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Evaluating and ranking patents using weighted citations

Published: 10 June 2012 Publication History

Abstract

Citation counts have been widely used in a digital library for purposes such as ranking scientific publications and evaluating patents. This paper demonstrates that distinguishing different types of citations could rank better for these purposes. We differentiate patent citations along two dimensions (assignees and technologies) into four types, and propose a weighted citation approach for assessing and ranking patents. We investigate five weight learning methods and compare their performance. Our weighted citation method performs consistently better than simple citation counts, in terms of rank correlations with patent renewal status. The estimated weights on different citations are consistent with economic insights on patent citations. Our study points to an interesting and promising research line on patent citation and network analysis that has not been explored.

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Cited By

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  • (2023)Patent Citation Network Simplification and Similarity Evaluation Based on Technological InheritanceIEEE Transactions on Engineering Management10.1109/TEM.2021.310913470:12(4144-4161)Online publication date: Dec-2023
  • (2020)Identification, Tracking and Impact: Understanding the Trade Secret of CatchphrasesProceedings of the ACM/IEEE Joint Conference on Digital Libraries in 202010.1145/3383583.3398512(67-76)Online publication date: 1-Aug-2020
  • (2019)Identifying Reference Relationship of Desktop Files Based on Access LogsDatabase Systems for Advanced Applications10.1007/978-3-030-18590-9_8(113-127)Online publication date: 24-Apr-2019
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    cover image ACM Conferences
    JCDL '12: Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
    June 2012
    458 pages
    ISBN:9781450311540
    DOI:10.1145/2232817
    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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    Publication History

    Published: 10 June 2012

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    1. patent citation
    2. patent ranking
    3. patent renewal
    4. weighted citation

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    Cited By

    View all
    • (2023)Patent Citation Network Simplification and Similarity Evaluation Based on Technological InheritanceIEEE Transactions on Engineering Management10.1109/TEM.2021.310913470:12(4144-4161)Online publication date: Dec-2023
    • (2020)Identification, Tracking and Impact: Understanding the Trade Secret of CatchphrasesProceedings of the ACM/IEEE Joint Conference on Digital Libraries in 202010.1145/3383583.3398512(67-76)Online publication date: 1-Aug-2020
    • (2019)Identifying Reference Relationship of Desktop Files Based on Access LogsDatabase Systems for Advanced Applications10.1007/978-3-030-18590-9_8(113-127)Online publication date: 24-Apr-2019
    • (2017)A Study on Generating Novel Inventions Based on F-Term Classification2017 Portland International Conference on Management of Engineering and Technology (PICMET)10.23919/PICMET.2017.8125331(1-6)Online publication date: Jul-2017
    • (2015)Patent MiningACM SIGKDD Explorations Newsletter10.1145/2783702.278370416:2(1-19)Online publication date: 21-May-2015
    • (2014)Patent Evaluation Based on Technological Trajectory Revealed in Relevant Prior PatentsAdvances in Knowledge Discovery and Data Mining10.1007/978-3-319-06608-0_45(545-556)Online publication date: 2014

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