KJCKorea
Journal Central

This paper analyzes how the problem of hindsight in determining inventiveness under patent law is exacerbated by the use of artificial intelligence technology in the invention process, and seeks solutions. In the process of determining inventiveness, examiners are prone to hindsight bias, which occurs when an examiner evaluates an already completed invention and thinks it was easy to invent something that was actually difficult to derive. Recent advances in artificial intelligence technology have exacerbated this problem, as sophisticated AI technology has moved from simply facilitating invention efficiency to enhancing the inventor's own creative ability. As a result, AI-enabled inventions often have sophisticated and organized logical structures, which increases the risk of misleading examiners into thinking that the invention can be naturally derived from prior art. To address these issues, we propose several solutions that, combined with existing discussions, could provide meaningful solutions to hindsight problems in AI-era patent examination.

In today's Over-The-Top (OTT) player agent environment, the process of collecting and analyzing user data is essential for providing personalized content. However, privacy protection and data security have emerged as critical issues in this process. This paper proposes a Zero-Knowledge Proof (ZKP)-based data anonymization protocol to safeguard user privacy in the OTT player agent environment. First, we analyze existing anonymization techniques and the concept of Zero-Knowledge Proofs to explore their applicability in the process of collecting content usage information in the OTT player agent environment. Additionally, we present an approach that utilizes pseudonymization and encryption techniques based on sensitivity classification to maintain data integrity while protecting personal information. Finally, we evaluate the effectiveness of the proposed methodology by examining its applicability in various scenarios. This study contributes to addressing privacy issues in the OTT player agent environment and serves as a valuable foundation for future advancements in data protection technologies.

Now a day, Quantum computing is a technology with the potential to offer superior parallel processing capabilities and significantly higher computational efficiency for certain problems compared to traditional classical computers. However, the development of quantum computer software is still in its early stages, and verifying it to ensure accuracy has emerged as one of the major challenges. This research presents a model for the similarity evaluation of quantum computing software, which we propose as a future study on approaches to improve the accuracy and reliability of quantum algorithms and programs. To achieve this, the behavior analysis of quantum algorithms, the temporal behavior of quantum circuits and software and systems, creates models using Kripke structures and specifies behavior using tempo-lal logic, and Kripke structures model by describing transitions between states in a manner similar to finite-state machines.