隨著知識經濟的發展，智慧財產研發成為企業競爭中重要的考量，現有的專利管理工具，著重於專利引用分析及專利申請管理；其缺乏專利保護、專利鑑價、技術授權等功能，針對企業智財管理中所需專利投資組合及技術發展預測亦無法支援。因此，組織要有效積極專利管理，須選擇一個合適的「智財管理經營平台 (Intellectual patent Management System, IPMS)」。目前已有數位學者運用模糊多屬性決策理論，進行軟體平台的評選，但傳統模糊邏輯要求決策者，針對各評估準則給予固定區間 (正面信心度) 的答案；但在專案評選初期，因資訊不完整，經常無法針對兩兩準則相對重要性給予評分，故本研究引進直覺式模糊集合 (Intuitionistic Fuzzy Set, IFS)，以提供決策者表達對問題的正面與負面信心度；此外，本研究介紹一個新的方案綜合滿意指標，整合所有決策者的滿意度 (concord) 及不滿意度 (discord)，以強化現有模糊排序法的鑑別能力，並運用敏感性分析加以驗證。最後舉一高科技單位的IPMS評選案例，進行方案綜合評鑑。由案例驗證得知，面對不完整資訊情況下，本模式可有效放寬決策者在不確定意見的表達，強化方案排序的鑑別能力。

The research and development of Intellectual Property (IP) becomes a significant concern for competition among enterprises increasing along with the popularity of knowledge-based economy. Current IP management systems mainly focus on analyzing patent citation and managing patent application, which might be neglect to identify the protection, valuation and licensing issues for patent assets, and also not to be fitting well for predicting investment portfolio and tendency of technology development. To effectively handle the patent management, enterprise needs to select an appropriate IPMS (IP Management System). Many researches have studied the selection issues of software platform. In general, the traditional fuzzy multiple criteria decision-making theory is employed to select software platform, which constraints the decision makers to express their ratings or scorings with positive confidence interval. However, decision makers often cannot discriminate the importance of criteria pairwise with incomplete information in the early stage of project development. In this paper, a new scheme for right selection of IPMS which exploits Intuitionistic Fuzzy Set (IFS) to assign their ratings based on decision maker’s positive and negative confidence. Then, an improved fuzzy ranking index is introduced to enhance the discrimination of current approach, which aggregates both concord and discord degree to calculate the synthetic alternative’s satisfaction degree for all decision makers. Furthermore, it is verified by the sensitivity analysis. Finally, a real case of IPMS selection for Hi-Tech organization is given to illustrate our approach. From the results of computations, this model not only can relax the expression of uncertainty of decision makers, but also improve the discrimination of current IFS ranking approach under incomplete information.