Patent Clustering and Outlier Ranking Methodologies for Attributed Patent Citation Networks for Technology Opportunity Discovery

Rodriguez, Andrew; Tosyali, Ali; Kim, Byung–Hoon; Choi, Jaepil; Lee, Jae-Min; Coh, Byoung-Youl; Jeong, Myong K.

doi:10.1109/tem.2016.2580619

Cited by 40 publications

(9 citation statements)

References 46 publications

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“…Rodriguez et al [5] proposed another approach that addresses TOD via patent outlierness, leveraging both patent attributes and citations. They proposed new outlier ranking methods developed specifically for patents in attributed patent citation networks.…”

Section: Bibliometricsmentioning

confidence: 99%

“…To overcome these difficulties, a variety of forecasting methods to identify promising industry, business, or technology areas have been developed thus far. Some of the more useful and widespread forecasting techniques are judgmental methods and bibliometric analysis methods [4][5][6][7]. Judgmental methods fundamentally rely on the opinions of experts or panels with knowledge in the fields that are relevant to the forecast.…”

Section: Introductionmentioning

confidence: 99%

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Development of a New Methodology to Identity Promising Technology Areas Using M&A Information

Choi

Chang

2020

Sustainability

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In this paper, we suggest a new methodology to identify promising technology areas by analyzing merger and acquisition (M&A) information. First, we present decision models for estimating the velocity and acceleration of M&A transactions to identify promising areas based on M&A information. Second, we identify the promising technology areas with longitudinal analyses of M&As over the entire period. Third, cross-sectional analysis is proposed to determine which technology areas are more promising through a relative comparison among technology areas within the IT sector for a specific period. The main significance of our research is that it is a prior data-based analytic method based on M&A transaction information to identify the growth of industry and technology. We hope this study will provide insights for R&D (Research&Development) policymakers and investment firms as a new approach that complements previous methods in exploring promising industry or technology areas.

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Section: Bibliometricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a New Methodology to Identity Promising Technology Areas Using M&A Information

Choi

Chang

2020

Sustainability

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“…Since patent information contains a variety of technology information, important properties of a technology field can be extracted by patent mining. Promising technology fields are identified [7], while the relationship between various technologies using a citation network is derived [8]. Technology trends are analyzed based on the number of registered patents each year [9, 10], while IPC code is used to find the core technology in an environmental ecology field [11].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Technology opportunity discovery by structuring user needs based on natural language processing and machine learning

et al. 2019

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Discovering technology opportunities from the opinion of users can promote successful technological development by satisfying the needs of users. However, although previous approaches using opinion mining only have classified various needs of users into positive or negative categories, they cannot derive the main reasons for their opinion. To solve this problem, this research proposes an approach to exploring technology opportunity by structuring user needs with a concept of opinion trigger of objects and functions of the technology-based products. To discover technology opportunity, first, an opinion trigger is identified from review data using Naïve Base classifier and natural language processing. Second, the opinion triggers and patent keywords that have a similar meaning in context are clustered to discover the needs of the user and need-related technology. Then, the sentimental values of needs are calculated through graph-based semi-supervised learning. Finally, the needs of the user are classified in resolving the problem of vacant technology to discover technology opportunity. Then, an R&D strategy of each opportunity is suggested based on opinion triggers, patent keywords, and their property. Based on the concept of opinion trigger-based methodology, a case study is conducted on automobile—related reviews, extracting the customer needs and presenting important R&D projects such as an extracted need (cargo transportation) and its R&D strategy (resolving contradiction). The proposed approach can analyze the needs of user at a functional level to discover new technology opportunities.

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“…Attempting to predict emerging technologies is a challenging task, because there are limited historical data on which to base such predictions [4]. Several forecasting methods have been proposed to overcome these difficulties, including judgmental approaches [5][6][7][8] and bibliometric analysis methods [9][10][11][12]. Existing methodologies effectively help to identify the most recent technological trends and discover hidden patterns in information on authors, inventors, affiliations, recent research, and patents [5].…”

Section: Introductionmentioning

confidence: 99%

Strategic Investment Decisions for Emerging Technology Fields in the Health Care Sector Based on M&A Analysis

2021

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The existing approaches to identification of emerging technologies create a prominent opportunity for technology convergence and market growth potential. However, existing approaches either suffer from the time lag issue or have yet to explorethe assessment’s uncertainty and ambiguity. Based on a total of 14 years of mergers and acquisitions (M&A) activity data in the Health Care sector, the complex patterns between growth velocity and accelerating of M&A activities are analyzed with two quantitative indicators (Promising Index and Promising Index Sharpe Ratio) to identify emerging technological opportunities. The proposed integrative approach offers a mean to resolve the time lag issue, deal with market trend irregularity, and manage expectations of investors for emerging technology and industry. Specifically, this study aims to (i) provide a decision support system integrating M&A activity information for strategic investment planning and (ii) identify promising technologies in the Healthcare sector to manage the irregularities of market trend and investment outcome. This study is one of the first research that employs a prior data-based approach to delineate emerging technologies by analyzing the growth momentum properties of specific industry areas based on the M&A activity data.

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Patent Clustering and Outlier Ranking Methodologies for Attributed Patent Citation Networks for Technology Opportunity Discovery

Cited by 40 publications

References 46 publications

Development of a New Methodology to Identity Promising Technology Areas Using M&A Information

Development of a New Methodology to Identity Promising Technology Areas Using M&A Information

Technology opportunity discovery by structuring user needs based on natural language processing and machine learning

Strategic Investment Decisions for Emerging Technology Fields in the Health Care Sector Based on M&A Analysis

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