i 4 an i: Why Changing the Standard for Overcoming the Presumption of Patent Validity Will Cause More Harm than Good

Bad facts make bad law. The Supreme Court recently addressed the issue of what constitutes the appropriate standard of proof for invalidating an issued patent. The Patent Act provides a presumption of patent validity. Therefore, a party challenging a patent’s validity bears the burden of overcoming this presumption. However, the Patent Act is silent as to the standard of proof required to satisfy this burden. Despite the Act’s silence, the U.S. Court of Appeals for the Federal Circuit (“Federal Circuit”) has consistently held that the Patent Act’s presumption of validity can only be overcome by a showing of clear and convincing evidence. Major players in the patent field, including Google, filed an amicus brief to the Supreme Court, arguing for the standard to be lowered to preponderance of the evidence. Despite the unusual facts of the case, the Supreme Court correctly chose not to lower the required standard of proof. While lowering the standard of proof has appeal in certain instances, it would damage the patent system and stifle innovation because the enforceability of a patent would remain questionable until litigation or beyond

i 4 an i: Why Changing the Standard for Overcoming the Presumption of Patent Validity Will Cause More Harm Than Good

First, this paper describes the interests behind the presumption of patent validity and the historical treatment of the burden of proof required to overcome that presumption. While precedent does not bind the Supreme Court, it is important to consider how and why a particular standard has been applied in addition to Congress’s inaction in implementing a new standard. Second, this paper examines arguments in support of maintaining the status quo, changing to a preponderance of the evidence standard, and adopting a dual standard where some evidence must rise to the level of clear and convincing evidence while other evidence need only show invalidity by a preponderance of the evidence. Finding this dual standard to be impractical, and the broad application of the preponderance of the evidence standard to be inappropriate, this paper supports the continued broad application of the clear and convincing standard along with congressional action to address the unfairness that accompanies broad application of that standard

Missing Data in the Relational Model

This research provides improved support for missing data in the relational model and relational database systems. There is a need for a systematic method to represent and interpret missing data values in the relational model. A system that processes missing data needs to enable making reasonable decisions when some data values are unknown. The user must be able to understand query results with respect to these decisions. While a number of approaches have been suggested, none have been completely implemented in a relational database system. This research describes a missing data model that works within the relational model, is implemented in MySQL, and was validated by a user feasibility study

Cooperative Driving Automation for Self-Interested Agents

Driving is inherently interactive, and drivers must coordinate with other vehicles, cyclists, and pedestrians to avoid collisions. Furthermore, drivers typically prefer some interaction outcomes over others. People in a rush typically cut others off and drive aggressively, while those on a leisurely outing tend to move slowly and behave more altruistically. Even as passengers in autonomous vehicles, people may exhibit these preferences. Unfortunately, autonomous vehicles do not share these preferences, instead sharing only minimal information or operating in isolation. Existing coordination and management methods do not consider these preferences when making decisions. They focus specifically on minimizing the trip duration and maximizing throughput. Cooperative driving automation is an emerging research field in which vehicles work together to achieve individual goals. Standards organizations such as SAE International have developed a taxonomy for various collaboration categories among connected autonomous vehicles (CAVs) with varying automation capabilities. The Federal Highway Administration is developing research platforms to facilitate cooperative driving algorithm development. This research seeks to understand how sharing passenger preferences and other high-level information affects vehicles\u27 coordination abilities and management performance. Specifically, we attempt to answer three main research questions. 1) Can CAVs achieve better interaction outcomes by sharing high-level information, such as their passengers\u27 preferences? 2) Can CAVs solve challenging cooperative decision-making problems by sharing high-level information, such as planned destinations? 3) How does optimizing for passenger preferences in interactive scenarios affect other traditional performance metrics? We explore two application areas under the cooperative driving automation theme. The first scenario considers streams of autonomous vehicles simultaneously approaching an unmanaged intersection. Vehicle passengers have different preferences on how quickly they cross and in which order. Vehicles convey this information to an auction-based intersection management system installed at the intersection. The management system then assigns crossing durations and a crossing schedule to satisfy everyone\u27s preferences as best as possible. The second scenario investigates vehicles moving in a spatially-constrained environment such as an alleyway. In this work, we formulate a new type of finite multi-stage game we call a deadlock game. Additionally, we propose a solution method that solves general problem instances. This work provides the foundation for continued research into equilibria refinement and satisfying passengers\u27 preferences on the outcomes