Towards a Predictive Patent Analytics and Evaluation Platform

Nebula Alam; Khoi Nguyen Tran; Sue Ann Chen; John Wagner; Josh Andres; Mukesh Mohania

doi:10.1007/978-3-030-46133-1_49

Towards a Predictive Patent Analytics and Evaluation Platform

Nebula Alam^*, Khoi Nguyen Tran, Sue Ann Chen, John Wagner, Josh Andres, Mukesh Mohania

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The importance of patents is well recognised across many regions of the world. Many patent mining systems have been proposed, but with limited predictive capabilities. In this demo, we showcase how predictive algorithms leveraging the state-of-the-art machine learning and deep learning techniques can be used to improve understanding of patents for inventors, patent evaluators, and business analysts alike. Our demo video is available at http://ibm.biz/ecml2019-demo-patent-analytics.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings
Editors	Ulf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, Céline Robardet
Publisher	Springer
Pages	773-776
Number of pages	4
ISBN (Print)	9783030461324
DOIs	https://doi.org/10.1007/978-3-030-46133-1_49
Publication status	Published - 2020
Externally published	Yes
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 - Wurzburg, Germany Duration: 16 Sept 2019 → 20 Sept 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11908 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019
Country/Territory	Germany
City	Wurzburg
Period	16/09/19 → 20/09/19

Access to Document

10.1007/978-3-030-46133-1_49

Cite this

Alam, N., Tran, K. N., Chen, S. A., Wagner, J., Andres, J., & Mohania, M. (2020). Towards a Predictive Patent Analytics and Evaluation Platform. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings (pp. 773-776). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11908 LNAI). Springer. https://doi.org/10.1007/978-3-030-46133-1_49

Alam, Nebula ; Tran, Khoi Nguyen ; Chen, Sue Ann et al. / Towards a Predictive Patent Analytics and Evaluation Platform. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings. editor / Ulf Brefeld ; Elisa Fromont ; Andreas Hotho ; Arno Knobbe ; Marloes Maathuis ; Céline Robardet. Springer, 2020. pp. 773-776 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Towards a Predictive Patent Analytics and Evaluation Platform",

abstract = "The importance of patents is well recognised across many regions of the world. Many patent mining systems have been proposed, but with limited predictive capabilities. In this demo, we showcase how predictive algorithms leveraging the state-of-the-art machine learning and deep learning techniques can be used to improve understanding of patents for inventors, patent evaluators, and business analysts alike. Our demo video is available at http://ibm.biz/ecml2019-demo-patent-analytics.",

keywords = "Data mining, Machine learning, Patent information retrieval, Patent mining, Patents, USPTO",

author = "Nebula Alam and Tran, {Khoi Nguyen} and Chen, {Sue Ann} and John Wagner and Josh Andres and Mukesh Mohania",

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year = "2020",

doi = "10.1007/978-3-030-46133-1_49",

language = "English",

isbn = "9783030461324",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Ulf Brefeld and Elisa Fromont and Andreas Hotho and Arno Knobbe and Marloes Maathuis and C{\'e}line Robardet",

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Alam, N, Tran, KN, Chen, SA, Wagner, J, Andres, J & Mohania, M 2020, Towards a Predictive Patent Analytics and Evaluation Platform. in U Brefeld, E Fromont, A Hotho, A Knobbe, M Maathuis & C Robardet (eds), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11908 LNAI, Springer, pp. 773-776, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019, Wurzburg, Germany, 16/09/19. https://doi.org/10.1007/978-3-030-46133-1_49

Towards a Predictive Patent Analytics and Evaluation Platform. / Alam, Nebula; Tran, Khoi Nguyen; Chen, Sue Ann et al.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings. ed. / Ulf Brefeld; Elisa Fromont; Andreas Hotho; Arno Knobbe; Marloes Maathuis; Céline Robardet. Springer, 2020. p. 773-776 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11908 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Towards a Predictive Patent Analytics and Evaluation Platform

AU - Alam, Nebula

AU - Tran, Khoi Nguyen

AU - Chen, Sue Ann

AU - Wagner, John

AU - Andres, Josh

AU - Mohania, Mukesh

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - The importance of patents is well recognised across many regions of the world. Many patent mining systems have been proposed, but with limited predictive capabilities. In this demo, we showcase how predictive algorithms leveraging the state-of-the-art machine learning and deep learning techniques can be used to improve understanding of patents for inventors, patent evaluators, and business analysts alike. Our demo video is available at http://ibm.biz/ecml2019-demo-patent-analytics.

AB - The importance of patents is well recognised across many regions of the world. Many patent mining systems have been proposed, but with limited predictive capabilities. In this demo, we showcase how predictive algorithms leveraging the state-of-the-art machine learning and deep learning techniques can be used to improve understanding of patents for inventors, patent evaluators, and business analysts alike. Our demo video is available at http://ibm.biz/ecml2019-demo-patent-analytics.

KW - Data mining

KW - Machine learning

KW - Patent information retrieval

KW - Patent mining

KW - Patents

KW - USPTO

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DO - 10.1007/978-3-030-46133-1_49

M3 - Conference contribution

SN - 9783030461324

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 773

EP - 776

BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings

A2 - Brefeld, Ulf

A2 - Fromont, Elisa

A2 - Hotho, Andreas

A2 - Knobbe, Arno

A2 - Maathuis, Marloes

A2 - Robardet, Céline

PB - Springer

T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019

Y2 - 16 September 2019 through 20 September 2019

ER -

Alam N, Tran KN, Chen SA, Wagner J, Andres J, Mohania M. Towards a Predictive Patent Analytics and Evaluation Platform. In Brefeld U, Fromont E, Hotho A, Knobbe A, Maathuis M, Robardet C, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings. Springer. 2020. p. 773-776. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-46133-1_49

Towards a Predictive Patent Analytics and Evaluation Platform

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