Use of the industrial property system in Colombia (2018): A supervised learning application

The purpose of this paper is to establish ways to predict the spatial distribution of the use of the intellectual property system from information on industrial property applications and grants (distinctive signs and new creations) and copyright registrations in 2018. This will be done using supervi...

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Autores:
Lis-Gutiérrez, Jenny-Paola
Lis Gutiérrez, Melissa
GALLEGO-TORRES, ADRIANA PATRICIA
Ballesteros Ballesteros, Vladimir
Romero-Ospina, Manuel Francisco
Tipo de recurso:
http://purl.org/coar/resource_type/c_816b
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8043
Acceso en línea:
https://hdl.handle.net/11323/8043
https://doi.org/10.1007/978-3-030-53956-6_46
https://repositorio.cuc.edu.co/
Palabra clave:
Spatial distribution
Distinctive signs
New creations
Supervised learning
Machine learning
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_47698ec0456929e40e3e95380780368a
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8043
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Use of the industrial property system in Colombia (2018): A supervised learning application
title Use of the industrial property system in Colombia (2018): A supervised learning application
spellingShingle Use of the industrial property system in Colombia (2018): A supervised learning application
Spatial distribution
Distinctive signs
New creations
Supervised learning
Machine learning
title_short Use of the industrial property system in Colombia (2018): A supervised learning application
title_full Use of the industrial property system in Colombia (2018): A supervised learning application
title_fullStr Use of the industrial property system in Colombia (2018): A supervised learning application
title_full_unstemmed Use of the industrial property system in Colombia (2018): A supervised learning application
title_sort Use of the industrial property system in Colombia (2018): A supervised learning application
dc.creator.fl_str_mv Lis-Gutiérrez, Jenny-Paola
Lis Gutiérrez, Melissa
GALLEGO-TORRES, ADRIANA PATRICIA
Ballesteros Ballesteros, Vladimir
Romero-Ospina, Manuel Francisco
dc.contributor.author.spa.fl_str_mv Lis-Gutiérrez, Jenny-Paola
Lis Gutiérrez, Melissa
GALLEGO-TORRES, ADRIANA PATRICIA
Ballesteros Ballesteros, Vladimir
Romero-Ospina, Manuel Francisco
dc.subject.spa.fl_str_mv Spatial distribution
Distinctive signs
New creations
Supervised learning
Machine learning
topic Spatial distribution
Distinctive signs
New creations
Supervised learning
Machine learning
description The purpose of this paper is to establish ways to predict the spatial distribution of the use of the intellectual property system from information on industrial property applications and grants (distinctive signs and new creations) and copyright registrations in 2018. This will be done using supervised learning algorithms applied to information on industrial property applications and grants (trademarks and new creations) and copyright registrations in 2018. Within the findings, 4 algorithms were identified with a level of explanation higher than 80%: (i) Linear Regression, with an elastic network regularization; (ii) Stochastic Gradient Descent, with Hinge loss function, Ringe regularization (L2) and a constant learning rate; (iii) Neural Networks, with 1,000 layers, with Adam’s solution algorithm and 2,000 iterations; (iv) Random Forest, with 10 trees
publishDate 2020
dc.date.issued.none.fl_str_mv 2020-06-22
dc.date.accessioned.none.fl_str_mv 2021-03-18T16:52:50Z
dc.date.available.none.fl_str_mv 2021-03-18T16:52:50Z
dc.type.spa.fl_str_mv Pre-Publicación
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_816b
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dc.identifier.issn.spa.fl_str_mv 03029743
16113349
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dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-3-030-53956-6_46
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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identifier_str_mv 03029743
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Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8043
https://doi.org/10.1007/978-3-030-53956-6_46
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
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dc.relation.references.spa.fl_str_mv 1. Lis-Gutiérrez, J.P., Zerda-Sarmiento, A., Balaguera, M., Gaitán-Angulo, M., Lis-Gutiérrez, M.: Uso del sistema de propiedad industrial para signos distintivos en Colombia: un análisis departamental (2000–2016). En: Campos, G., Castaño, M., Gaitán-Angulo, M. & Sánchez, V. (Comps). Diálogos sobre investigación: avances científicos Konrad Lorenz, pp 193–215. Bogotá: Konrad Lorenz Editores (2019)
2. Lis-Gutiérrez, J.P., Lis-Gutiérrez, M., Gaitán-Angulo, M., Balaguera, M.I., Viloria, A., Santander-Abril, J.E.: Use of the industrial property system for new creations in colombia: a departmental analysis (2000–2016). In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 786–796. Springer, Cham (2018). 10.1007/978-3-319-93803-5_74
3. WIPO. World intellectual property indicators. Ginebra: OMPI (2018)
4. WIPO. Datos y cifras de la OMPI sobre PI, edición de 2018. Ginebra: OMPI (2019)
6. Dirección Nacional de Derechos de Autor (DNDA). Estadísticas en línea [Base de datos]. Bogotá: DNDA (2019)
7. Moros Ochoa, A., Lis-Gutiérrez, J.P., Castro Nieto, G.Y., Vargas, C.A., Rincón. J.C.: La percepción de calidad de servicio como determinante de la recomendación: una predicción mediante inteligencia artificial para los hoteles en Cartagena. En: G. Campos, M.A. Castaño, M. Gaitán-Angulo, V. Sánchez (comp). Diálogos sobre investigación. Bogotá: Editorial Konrad Lorenz (2020)
8. Lis-Gutiérrez, J.P., Aguilera-Hernández, D., Escobedo David, L.R.: Análisis de las demandas de los integrantes del Ejército colombiano en calidad de víctimas; una aplicación de machine learning. En: G. Barbosa Castillo, M. Correa, y A. Ciro Gómez (eds.), Análisis de las demandas de los integrantes del Ejército en calidad de víctimas: una aplicación de “machine learning”, pp. 437–468. Universidad Externado de Colombia, Bogotá (2020)
9. Alimov A. Intellectual property rights reform and the cost of corporate debt. J. Int. Money Finance. 2019;91:195–211. doi: 10.1016/j.jimonfin.2018.12.004.
10. Sweet C, Eterovic D. Do patent rights matter? 40 years of innovation, complexity and productivity. World Dev. 2019;115:78–93. doi: 10.1016/j.worlddev.2018.10.009.
11. Auriol E, Biancini S, Paillacar R. Universal intellectual property rights: too much of a good thing? Int. J. Ind. Organ. 2019;65:51–81. doi: 10.1016/j.ijindorg.2019.01.003.
12. Campi M, Dueñas M. Intellectual property rights, trade agreements, and international trade. Res. Policy. 2019;48(3):531–545. doi: 10.1016/j.respol.2018.09.011.
13. Papageorgiadis N, McDonald F. Defining and measuring the institutional context of national intellectual property systems in a post-trips world. J. Int. Manag. 2019;25(1):3–18. doi: 10.1016/j.intman.2018.05.002
14. Miric M, Boudreau KJ, Jeppesen LB. Protecting their digital assets: the use of formal & informal appropriability strategies by App developers. Res. Policy. 2019;48(8):103738. doi: 10.1016/j.respol.2019.01.012.
15. Barroso A, Giarratana MS, Pasquini M. Product portfolio performance in new foreign markets: the EU trademark dual system. Res. Policy. 2019;48(1):11–21. doi: 10.1016/j.respol.2018.07.013.
16. Denicolai S, Hagen B, Zucchella A, Dudinskaya EC. When less family is more: trademark acquisition, family ownership, and internationalization. Int. Bus. Rev. 2019;28(2):238–251. doi: 10.1016/j.ibusrev.2018.09.002
17. Teixeira AA, Ferreira C. Intellectual property rights and the competitiveness of academic spin-offs. J. Innov. Knowl. 2019;4(3):154–161. doi: 10.1016/j.jik.2018.12.002.
18. Zhang D, Zheng W, Ning L. Does innovation facilitate firm survival? Evidence from chinese high-tech firms. Econ. Model. 2018;75:458–468. doi: 10.1016/j.econmod.2018.07.030
19. Kannan R, Vasanthi V. Soft Computing and Medical Bioinformatics. Singapore: Springer; 2019. Machine learning algorithms with roc curve for predicting and diagnosing the heart disease; pp. 63–72.
20. Wu, C.C., et al.: Prediction of fatty liver disease using machine learning algorithms. Comput. Methods Programs Biomed. 170, 23–29 (2019
21. Alic, A.S., et al.: BIGSEA: a big data analytics platform for public transportation information. Future Gen. Comput. Syst. 96, 243–269 (2019)
22. Banik D, Ekbal A, Bhattacharyya P. Machine learning based optimized pruning approach for decoding in statistical machine translation. IEEE Access. 2019;7:1736–1751. doi: 10.1109/ACCESS.2018.2883738.
23. Aguilar, R., Torres, J., Martín, C.: Aprendizaje Automático en la Identificación de Sistemas. Un caso de estudio en la generación de un parque eólico. Revista iberoamericana de automática e informática industrial 16(1), 114–127 (2018)
24. Aristodemou L, Tietze F. The state-of-the-art on Intellectual Property Analytics (IPA): a literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data. World Patent Inf. 2018;55:37–51. doi: 10.1016/j.wpi.2018.07.002.
25. Havermans QA, Gabaly S, Hidalgo A. Forecasting European trademark and design filings: An innovative approach including exogenous variables and IP offices’ events. World Patent Inf. 2017;48:96–108. doi: 10.1016/j.wpi.2017.01.004.
26. Demsar, J., et al.: Orange: data mining toolbox in Python. J. Mach. Learn. Res. 14(Aug), 2349–2353 (2013)
27. Departamento Administrativo Nacional de Estadística (DANE). Proyecciones de Población Departamental [Base de datos]. Bogotá: Dane (2020)
28. Quitian OIT, Lis-Gutiérrez JP, Viloria A. Supervised and unsupervised learning applied to crowdfunding. Adv. Intell. Syst. Comput. 2020;1108:90–97.
29. Viloria A, Lis-Gutiérrez JP, Gaitán-Angulo M, Stanescu CLV, Crissien T. Machine learning applied to the H index of colombian authors with publications in scopus. Smart Innov. Syst. Technol. 2020;167:388–397. doi: 10.1007/978-981-15-1564-4_36.
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spelling Lis-Gutiérrez, Jenny-PaolaLis Gutiérrez, MelissaGALLEGO-TORRES, ADRIANA PATRICIABallesteros Ballesteros, VladimirRomero-Ospina, Manuel Francisco2021-03-18T16:52:50Z2021-03-18T16:52:50Z2020-06-220302974316113349https://hdl.handle.net/11323/8043https://doi.org/10.1007/978-3-030-53956-6_46Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The purpose of this paper is to establish ways to predict the spatial distribution of the use of the intellectual property system from information on industrial property applications and grants (distinctive signs and new creations) and copyright registrations in 2018. This will be done using supervised learning algorithms applied to information on industrial property applications and grants (trademarks and new creations) and copyright registrations in 2018. Within the findings, 4 algorithms were identified with a level of explanation higher than 80%: (i) Linear Regression, with an elastic network regularization; (ii) Stochastic Gradient Descent, with Hinge loss function, Ringe regularization (L2) and a constant learning rate; (iii) Neural Networks, with 1,000 layers, with Adam’s solution algorithm and 2,000 iterations; (iv) Random Forest, with 10 treesLis-Gutiérrez, Jenny-Paola-will be generated-orcid-0000-0002-1438-7619-600Lis Gutiérrez, Melissa-will be generated-orcid-0000-0002-2598-3088-600GALLEGO-TORRES, ADRIANA PATRICIA-will be generated-orcid-0000-0002-6654-3177-600Ballesteros Ballesteros, Vladimir-will be generated-orcid-0000-0002-6920-789X-600Romero-Ospina, Manuel-Francisco-will be generated-orcid-0000-0002-6457-8827-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lecture Notes in Computer Sciencehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354787/Spatial distributionDistinctive signsNew creationsSupervised learningMachine learningUse of the industrial property system in Colombia (2018): A supervised learning applicationPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersion1. Lis-Gutiérrez, J.P., Zerda-Sarmiento, A., Balaguera, M., Gaitán-Angulo, M., Lis-Gutiérrez, M.: Uso del sistema de propiedad industrial para signos distintivos en Colombia: un análisis departamental (2000–2016). En: Campos, G., Castaño, M., Gaitán-Angulo, M. & Sánchez, V. (Comps). Diálogos sobre investigación: avances científicos Konrad Lorenz, pp 193–215. Bogotá: Konrad Lorenz Editores (2019)2. Lis-Gutiérrez, J.P., Lis-Gutiérrez, M., Gaitán-Angulo, M., Balaguera, M.I., Viloria, A., Santander-Abril, J.E.: Use of the industrial property system for new creations in colombia: a departmental analysis (2000–2016). In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 786–796. Springer, Cham (2018). 10.1007/978-3-319-93803-5_743. WIPO. World intellectual property indicators. Ginebra: OMPI (2018)4. WIPO. Datos y cifras de la OMPI sobre PI, edición de 2018. Ginebra: OMPI (2019)6. Dirección Nacional de Derechos de Autor (DNDA). Estadísticas en línea [Base de datos]. Bogotá: DNDA (2019)7. Moros Ochoa, A., Lis-Gutiérrez, J.P., Castro Nieto, G.Y., Vargas, C.A., Rincón. J.C.: La percepción de calidad de servicio como determinante de la recomendación: una predicción mediante inteligencia artificial para los hoteles en Cartagena. En: G. Campos, M.A. Castaño, M. Gaitán-Angulo, V. Sánchez (comp). Diálogos sobre investigación. Bogotá: Editorial Konrad Lorenz (2020)8. Lis-Gutiérrez, J.P., Aguilera-Hernández, D., Escobedo David, L.R.: Análisis de las demandas de los integrantes del Ejército colombiano en calidad de víctimas; una aplicación de machine learning. En: G. Barbosa Castillo, M. Correa, y A. Ciro Gómez (eds.), Análisis de las demandas de los integrantes del Ejército en calidad de víctimas: una aplicación de “machine learning”, pp. 437–468. Universidad Externado de Colombia, Bogotá (2020)9. Alimov A. Intellectual property rights reform and the cost of corporate debt. J. Int. Money Finance. 2019;91:195–211. doi: 10.1016/j.jimonfin.2018.12.004.10. Sweet C, Eterovic D. Do patent rights matter? 40 years of innovation, complexity and productivity. World Dev. 2019;115:78–93. doi: 10.1016/j.worlddev.2018.10.009.11. Auriol E, Biancini S, Paillacar R. Universal intellectual property rights: too much of a good thing? Int. J. Ind. Organ. 2019;65:51–81. doi: 10.1016/j.ijindorg.2019.01.003.12. Campi M, Dueñas M. Intellectual property rights, trade agreements, and international trade. Res. Policy. 2019;48(3):531–545. doi: 10.1016/j.respol.2018.09.011.13. Papageorgiadis N, McDonald F. Defining and measuring the institutional context of national intellectual property systems in a post-trips world. J. Int. Manag. 2019;25(1):3–18. doi: 10.1016/j.intman.2018.05.00214. Miric M, Boudreau KJ, Jeppesen LB. Protecting their digital assets: the use of formal & informal appropriability strategies by App developers. Res. Policy. 2019;48(8):103738. doi: 10.1016/j.respol.2019.01.012.15. Barroso A, Giarratana MS, Pasquini M. Product portfolio performance in new foreign markets: the EU trademark dual system. Res. Policy. 2019;48(1):11–21. doi: 10.1016/j.respol.2018.07.013.16. Denicolai S, Hagen B, Zucchella A, Dudinskaya EC. When less family is more: trademark acquisition, family ownership, and internationalization. Int. Bus. Rev. 2019;28(2):238–251. doi: 10.1016/j.ibusrev.2018.09.00217. Teixeira AA, Ferreira C. Intellectual property rights and the competitiveness of academic spin-offs. J. Innov. Knowl. 2019;4(3):154–161. doi: 10.1016/j.jik.2018.12.002.18. Zhang D, Zheng W, Ning L. Does innovation facilitate firm survival? Evidence from chinese high-tech firms. Econ. Model. 2018;75:458–468. doi: 10.1016/j.econmod.2018.07.03019. Kannan R, Vasanthi V. Soft Computing and Medical Bioinformatics. Singapore: Springer; 2019. Machine learning algorithms with roc curve for predicting and diagnosing the heart disease; pp. 63–72.20. Wu, C.C., et al.: Prediction of fatty liver disease using machine learning algorithms. Comput. Methods Programs Biomed. 170, 23–29 (201921. Alic, A.S., et al.: BIGSEA: a big data analytics platform for public transportation information. Future Gen. Comput. Syst. 96, 243–269 (2019)22. Banik D, Ekbal A, Bhattacharyya P. Machine learning based optimized pruning approach for decoding in statistical machine translation. IEEE Access. 2019;7:1736–1751. doi: 10.1109/ACCESS.2018.2883738.23. Aguilar, R., Torres, J., Martín, C.: Aprendizaje Automático en la Identificación de Sistemas. Un caso de estudio en la generación de un parque eólico. Revista iberoamericana de automática e informática industrial 16(1), 114–127 (2018)24. Aristodemou L, Tietze F. The state-of-the-art on Intellectual Property Analytics (IPA): a literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data. World Patent Inf. 2018;55:37–51. doi: 10.1016/j.wpi.2018.07.002.25. Havermans QA, Gabaly S, Hidalgo A. Forecasting European trademark and design filings: An innovative approach including exogenous variables and IP offices’ events. World Patent Inf. 2017;48:96–108. doi: 10.1016/j.wpi.2017.01.004.26. Demsar, J., et al.: Orange: data mining toolbox in Python. J. Mach. Learn. Res. 14(Aug), 2349–2353 (2013)27. Departamento Administrativo Nacional de Estadística (DANE). Proyecciones de Población Departamental [Base de datos]. Bogotá: Dane (2020)28. Quitian OIT, Lis-Gutiérrez JP, Viloria A. Supervised and unsupervised learning applied to crowdfunding. Adv. Intell. Syst. Comput. 2020;1108:90–97.29. Viloria A, Lis-Gutiérrez JP, Gaitán-Angulo M, Stanescu CLV, Crissien T. Machine learning applied to the H index of colombian authors with publications in scopus. Smart Innov. Syst. 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