Purpose:This research proposes a patent portfolio analysis model based on the legal status information to chart out a competitive landscape in a particular field,enabling organizations to position themselves within th...Purpose:This research proposes a patent portfolio analysis model based on the legal status information to chart out a competitive landscape in a particular field,enabling organizations to position themselves within the overall technology landscape.Design/methodology/approach:Three indicators were selected for the proposed model:Patent grant rate,valid patents rate and patent maintenance period.The model uses legal status information to perform a qualitative evaluation of relative values of the individual patents,countries or regions’ technological capabilities and competitiveness of patent applicants.The results are visualized by a four-quadrant bubble chart To test the effectiveness of the model,it is used to present a competitive landscape in the lithium ion battery field.Findings:The model can be used to evaluate the values of the individual patents,highlight countries or regions’ positions in the field,and rank the competitiveness of patent applicants in the field.Research limitations:The model currently takes into consideration only three legal status indicators.It is actually feasible to introduce more indicators such as the reason for invalid patents and the distribution of patent maintenance time and associate them with those in the proposed model.Practical implications:Analysis of legal status information in combination of patent application information can help an organization to spot gaps in its patent claim coverage,as well as evaluate patent quality and maintenance situation of its granted patents.The study results can be used to support technology assessment,technology innovation and intellectual property management.Originality/value:Prior studies attempted to assess patent quality or competitiveness by using either single patent legal status indicator or comparative analysis of the impacts of each indicator.However,they are insufficient in presenting the combined effects of the evaluation indicators.Using our model,it appears possible to get a more complete and objective picture of the current competitive situation.展开更多
A forecasting system of patent application counts is studied in this paper. The optimization model proposed in the research is based on support vector machines （SVM）, in which cross-validation algorithm is used for ...A forecasting system of patent application counts is studied in this paper. The optimization model proposed in the research is based on support vector machines （SVM）, in which cross-validation algorithm is used for preferences selection. Results of data simulation show that the proposed method has higher forecasting precision power and stronger generalization ability than BP neural network and RBF neural network. In addi- tion, it is feasible and effective in forecasting patent application counts.展开更多
基金supported by the Chinese Academy of Sciences(Grant No.:Y110071001)
文摘Purpose:This research proposes a patent portfolio analysis model based on the legal status information to chart out a competitive landscape in a particular field,enabling organizations to position themselves within the overall technology landscape.Design/methodology/approach:Three indicators were selected for the proposed model:Patent grant rate,valid patents rate and patent maintenance period.The model uses legal status information to perform a qualitative evaluation of relative values of the individual patents,countries or regions’ technological capabilities and competitiveness of patent applicants.The results are visualized by a four-quadrant bubble chart To test the effectiveness of the model,it is used to present a competitive landscape in the lithium ion battery field.Findings:The model can be used to evaluate the values of the individual patents,highlight countries or regions’ positions in the field,and rank the competitiveness of patent applicants in the field.Research limitations:The model currently takes into consideration only three legal status indicators.It is actually feasible to introduce more indicators such as the reason for invalid patents and the distribution of patent maintenance time and associate them with those in the proposed model.Practical implications:Analysis of legal status information in combination of patent application information can help an organization to spot gaps in its patent claim coverage,as well as evaluate patent quality and maintenance situation of its granted patents.The study results can be used to support technology assessment,technology innovation and intellectual property management.Originality/value:Prior studies attempted to assess patent quality or competitiveness by using either single patent legal status indicator or comparative analysis of the impacts of each indicator.However,they are insufficient in presenting the combined effects of the evaluation indicators.Using our model,it appears possible to get a more complete and objective picture of the current competitive situation.
基金Sponsored by "985" Philosophy and Social Science Innovation Base of the Ministry of Education of China (107008200400024)
文摘A forecasting system of patent application counts is studied in this paper. The optimization model proposed in the research is based on support vector machines （SVM）, in which cross-validation algorithm is used for preferences selection. Results of data simulation show that the proposed method has higher forecasting precision power and stronger generalization ability than BP neural network and RBF neural network. In addi- tion, it is feasible and effective in forecasting patent application counts.
