Forecasting European trade mark and design filings: An innovative approach including exogenous variables and IP offices' events

Havermans, Quirinus A.; Gabaly, Samuel; Nuchera, Antonio Hidalgo

doi:10.1016/j.wpi.2017.01.004

Cited by 6 publications

(7 citation statements)

References 12 publications

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“…In the case of supervised learning it was possible to identify 4 algorithms with a level of explanation higher than 80%, these are: (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. The results found in this study are consistent with those of [ 25 ], and some algorithms are added.…”

Section: Discussionsupporting

confidence: 92%

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Use of the Industrial Property System in Colombia (2018): A Supervised Learning Application

Lis-Gutiérrez

Torres

et al. 2020

Lecture Notes in Computer Science

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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.

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Section: Discussionsupporting

confidence: 92%

“…As for the applications of machine learning to intellectual property, in [ 24 ], they reviewed 57 papers on artificial intelligence, automatic and in-depth learning associated with intellectual property. In [ 25 ], the employed algorithms were Support Vector Machines, Neural Networks and Decision Trees.…”

Section: Literature Reviewmentioning

confidence: 99%

Use of the Industrial Property System in Colombia (2018): A Supervised Learning Application

Lis-Gutiérrez

Torres

et al. 2020

Lecture Notes in Computer Science

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“…The years where there is a lower numbers of grants are relatively many more when compared to the years with higher numbers of patents. Several studies have shown that it is possible to model the series of patents with ARIMA [41][42][43][44]. Some satisfactory model adjustments can be required to decrease the model residuals.…”

Section: Methods and The Case Studymentioning

confidence: 99%

Effects of Clean Air Act on Patenting Activities in Chemical Industry: Learning from Past Experiences

Durmuşoğlu

2017

Sustainability

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Abstract:The chemical industry provides essential goods we use in our daily lives and key ingredients for many diverse industries. On the other hand, their production and use require serious attention while they may be seriously harmful to local air quality. The Clean Air Act (CAA) and its subsequent amendments regulate the emissions of hazardous air pollutants to protect public health and welfare in the U.S.A. since 1970. This study aimed to assess the impact of CAA on the rate of patenting in the chemical industry. With this in mind, basic chemical utility patents were analyzed to detect the effects of CAA on the patenting activities. Subsequent to the fitting of the ARIMA (Autoregressive Integrated Moving Average) model (producing the least square of errors), a significant outlier was detected with the Dixon's r22 Ratio Test. This outlier was the number of patents granted in 1972. Contextual queries in the text of those patent documents have shown that there was a considerable increase in the patents of chemistry which takes the sustainability relevant terms (air, emission, pollution, etc.) into consideration. It was concluded that companies in the chemistry industry adapted themselves very rapidly to the changes and CAA was an important incentive to create novel technologies.

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“…La técnica LR combina métodos de árboles de decisión convencionales con funciones de regresión lineal en los nodos del árbol de decisión para seleccionar los mejores modelos posibles de predicción basados en sus AIC. Los mejores modelos de regresión lineal tendrán los menores valores de AIC (Havermans et al, 2017 El modelo cuadrático se ha construido sobre la serie de datos a medio plazo, es decir, desde 2000 hasta 2016. El criterio para llevar a cabo la selección del modelo más apropiado se ha basado en la bondad del ajuste de cada modelo a los datos.…”

Section: 23ii áRboles De Regresión Lineal (Linear Regression Trees Lr)unclassified

“…Los indicadores seleccionados son los utilizados de forma más habitual en la investigación e evaluación de la predicción y son los siguientes (Havermans, Gabaly, & Hidalgo, 2017). Los modelos que obtengan los menores valores de error en los indicadores señalados son los más interesantes debido a su capacidad de detectar tendencias en los datos y mejorar valores de predicción obtenidos por métodos basados en la extrapolación de tendencias.…”

Section: Análisis De La Viabilidad De Los Modelosunclassified

Modelos de predicción de marcas en oficinas de propiedad industrial

García¹

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2.1.Introducción 2.2.La protección a través de patentes y marcas 2.3.Planes estratégicos en las principales Oficinas de Propiedad industrial 2.3.1.Descripción de las principales oficinas de propiedad industrial 2.3.1.i.Oficina de Propiedad industrial de los Estados Unidos (USPTO) 2.3.1.ii.Oficina de Propiedad industrial de Japón (JPO) 2.3.1.iii.Oficina de Propiedad Intelectual de Corea (KIPO) 2.3.1.iv.Oficina Europea de Patentes (EPO) 2.3.1.v.Oficina de la Propiedad Intelectual China (SIPO) 2.3.1.vi.Oficina Europea de la Propiedad Intelectual (EUIPO) 2.3.1.vii.Oficina Española de Patentes y Marcas (OEPM) 2.3.2. Rasgos comunes y diferenciadores de los planes estratégicos de las oficinas de propiedad industrial 2.

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Forecasting European trade mark and design filings: An innovative approach including exogenous variables and IP offices' events

Cited by 6 publications

References 12 publications

Use of the Industrial Property System in Colombia (2018): A Supervised Learning Application

Use of the Industrial Property System in Colombia (2018): A Supervised Learning Application

Effects of Clean Air Act on Patenting Activities in Chemical Industry: Learning from Past Experiences

Modelos de predicción de marcas en oficinas de propiedad industrial

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