Abstract

Based on relevant literature and theoretical consideration on management for patent resources, a theory of inter-enterprise collaborative management for patent resources (ICMPR) based on multi-agent was proposed. We put forward 4 main function modules, including collaborative recommendation, collaborative analysis, collaborative application and system server management, to support ICMPR. The ICMPR can be good to solve those tough and important problems, such as patent cross licensing among enterprises, early warning analysis of patents, collaborative technology innovation and other issues. The ICMPR can provide enterprises with practical methodology, and improve the overall competitiveness of world manufacturing industry.

Highlights

  • In knowledge-based economy age, the management of intellectual properties has become more and more important for an enterprise

  • This paper study in the patent domain, and the research question is inter-enterprise collaborative management for patent resources (ICMPR)

  • We can see from fig.2, collaborative recommendation for patent resources is the basis of the whole system, which offers important premise for collaborative analysis, the application of patent resources

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Summary

Introduction

In knowledge-based economy age, the management of intellectual properties has become more and more important for an enterprise. Patent is the core part of intellectual properties, and it is very important intangible assets. If the related patent information can be effectively used, modern enterprises will raise the starting point of scientific research and save funds and time in the development course. They will get their own intellectual property rights, reliable technical support and high value-added products to improve the ability of market competitiveness of enterprises (Soo et al, 2006). This paper study in the patent domain, and the research question is inter-enterprise collaborative management for patent resources (ICMPR)

Related work
Methodology
Function model of ICMPR based on multi-agent
Collaborative recommendation
Collaborative analysis
Collaborative application
System Service Management
Findings
Conclusion
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