive experiments on a large patent citation dataset collected from USPTO demonstrate that the proposed model outperforms state-of-the-art models at forward citation forecasting