Ontology-based GFML agent for patent technology requirement evaluation and recommendation
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Patent technology requirement evaluation and recommendation are critical for patent strategy, patent management, and patent usage in an organization. This paper proposes a patent technology evaluation and recommendation agent based on a soft-computing approach to enhance patent expansibility and technology transfer. First, we investigate the relationship between patent technology and patent owners, such as academic institutes or organizations, and integrate the collected patent data with the characteristics of organizations to establish a popular patent ontology for general academic institutes or organizations. Then, the patent’s suitability for a specific organization is determined based on concepts extracted using Chinese Knowledge Information Processing. Next, we refer to the Japan Patent Office evaluation index and intellectual property quotient to describe the knowledge base and rule base of the patent quality evaluation agent by using fuzzy markup language (FML). A comprehensive patent quality evaluation mechanism is implemented, and the genetic algorithm is adopted to improve the performance of the proposed agent. Additionally, the patent requirement level evaluation mechanism infers the patent requirement level according to the basic information of an organization. Finally, we present a novel FML-based patent requirement recommendation agent to recommend a patent for an organization by considering the suitability of the patent technology for such an organization, the results of the comprehensive patent quality evaluation process, and the results of the evaluation of the demander’s patent requirements. According to the results, the proposed agent is feasible for patent recommendation. In the future, we will combine an intelligent robot with the GFML agent to assist humans or organizations in recommending an appropriate patent.
KeywordsOntology Genetic fuzzy markup language Patent evaluation Patent recommendation Intelligent agent
The authors would like to thank the financially support sponsored by the Ministry of Science Technology (MOST) of Taiwan under the grant MOST 106-3114-E-024-001, MOST 106-2221-E-024-019, and MOST 105-2622-E-024-003-CC2. Additionally, the authors also would like to express their great appreciation to the members of R&D office of National University of Tainan (NUTN), Taiwan, for their valuable comments and help on patent’s application.
Compliance with Ethical Standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals participants performed by any of the authors.
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