research-article

Automatic discovery of technology trends from patent text

Authors:

Sung-Hyon MyaengAuthors Info & Claims

SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing

Pages 1480 - 1487

https://doi.org/10.1145/1529282.1529611

Published: 08 March 2009 Publication History

Abstract

Patent text is a rich source to discover technological progresses, useful to understand the trend and forecast upcoming advances. For the importance in mind, several researchers have attempted textual-data mining from patent documents. However, previous mining methods are limited in terms of readability, domain-expertise, and adaptability. In this paper, we first formulate the task of technological trend discovery and propose a method for discovering such a trend. We complement a probabilistic approach by adopting linguistic clues and propose an unsupervised procedure to discover technological trends. Based on the experiment, our method is promising not only in its accuracy, 77% in R-precision, but also in its functionality and novelty of discovering meaningful technological trends.

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Cited By

Abi Younes Gde Rassenfosse G(2024)Replicable Patent Indicators Using the Google Patents Public DatasetsAustralian Economic Review10.1111/1467-8462.1254557:1(102-113)Online publication date: 10-Mar-2024
https://doi.org/10.1111/1467-8462.12545
Li XWang YHuang LGao NHuang X(2024)A Novel Integrated Approach for Roadmapping Disruptive Technologies From a Technology Convergence PerspectiveIEEE Transactions on Engineering Management10.1109/TEM.2024.339863871(8651-8670)Online publication date: 2024
https://doi.org/10.1109/TEM.2024.3398638
Keresten ITereshchenko VMiller MDavydov A(2023)Development of the technology for diagnostic assessment of scientific, technical and financial risks of technology integration with production systemsE3S Web of Conferences10.1051/e3sconf/202341902017419(02017)Online publication date: 25-Aug-2023
https://doi.org/10.1051/e3sconf/202341902017
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Index Terms

Automatic discovery of technology trends from patent text
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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cover image ACM Conferences

SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing

March 2009

2347 pages

ISBN:9781605581668

DOI:10.1145/1529282

Conference Chairs:
Sung Y. Shin
South Dakota State University, United States
,
Sascha Ossowski
University Rey Juan Carlos, Spain

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 08 March 2009

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SAC09: The 2009 ACM Symposium on Applied Computing

March 8, 2009 - March 12, 2008

Hawaii, Honolulu

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Abi Younes Gde Rassenfosse G(2024)Replicable Patent Indicators Using the Google Patents Public DatasetsAustralian Economic Review10.1111/1467-8462.1254557:1(102-113)Online publication date: 10-Mar-2024
https://doi.org/10.1111/1467-8462.12545
Li XWang YHuang LGao NHuang X(2024)A Novel Integrated Approach for Roadmapping Disruptive Technologies From a Technology Convergence PerspectiveIEEE Transactions on Engineering Management10.1109/TEM.2024.339863871(8651-8670)Online publication date: 2024
https://doi.org/10.1109/TEM.2024.3398638
Keresten ITereshchenko VMiller MDavydov A(2023)Development of the technology for diagnostic assessment of scientific, technical and financial risks of technology integration with production systemsE3S Web of Conferences10.1051/e3sconf/202341902017419(02017)Online publication date: 25-Aug-2023
https://doi.org/10.1051/e3sconf/202341902017
Qiu ZWang Z(2023)Technological origination and evolution analysis by combining patent claims and citations: A case of surgical robot domainAdvanced Engineering Informatics10.1016/j.aei.2023.10214558(102145)Online publication date: Oct-2023
https://doi.org/10.1016/j.aei.2023.102145
Miao HWang YLi XWu F(2022)Integrating Technology-Relationship-Technology Semantic Analysis and Technology Roadmapping Method: A Case of Elderly Smart Wear TechnologyIEEE Transactions on Engineering Management10.1109/TEM.2020.297097269:1(262-278)Online publication date: Mar-2022
https://doi.org/10.1109/TEM.2020.2970972
Huang SYen DYan TYang Y(2021)An Intelligent Mechanism to Automatically Discover Emerging Technology Trends: Exploring Regulatory TechnologyACM Transactions on Management Information Systems10.1145/348518713:2(1-29)Online publication date: 11-Dec-2021
https://dl.acm.org/doi/10.1145/3485187
He LDong BJiang P(2021)A Heuristic Grafting Strategy for Manufacturing Knowledge Graph Extending and Completion Based on Nature Language Processing: KnowTreeIEEE Access10.1109/ACCESS.2021.30920199(90847-90862)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3092019
Nazemi KBurkhardt DKock A(2021)Visual analytics for technology and innovation managementMultimedia Tools and Applications10.1007/s11042-021-10972-381:11(14803-14830)Online publication date: 20-May-2021
https://doi.org/10.1007/s11042-021-10972-3
Nazemi KBurkhardt D(2019)Visual Analytics for Analyzing Technological Trends from Text2019 23rd International Conference Information Visualisation (IV)10.1109/IV.2019.00041(191-200)Online publication date: Jul-2019
https://doi.org/10.1109/IV.2019.00041
Wang XRen HChen YLiu YQiao YHuang Y(2019)Measuring patent similarity with SAO semantic analysisScientometrics10.1007/s11192-019-03191-z121:1(1-23)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s11192-019-03191-z
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