Essays on Innovative Activity and the Protection of Innovations

This dissertation looks at three widely accepted assumptions about how the patent system works: patent documents disclose inventions; this disclosure happens quickly, and patent owners are able to enforce patents. The first chapter estimates the effect of stronger trade secret protection on the number of patented innovations. When firms find it easier to protect business information, there is less need for patent protection, and accordingly less need for the disclosure of technical information that is required by patent law. The novel finding is that when it is easier to keep innovations, there is not only a reduction in the number of patents but also a sizeable reduction in disclosed knowledge per patent. The chapter then shows how this endogeneity of the amount of knowledge per patent can affect the measurement of innovation using patent data. The second chapter develops a game-theoretic model to study how the introduction of fee-shifting in US patent litigation would influence firms’ patenting propensities. When the defeated party to a lawsuit has to bear not only their own cost but also the legal expenditure of the winning party, manufacturing firms in the model unambiguously reduce patenting, with small firms affected the most. For fee-shifting to have the same effect as in Europe, the US legal system would require shifting of a much smaller share of fees. Lessons from European patent litigation may, therefore, have only limited applicability in the US case. The third chapter contains a theoretical analysis of the influence of delayed disclosure of patent applications by the patent office. Such a delay is a feature of most patent systems around the world but has so far not attracted analytical scrutiny. This delay may give firms various kinds of strategic (non-)disclosure incentives when they are competing for more than a single innovation.

Development of advanced methods for the simulation of the reacting mixture formation in internal combustion engines with the use of machine learning algorithms

Besides increasing the share of electric and hybrid vehicles, in order to comply with more stringent environmental protection limitations, in the mid-term the auto industry must improve the efficiency of the internal combustion engine and the well to wheel efficiency of the employed fuel. To achieve this target, a deeper knowledge of the phenomena that influence the mixture formation and the chemical reactions involving new synthetic fuel components is mandatory, but complex and time intensive to perform purely by experimentation. Therefore, numerical simulations play an important role in this development process, but their use can be effective only if they can be considered accurate enough to capture these variations. The most relevant models necessary for the simulation of the reacting mixture formation and successive chemical reactions have been investigated in the present work, with a critical approach, in order to provide instruments to define the most suitable approaches also in the industrial context, which is limited by time constraints and budget evaluations. To overcome these limitations, new methodologies have been developed to conjugate detailed and simplified modelling techniques for the phenomena involving chemical reactions and mixture formation in non-traditional conditions (e.g. water injection, biofuels etc.). Thanks to the large use of machine learning and deep learning algorithms, several applications have been revised or implemented, with the target of reducing the computing time of some traditional tasks by orders of magnitude. Finally, a complete workflow leveraging these new models has been defined and used for evaluating the effects of different surrogate formulations of the same experimental fuel on a proof-of-concept GDI engine model.

On the role of Computational Logic in Data Science: representing, learning, reasoning, and explaining knowledge

In this thesis we discuss in what ways computational logic (CL) and data science (DS) can jointly contribute to the management of knowledge within the scope of modern and future artificial intelligence (AI), and how technically-sound software technologies can be realised along the path. An agent-oriented mindset permeates the whole discussion, by stressing pivotal role of autonomous agents in exploiting both means to reach higher degrees of intelligence. Accordingly, the goals of this thesis are manifold. First, we elicit the analogies and differences among CL and DS, hence looking for possible synergies and complementarities along 4 major knowledge-related dimensions, namely representation, acquisition (a.k.a. learning), inference (a.k.a. reasoning), and explanation. In this regard, we propose a conceptual framework through which bridges these disciplines can be described and designed. We then survey the current state of the art of AI technologies, w.r.t. their capability to support bridging CL and DS in practice. After detecting lacks and opportunities, we propose the notion of logic ecosystem as the new conceptual, architectural, and technological solution supporting the incremental integration of symbolic and sub-symbolic AI. Finally, we discuss how our notion of logic ecosys- tem can be reified into actual software technology and extended towards many DS-related directions.