研究生: |
劉奕恩 Yi-En Liu |
---|---|
論文名稱: |
專利因素對美國多方複審程序立案決定之影響 Effect of Patent Factors on Institution Decisions of Inter Partes Review |
指導教授: |
蔡鴻文
Hung-Wen Tsai |
口試委員: |
耿筠
Yun Ken 林季陽 Chi-yang Lin |
學位類別: |
碩士 Master |
系所名稱: |
應用科技學院 - 專利研究所 Graduate Institute of Patent |
論文出版年: | 2020 |
畢業學年度: | 108 |
語文別: | 中文 |
論文頁數: | 113 |
中文關鍵詞: | 多方複審 、立案決定 、專利審查品質 、專利審理暨訴願委員會 、卡方獨立性檢定 、皮爾森相關分析 、羅吉斯迴歸 |
外文關鍵詞: | Inter Partes Review, Institution decision, Patent examination quality, Patent Trial and Appeal Board, Chi-Squared Test, Pearson Correlation, Logistic Regression |
相關次數: | 點閱:284 下載:0 |
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多方複審(IPR)程序自美國AIA修法施行以來,因其所耗時間與金錢相較專利無效訴訟較少,常被侵權訴訟中的被告,用以對抗並反制專利權人,且只要經PTAB通過立案決定,並進入IPR程序之審查階段,即有近8成專利至少一項請求項無效,因此專利在進入IPR程序後立案與否,也能作為該專利之品質的篩選。
因此,本研究收集2013年至2019年底之IPR案件,共10097件IPR案件,並篩選、去重做出立案決定之專利案件後,共3833件案件,並將專利案件依其IPR案件立案狀態,分為每次皆立案之專利與每次皆無立案。透過先前文獻與USPTO內部審核專利品質之指標,選出26個分別為專利申請階段、專利審查階段、專利核准階段與專利獲證後之變數,並透過統計分析方法,進一步判斷案件立案狀態與變數之間的原因,以求出可能影響IPR立案決定之變數。
本研究分別使用平均數t檢定、卡方獨立性分析、皮爾森積差相關分析與羅吉斯迴歸分析等統計方法,篩選掉不適用之變數,再針對案件狀態「從未在IPR被立案」與「至少立案過一次」之間、「每次在IPR皆被立案」與「至少沒有被立案過一次」之間建立模型,分別是為了找出選列的變數中,使專利不容易被立案的因素與容易被立案的因素,並整理與歸納迴歸係數具顯著之變數,並解釋變數的差異對案件狀態所造成之影響。
The inter partes review(IPR) procedure was enacted as part of the America Invents Act. Because IPR procedure takes less time and money than patent invalidation litigation, it is often used by defendants in infringement litigation to counter patent owners. And as long as the case is instituted by PTAB and enters the examination phase of the IPR procedure, there are nearly 80% of the patents, at least one of the claims is invalid. Therefore, after the patent enters the IPR procedure, the institution decision can also be used as a screening of the quality of the patent.
This study collected IPR cases from 2013 to the end of 2019, a total of 10097 IPR cases, and after screening and de-duplicating the patent cases that made the institution decision, a total of 3833 cases. According to the status of the IPR case, the patent cases are divided into patents that are instituted every time and patents that never instituted when filed in IPR procedure.Through the previous literature and the USPTO internal review of patent quality indicators, 26 variables were selected for the patent application stage, patent examination stage, patent approval stage and after the patent was granted. And through statistical analysis methods, to further determine the reason between the IPR case status and the variables, in order to find the variables that may affect the IPR institution decision.
In this study, statistical methods such as the mean t test, chi-square independence analysis, Pearson correlation analysis, and logistic regression analysis were used to establish models based on the IPR case status.Analyze the impact of patent variables of the case status.Sorting out and summarize the significant variables with regression coefficients, and the impact of variables to IPR institution decision.
中文
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英文
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