Using non-supervised machine learning to generate a knowledge ontology for patent analytics

Amy J.C. Trappey; Charles V. Trappey; C. H. Zou

Using non-supervised machine learning to generate a knowledge ontology for patent analytics

Amy J.C. Trappey^*, Charles V. Trappey, C. H. Zou

^*Corresponding author for this work

Department of Management Science

Research output: Contribution to journal › Conference article › peer-review

Abstract

The growth of patenting activities has increased as a result of enterprises seeking greater intellectual property protection and commercialization using global patent systems and legal conventions for sales or licensing. This research develops a novel system to intelligently generate the knowledge ontology for patent analytics. The intelligent system automatically tracks patent topics and searches related patents across multiple patent databases. The first step is to cluster the domain patents into key groups without supervision. Latent Dirichlet Allocation (LDA), an unsupervised machine learning approach, is modified to identify key topics and to match patents with similar topic categories. The system extracts key terms and constructs the ontology. The technical and functional terms are depicted using links across ontology nodes that enable enterprises to target research and development investments that best support their patent portfolios and market investment strategies.

Original language	English
Journal	Proceedings of International Conference on Computers and Industrial Engineering, CIE
Volume	2018-December
State	Published - 1 Jan 2018
Event	48th International Conference on Computers and Industrial Engineering, CIE 2018 - Auckland, New Zealand Duration: 2 Dec 2018 → 5 Dec 2018

Keywords

Latent dirichlet allocation
Ontology
Patent clustering
Technology mining

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title = "Using non-supervised machine learning to generate a knowledge ontology for patent analytics",

abstract = "The growth of patenting activities has increased as a result of enterprises seeking greater intellectual property protection and commercialization using global patent systems and legal conventions for sales or licensing. This research develops a novel system to intelligently generate the knowledge ontology for patent analytics. The intelligent system automatically tracks patent topics and searches related patents across multiple patent databases. The first step is to cluster the domain patents into key groups without supervision. Latent Dirichlet Allocation (LDA), an unsupervised machine learning approach, is modified to identify key topics and to match patents with similar topic categories. The system extracts key terms and constructs the ontology. The technical and functional terms are depicted using links across ontology nodes that enable enterprises to target research and development investments that best support their patent portfolios and market investment strategies.",

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AU - Trappey, Amy J.C.

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AU - Zou, C. H.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - The growth of patenting activities has increased as a result of enterprises seeking greater intellectual property protection and commercialization using global patent systems and legal conventions for sales or licensing. This research develops a novel system to intelligently generate the knowledge ontology for patent analytics. The intelligent system automatically tracks patent topics and searches related patents across multiple patent databases. The first step is to cluster the domain patents into key groups without supervision. Latent Dirichlet Allocation (LDA), an unsupervised machine learning approach, is modified to identify key topics and to match patents with similar topic categories. The system extracts key terms and constructs the ontology. The technical and functional terms are depicted using links across ontology nodes that enable enterprises to target research and development investments that best support their patent portfolios and market investment strategies.

AB - The growth of patenting activities has increased as a result of enterprises seeking greater intellectual property protection and commercialization using global patent systems and legal conventions for sales or licensing. This research develops a novel system to intelligently generate the knowledge ontology for patent analytics. The intelligent system automatically tracks patent topics and searches related patents across multiple patent databases. The first step is to cluster the domain patents into key groups without supervision. Latent Dirichlet Allocation (LDA), an unsupervised machine learning approach, is modified to identify key topics and to match patents with similar topic categories. The system extracts key terms and constructs the ontology. The technical and functional terms are depicted using links across ontology nodes that enable enterprises to target research and development investments that best support their patent portfolios and market investment strategies.

KW - Latent dirichlet allocation

KW - Ontology

KW - Patent clustering

KW - Technology mining

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M3 - Conference article

AN - SCOPUS:85061341387

VL - 2018-December

JO - Proceedings of International Conference on Computers and Industrial Engineering, CIE

JF - Proceedings of International Conference on Computers and Industrial Engineering, CIE

SN - 2164-8689

Y2 - 2 December 2018 through 5 December 2018

ER -

Using non-supervised machine learning to generate a knowledge ontology for patent analytics

Abstract

Keywords

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