Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT

Feng, Lijie; Niu, Yuxiang; Liu, Zhongfan; Wang, Jinfeng; Zhang, Ke

doi:10.3390/su12010136

Cited by 25 publications

(16 citation statements)

References 54 publications

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“…The use of DMC-based MA generates a large number of dimensions and sub-dimensions of the target technology compared to the traditional MA. However, the construction of the innovation dimensions in this paper is different from the previous literature on MA [45][46][47]59,72]. Since the recombination of multiple innovation dimensions similar to MA are hard to apply to the creative ideas extension, which is achieved through differentiation and heterogeneity.…”

Section: Discussionmentioning

confidence: 90%

Resource-Constrained Innovation Method for Sustainability: Application of Morphological Analysis and TRIZ Inventive Principles

2020

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Extensive research on resource-constrained innovation has been conducted by scholars and practitioners in recent years. An interesting research avenue is how firms explore the process of the new product development (NPD) and the ideas generation to foster resource-constrained innovation. However, despite the importance of product development and creative ideas under the resource-constraints contexts, innovation methods for applying to the resource-constrained innovation and designers have received comparatively less attention. As a remedy, this paper proposes a resource-constrained innovation method (RCIM) to generate ideas for the NPD. The RCIM is mainly divided into four sections: Developing the resource-constrained innovation approaches, developing the resource-constrained innovation dimensions, generating the creative ideas and evaluating the creative ideas. First, the resource-constrained innovation algorithms are developed based on success factors, characteristics, and attributes of resource-constrained innovation and the TRIZ (Teopия Peшeния Изoбpeтaтeльcкиx Зaдaч in Russian; Theory of Inventive Problem Solving in English) inventive principles via the systematic literature review (SLR). Second, the innovation dimensions are categorized to structure a target technology by means of the morphological analysis (MA) and the Derwent manual codes (DMCs) mapping based on collected patents. Third, the creative ideas are generated for the NPD by combining the innovation dimensions with the resource-constrained innovation approaches. Finally, the creative ideas are evaluated by the frugal criteria. The RCIM will stimulate designers’ creativity for achieving sustainability and innovation within constraint-based scenarios, MA and TRIZ.

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Section: Discussionmentioning

confidence: 90%

Resource-Constrained Innovation Method for Sustainability: Application of Morphological Analysis and TRIZ Inventive Principles

2020

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“…e SSE method calculates the total error value between any data point and the cluster centroid, and the calculation method is shown in equation (5). dis represents the distance function, p is any data point in the cluster of c i , and m i is the cluster centroid.…”

Section: Validation Methodsmentioning

confidence: 99%

“…Text mining, as a representative of the intelligent measure method, has been widely applied in various fields. For example, the evaluation of business intelligence and enterprise technology analysis [1][2][3], the enterprise technology opportunity identification [4,5], correlation analysis of enterprise technology cooperation behavior [6][7][8], the analysis of enterprises technology maturity [9][10][11], and prediction of enterprise technology development trend, etc. [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Big Data-Driven Approach to Analyze the Influencing Factors of Enterprise’s Technological Innovation

Zhang

2022

Computational Intelligence and Neuroscience

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A data-driven intelligent analysis method is proposed in this paper to explore and identify the enterprise’s technological innovation influencing factors. Questionnaire surveys or expert interviews are usually adopted by the traditional evaluation methods for indicators of technological innovation selection. However, it inevitably involves human factors and experts’ subjective judgments, which may affect the result of enterprises evaluation. The research presents an improved text clustering method based on a semantic concept model to explore and analyze the key influencing factors of enterprise’s technological innovation. The study collects textual data from 400 enterprises in Beijing and smart analyzes the critical influencing factors of enterprise’s technological innovation by using the proposed method. The influencing factors can be divided into seven categories. In addition, compared with the traditional K-means clustering method, the proposed method has a good effect. We proposed a methodology to conduct an intelligent analysis for enterprise’s technological innovation under the data-driven. It can provide more objective and auxiliary suggestions for the evaluation of the enterprise’s technology innovation.

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“…[22] Feng et al (2020) proposed a hybrid approach based on morphological analysis (MA) and integrated structural creative thinking (USIT) for technology opportunity discovery (TOD) through patent analysis using text mining and Word2Vec clustering analysis to provide essential A method for exploring the link was proposed. [23] Geum et al (2020) proposed a key graph-based approach combined with an index-based validation approach to analyze patents. [24] Evangelista et al (2020) using Word2Vec, we propose a model that composes a product landscape with a vector space model in which products with similar technology base are located close to each other while maintaining technology relationship.…”

Section: -1 Patent Analysismentioning

confidence: 99%

Patent Analysis Using Vector Space Model and Deep Learning Model : A Case of Artificial Intelligence Industry Technology

Yoo¹,

Lim²,

Kim³

2021

Preprint

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Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws to create investment models or predict changes in financial markets. As such, artificial intelligence technology is being used in various fields. So, it is very important to know exactly what factors have an important influence on each field of artificial intelligence technology and how the relationship between each field is connected. Therefore, it is necessary to analyze artificial intelligence technology in each field. In this paper, we analyze patent documents related to artificial intelligence technology. We propose a method for keyword analysis within factors using artificial intelligence patent data sets for artificial intelligence technology analysis. This is a model that relies on feature engineering based on deep learning model named KeyBERT, and using vector space model. A case study of collecting and analyzing artificial intelligence patent data was conducted to show how the proposed model can be applied to real-world problems.

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Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT

Cited by 25 publications

References 54 publications

Resource-Constrained Innovation Method for Sustainability: Application of Morphological Analysis and TRIZ Inventive Principles

Resource-Constrained Innovation Method for Sustainability: Application of Morphological Analysis and TRIZ Inventive Principles

A Big Data-Driven Approach to Analyze the Influencing Factors of Enterprise’s Technological Innovation

Patent Analysis Using Vector Space Model and Deep Learning Model : A Case of Artificial Intelligence Industry Technology

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