專利制度是知識經濟時代最為重要的一種智慧財產權形式，不但對於技術創新居功厥偉，在國際商業活動中也占有極具份量的地位。而在取得專利的三大要件中，以非顯而易知性（即我國進步性）最為棘手，蓋其本身屬於不確定之法律概念，而容有裁量空間。 非顯而易知性發軔於美國判例法，其後由實務主導其發展。在指標性案例KSR判決中，最高法院揭示了非顯而易知性的審查架構，以Graham四要件法則為根柢，並輔以顯可嘗試原則及彈性運用的TSM檢測法，整體而言KSR判決提高了非顯而易知性的適格門檻。在後KSR時代，CAFC在機械工業、醫藥品與生物科技等領域分別依不同程度適用KSR見解。2009年In re Kubin案確認KSR見解可適用於不可預測性較高之基因生技領域，近幾年來顯可嘗試原則也獲得高度重視。 相較而言，我國進步性審查主要依據智慧財產局所制定的專利審查基準，但行政審查常有過於直觀簡略之嫌；法院判決則在「發明所屬領域中具通常技術者之技術水準」與「該領域具通常技術者參酌先前技術所揭露之內容及申請時的通常知識，是否能所能輕易完成系爭申請發明之整體」此兩步驟的論證上較為欠缺，整體而言達成進步性結論之心證揭露程度不足，對於當事人有突襲性裁判之虞。 本研究基於上述觀察所得，對美國與我國關於非顯而易知性概念之認知與實踐進行比對，並分別就審查實務面與產業因應面提出微薄建議，以期借鏡美國法經驗使我國未來實務操作更趨完善。 / Patent system is one of the most important forms of intellectual property rights in the era of knowledge economy, not only indispensable for technological innovation, also of great influnce in the international business activities. Among the three requirements of patentability, “Non-obviousness” (ie, “Inventive Step” in Taiwan) is the most difficult to fulfill, due to the uncertainty of its legal concept and the room for discretion. Non-obviousness was carved out in the U.S. case law and continuously developed by the court rulings. In the benchmark case KSR v. Teleflex, the Supreme Court articulated that the examination framework of non-obviousness is based on Graham four factors, along with other principles like “Obvious to Try” and the TSM test in a more flexible way. In general, KSR lifted the eligibility threshold for non-obviousness. It has been applied in different degrees by the Court of Appeals for the Federal Circuit to various fields such as machinery industry, pharmaceuticals and biotechnology in the post-KSR era. Then it was recognized in 2009 In re Kubin case that the KSR opinion is applicable to the unpredictable field, gene biotechnology, for instance. Besides, the “Obvious to Try” principle has been gaining much attention in recent years. In comparison, both administrative and juducial examinations of inventive step in Taiwan are mainly based on the “Substantive examination guidelines for invention patent” issued by the Intellectual Property Office. However, the administrative review is often reckoned to be too intuitive and rough, and the court decisions are considered to be made with less expression on “the level of the PHOSITA” and “whether a PHOSITA with the reference to prior arts and common knowledge can complete the whole invention without difficulty.” In all, the lack of revealing the reasoning on the inventive step conclusion might expose the parties in danger of surprise judgements. Based on the above observations, this study compared the cognition and practice of non-obviousness both in the United States and in Taiwan, and as a result, presented some primary suggestions in light of the United States’ experience toward both the practice and industries, so that our inventive step examination practice in the future could be improved.

Approximately 20% of students with emotional and behavioral disorders (E/BD) are sent to an alternative campus for their lack of social competence. Social skills training (SST) is an evidence-based intervention to help increase social competence for students with E/BD, but there is limited research that addresses SST for students with E/BD at alternative campuses. A mixed-methods design was utilized to examine SST at an alternative campus for students with E/BD. Pre-intervention data were collected for students' attendance, grades, office disciplinary referrals, and behavioral rating scales, after which, the Why Try SST program was implemented. Following the intervention, the same type of data were collected. Nonparametric statistics guided the quantitative analysis, because of the small population being studied. Differences from pre- to post-intervention were examined. Triangulation methods drove the qualitative data collection and analysis through observations, student interviews, and teacher interviews. Students exhibited significant differences from pre- to post-intervention in the number of office disciplinary referrals and several areas on the behavioral rating scales. Important insight into motivation and perceptions was gained through the observations and interviews.

The diploma thesis devotes to the process of ensuring functional safety of products in Bosch Rexroth Brno, which deals with industrial hydraulic systems. The first part introduces the basic legislative regulations concerning product safety and describes selected standards relevant to this thesis. The second part focuses on Bosch Rexroth and the current state in the field of ensuring functional safety of their products. It also includes a description of the functional safety solution of a specific project – a hydraulic try-out press. At the end of the thesis, improvements to the process of ensuring functional safety in response to the identified shortcomings are proposed.

Denna studie skrivs i ett intresse av ett företag som arbetar med 3D-simulering vid prototypframtagning av provplagg. Syfte med studien är att undersöka korrespondensen mellan virtuell- och fysisk avprovning gällande storlek, passform och val av tyg. I studien undersöks 3D-simuleringsprogrammet CLO 3D och digitalisering av tygprov med applikationen CLO Fabric kit, som komplement i syfte att utvärdera skillnaden mellan virtuell- och fysisk avprovning. Två plagg, bestående av t-tröja i bomullstrikå och skjorta i linneväv, provas av i storlekar: XS, M och XL på tre provpersoner och tre avatarer med motsvarande mått. Ett protokollmed foton från båda avprovningarna bedöms med frågor gällande plaggets visuella skillnad och passform. Resultatet visade att T-tröjan i bomullstrikå gav likvärdigt passform mellan virtuell- och fysisk avprovning i samtliga storlekar. Vid avprovning av skjortan i linneväv var passformsskillnaden mellan virtuell- och fysisk avprovning på storlek XL tydlig. Övriga storlekar hade däremot ett likartat resultat.3D-simulering är en effektiv avprovningsmetod, då man kan modifiera avaterns mått, digitalisera in tygprover och prova av flera storlekar vid virtuell avprovning. Däremot behöver man vara medveten att flera faktorer kan påverka passformen vid virtuell avprovning som till exempel avatarens kroppshållning och tygets mekaniska egenskaper vid digitalisering. / This study writes in an interest of a company that works with 3D-simulation in prototyping sample garments. The purpose of the study is to investigate the correspondence between the virtual and physical try-on regarding size, fit and fabric choice. The study examines the 3D-simulation program CLO 3D and digitization of fabric samples with the CLO Fabric kit application as a complement to evaluate the difference between virtual and physical try-on. Two garment, consisting of t-shirt made of knitted cotton and a shirt made of linen, are fitted in size XS, M and XL on three test persons and three avatars with corresponding measurements. A protocol containing photos from both virtual and physical try-on where evaluated with questions regarding the balance and fit of the garment. It showed that the T-shirt made of knitted cotton gave equivalent results between virtual and physical try-on in all sizes. When comparing the shirt in linen, the fit differed in size XL during virtual and physical try-on. However, Size XS and M had a similar result during virtual and physical try-on. Using 3D-simulation is an advantage when you can modify the avatar's dimensions, digitize fabric samples and try multiple sizes during virtual try-on. However, one needs to be aware that several factors can affect the fit during virtual try-on, such as the avatar posture and the mechanical properties of the fabric during digitization.

In-flight entertainment (IFE) is facing exciting times ahead as more and more technological advancements find its way into the IFE-systems. With the increasing hardware capabilities of IFE-systems, one exciting technology which could be seen infuture IFE-systems is augmented reality (AR). AR could allow for virtual try-on (VTO) in a webshop, where passengers can view virtual representations of for instance, glasses or headpieces, augmented onto themselves through a camera on the IFE-screen. However, integrating VTO in a webshop comes with new challenges, both for the designer and the developer. This thesis has examined design guidelines for AR in a literature study, along with general principles for user experience (UX). Based on the findings from the literature study, a prototype for a webshop with integrated VTO, targeting IFE, has been build in an iterative design process. Furthermore, a small application has been built using Google’s AR-framework ARCore for Android, where users can try different virtual glasses. The literature study, along with usability testing in each design iteration, showed that there are no guidelines which can cover everything when designing for AR; however, the design guidelines presented by Google and Apple are a great place to start. Furthermore, the usability testing showed that people have certain expectations of a webshop, which indicates that VTO should be integrated seamlessly into the application without interfering with the layout. The application development showed that ARCore certainly is a potent tool when it comes to AR-development. However, ARCore is not open source,which could make it less appealing for companies in need of highly flexible solutions.

化學發明是專利申請領域別中另一個重要的領域，醫學藥品的龐大商機是重要的推手，因為大多數藥物的活性成份為有機小分子化合物，且研發一新藥，須費時10到15年，研發經費估計高達26億美金，各大專利藥廠當然積極申請專利，保護投資。另一方面，Hatch-Waxman Act鼓勵學名藥可以盡早上市，使一般民眾能以較便宜的價格取得所需藥品。在美國藥品市場數千億美元的商機吸引下，學名藥廠積極挑戰專利藥廠之專利權。其中非顯而易知性要件認定，為雙方訴訟爭點。 然，化學發明的非顯而易知性要件認定之所以異於機械、電子、電機等技術，在於我們無法準確預測在奈米尺度的化學反應與分子的物性與化性，因而產生化學反應結果的不可預期性。而藥物研發是先從資料庫中，篩選出具一定活性的化合物作為先導化合物，經由取代基的引入、改變，或官能基的置換，結合活性與化合物結構關係（SAR），達成先導化合物結構最佳化，以快速、準確的找出候選藥物分子，進入臨床實驗。 2007年，美國聯邦最高法院在KSR案，針對非顯而易知性，重申Graham 案建立的非顯而易知性判斷法則的重要性，與重新適用顯而易知的嘗試。KSR判決後，許多文章討論KSR判決將不只針對機械組合發明，同時也將會對醫藥化學發明非顯而易知性之認定標準，產生一定的影響。 本論文研究KSR判決後， CAFC使用顯而易知的嘗試、與先導化合物分析（lead compound analysis）判斷準則，於醫藥產業化學發明專利的非顯而易知性的判決。CAFC於涉及組合藥物或配方調配案件，使用顯而易知的嘗試審查基準；使用先導化合物分析，都涉及系爭藥物中「活性成份結構」的非顯而易知性認定，CAFC針對不同類型的化學發明案件，採用了不同的審查基準。 KSR判決雖然重新啟用「顯而易知的嘗試」判斷準則，且CAFC適用「顯而易知的嘗試」的案件，亦明顯的增加。唯，本論文研究發現，於醫藥產業化學發明專利的非顯而易知性的判決，仍明顯高於對非藥品相關案件。 / Chemical invention is one of key art in patent application driving from the huge market size of medicines, in which active ingredients are organic molecules. The average cost to research and develop each successful drug is estimated to be $2.6 billion US dollars, and took 10 to 15 years. In other word, whether pharmaceutical companies can recover their investment in drug development heavily depends on the patent protection of their drugs. On the other hand, the Hatch-Waxman Act introduced in 1984 created the generic drug pathway to the market, so general public can obtain the drugs at a affordable price. However, within this framework, the validity of drug patents are often challenged by generic manufactures, mainly the "non-obviousness" requirement in patent system. During this lengthy and expensive drug discovery, chemist often entails making small modifications to lead compounds to establish structure-activity relationship (SAR) to speed up the process. Those modifications might be deemed “obvious to try”—and then studying the largely unpredictable, yet critical, resulting biological effects. In 2007, the Supreme Court of the United States, in KSR decision, reasserted that a prima facie case of obviousness may be determined by the framework set forth in Graham and "obvious to try" test. Since then, there are predictions that KSR decision will have a substantial impact in pharmaceutical and life sciences arts. This study, we examine the CAFC ruling in pharmaceutical arts regarding to "non-obviousness" issue by "obvious to try" and "lead compound analysis" test after the KSR decision. And found that the "non-obviousness" judgment of the chemical invention patent in the pharmaceutical industry was still significantly higher than that of the non-drug-related cases.

No description available.

A patent is often characterized as a bargain between an inventor and society. Generally, for a patent to be considered valid, an invention must satisfy three broad criteria: it must be new, useful, and non-obvious. This paper focuses on the requirement of non-obviousness. It explores the criterion from inception to its current state and suggests a potential refinement. A multi-jurisdictional snap-shot is presented focusing on the United Kingdom, Australia, the United States, and Canada. It is submitted that the non-obvious prerequisite contains a problematic level of uncertainty. The law of obviousness lacks a baseline standard. To achieve certainty, it is suggested that the law adopt the principles pertaining to patenting combinations and aggregates. Ultimately the law of patents, inclusive of the doctrine of obviousness, must be fashioned and administered in a manner that respects the quid pro quo that has guided the law for many years.

A patent is often characterized as a bargain between an inventor and society. Generally, for a patent to be considered valid, an invention must satisfy three broad criteria: it must be new, useful, and non-obvious. This paper focuses on the requirement of non-obviousness. It explores the criterion from inception to its current state and suggests a potential refinement. A multi-jurisdictional snap-shot is presented focusing on the United Kingdom, Australia, the United States, and Canada. It is submitted that the non-obvious prerequisite contains a problematic level of uncertainty. The law of obviousness lacks a baseline standard. To achieve certainty, it is suggested that the law adopt the principles pertaining to patenting combinations and aggregates. Ultimately the law of patents, inclusive of the doctrine of obviousness, must be fashioned and administered in a manner that respects the quid pro quo that has guided the law for many years.

Les méthodes de Monte Carlo par chaînes de Markov (MCCM) sont des méthodes servant à échantillonner à partir de distributions de probabilité. Ces techniques se basent sur le parcours de chaînes de Markov ayant pour lois stationnaires les distributions à échantillonner. Étant donné leur facilité d’application, elles constituent une des approches les plus utilisées dans la communauté statistique, et tout particulièrement en analyse bayésienne. Ce sont des outils très populaires pour l’échantillonnage de lois de probabilité complexes et/ou en grandes dimensions. Depuis l’apparition de la première méthode MCCM en 1953 (la méthode de Metropolis, voir [10]), l’intérêt pour ces méthodes, ainsi que l’éventail d’algorithmes disponibles ne cessent de s’accroître d’une année à l’autre. Bien que l’algorithme Metropolis-Hastings (voir [8]) puisse être considéré comme l’un des algorithmes de Monte Carlo par chaînes de Markov les plus généraux, il est aussi l’un des plus simples à comprendre et à expliquer, ce qui en fait un algorithme idéal pour débuter. Il a été sujet de développement par plusieurs chercheurs. L’algorithme Metropolis à essais multiples (MTM), introduit dans la littérature statistique par [9], est considéré comme un développement intéressant dans ce domaine, mais malheureusement son implémentation est très coûteuse (en termes de temps). Récemment, un nouvel algorithme a été développé par [1]. Il s’agit de l’algorithme Metropolis à essais multiples revisité (MTM revisité), qui définit la méthode MTM standard mentionnée précédemment dans le cadre de l’algorithme Metropolis-Hastings sur un espace étendu. L’objectif de ce travail est, en premier lieu, de présenter les méthodes MCCM, et par la suite d’étudier et d’analyser les algorithmes Metropolis-Hastings ainsi que le MTM standard afin de permettre aux lecteurs une meilleure compréhension de l’implémentation de ces méthodes. Un deuxième objectif est d’étudier les perspectives ainsi que les inconvénients de l’algorithme MTM revisité afin de voir s’il répond aux attentes de la communauté statistique. Enfin, nous tentons de combattre le problème de sédentarité de l’algorithme MTM revisité, ce qui donne lieu à un tout nouvel algorithme. Ce nouvel algorithme performe bien lorsque le nombre de candidats générés à chaque itérations est petit, mais sa performance se dégrade à mesure que ce nombre de candidats croît. / Markov Chain Monte Carlo (MCMC) algorithms are methods that are used for sampling from probability distributions. These tools are based on the path of a Markov chain whose stationary distribution is the distribution to be sampled. Given their relative ease of application, they are one of the most popular approaches in the statistical community, especially in Bayesian analysis. These methods are very popular for sampling from complex and/or high dimensional probability distributions. Since the appearance of the first MCMC method in 1953 (the Metropolis algorithm, see [10]), the interest for these methods, as well as the range of algorithms available, continue to increase from one year to another. Although the Metropolis-Hastings algorithm (see [8]) can be considered as one of the most general Markov chain Monte Carlo algorithms, it is also one of the easiest to understand and explain, making it an ideal algorithm for beginners. As such, it has been studied by several researchers. The multiple-try Metropolis (MTM) algorithm , proposed by [9], is considered as one interesting development in this field, but unfortunately its implementation is quite expensive (in terms of time). Recently, a new algorithm was developed by [1]. This method is named the revisited multiple-try Metropolis algorithm (MTM revisited), which is obtained by expressing the MTM method as a Metropolis-Hastings algorithm on an extended space. The objective of this work is to first present MCMC methods, and subsequently study and analyze the Metropolis-Hastings and standard MTM algorithms to allow readers a better perspective on the implementation of these methods. A second objective is to explore the opportunities and disadvantages of the revisited MTM algorithm to see if it meets the expectations of the statistical community. We finally attempt to fight the sedentarity of the revisited MTM algorithm, which leads to a new algorithm. The latter performs efficiently when the number of generated candidates in a given iteration is small, but the performance of this new algorithm then deteriorates as the number of candidates in a given iteration increases.