Firms’ Negative Perceptions on Patents, Technology Management Strategies, and Subsequent Performance

2023

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Processing-in-Memory (PiM), which combines a memory device with a Processing Unit (PU) into an integrated chip, has drawn special attention in the field of Artificial Intelligence semiconductors. Currently, in the development and commercialization of PiM’s technology, there are challenges in the hegemony competition between the PU and memory device industries. In addition, there are challenges in finding strategic partnerships rather than independent development due to the complexity of technological development caused by heterogeneous chips. In this study, patent Main Path Analysis (MPA) is used to identify the majority and complementary groups between PU and memory devices for PiM. Subsequently, Document-to-Vector (Doc2Vec) and similarity-scoring analyses are used to determine the potential partners for technical cooperation required for PiM technology development for the majority group identified. According to the empirical results, PiM core technology is evolving from PU to memory device with an ‘architecture-operation-architecture’ design pattern. The ten ASIC candidates are identified for strategic partnerships with memory device suppliers. Those partnership candidates include several mobile AP firms, implying PiM’s opportunities in the field of mobile applications. It suggests that memory device suppliers should prepare for different technology strategies for PiM technology development. This study contributes to the literature and high-tech industry via the proposed quantitative technology partnership model.

Section: Discussionmentioning

confidence: 99%

Processing-in-Memory Development Strategy for AI Computing Using Main-Path and Doc2Vec Analyses

Chung

2023

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“…Managing intellectual property is increasingly important in diverse industries [1,87]. Particularly, the innovative BM is necessary for effective business management [88].…”

Section: Discussionmentioning

confidence: 99%

“…Innovation has been considered as the important factor in achieving a sustainable economy [1]. It is necessary to substantially implement such innovation in industries.…”

Section: Introductionmentioning

confidence: 99%

Identifying Emerging Trends of Financial Business Method Patents

Lee

2017

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Financial technology has become an important part of the banking industry in recent times. This study attempts to propose a framework to identify emerging areas and trends using financial business method patents. Based on the abstracts of financial business method patents registered at the United States Patent and Trademark Office, this study first applies latent Dirichlet allocation to identify emerging topics. The probability of the annual occurrence of each topic is adjusted through the exponentially weighted moving average to reflect the importance of the recent probability of topics. Each topic is classified as "hot" or "cold" depending on whether the exponentially weighted moving average of the probabilities exceeds the threshold. We applied survival analysis to the time gap of recurrently becoming hot from a cold status with the associated factor of financial business method patents. The findings suggest that the topic with the short granted period and high forward citation is likely to become hot. In addition, the topic that is aged and specific in narrow areas is likely to continuously change into the hot or cold status. The approach proposed in this study contributes toward understanding topic emergence in the financial area and pursuing sustainable development.

“…While many fields actively use quantification methods, such methods have not been applied to credit scoring activities. Lastly, Han and Sohn [42] investigated firms' negative perception on patents, technology management strategies, and subsequent performance based on the quantification method and canonical correlation analysis.…”

Section: Quantification Methodsmentioning

confidence: 99%

Technology Credit Scoring Based on a Quantification Method

2017

Credit scoring models are usually formulated by fitting the probability of loan default as a function of individual evaluation attributes. Typically, these attributes are measured using a Likert-type scale, but are treated as interval scale explanatory variables to predict loan defaults. Existing models also do not distinguish between types of default, although they vary: default by an insolvent company and default by an insolvent debtor. This practice can bias the results. In this paper, we applied Quantification Method II, a categorical version of canonical correlation analysis, to determine the relationship between two sets of categorical variables: a set of default types and a set of evaluation attributes. We distinguished between two types of loan default patterns based on quantification scores. In the first set of quantification scores, we found knowledge management, new technology development, and venture registration as important predictors of default from non-default status. Based on the second quantification score, we found that the technology and profitability factors influence loan defaults due to an insolvent company. Finally, we proposed a credit-risk rating model based on the quantification score.