Purpose:Patent classification is one of the areas in Intellectual Property Analytics(IPA),and a growing use case since the number of patent applications has been increasing worldwide.We propose using machine learning ...Purpose:Patent classification is one of the areas in Intellectual Property Analytics(IPA),and a growing use case since the number of patent applications has been increasing worldwide.We propose using machine learning algorithms to classify Portuguese patents and evaluate the performance of transfer learning methodologies to solve this task.Design/methodology/approach:We applied three different approaches in this paper.First,we used a dataset available by INPI to explore traditional machine learning algorithms and ensemble methods.After preprocessing data by applying TF-IDF,FastText and Doc2Vec,the models were evaluated by cross-validation in 5 folds.In a second approach,we used two different Neural Networks architectures,a Convolutional Neural Network(CNN)and a bi-directional Long Short-Term Memory(BiLSTM).Finally,we used pre-trained BERT,DistilBERT,and ULMFiT models in the third approach.Findings:BERTTimbau,a BERT architecture model pre-trained on a large Portuguese corpus,presented the best results for the task,even though with a performance of only 4%superior to a LinearSVC model using TF-IDF feature engineering.Research limitations:The dataset was highly imbalanced,as usual in patent applications,so the classes with the lowest samples were expected to present the worst performance.That result happened in some cases,especially in classes with less than 60 training samples.Practical implications:Patent classification is challenging because of the hierarchical classification system,the context overlap,and the underrepresentation of the classes.However,the final model presented an acceptable performance given the size of the dataset and the task complexity.This model can support the decision and improve the time by proposing a category in the second level of ICP,which is one of the critical phases of the grant patent process.Originality/value:To our knowledge,the proposed models were never implemented for Portuguese patent classification.展开更多
As the foundation of an industrialized country nowadays,machine tools industry is regarded as the engine of industrial development of a country.The developed countries,such as USA,Germany and Japan,have widely deploye...As the foundation of an industrialized country nowadays,machine tools industry is regarded as the engine of industrial development of a country.The developed countries,such as USA,Germany and Japan,have widely deployed the technology of using the patent in order to keep their strength in various fields.This research examins the CNC machine tools industry in the world by using the patent analysis method.It first gives an overview about the world patent application in CNC machine tools industry from 1963 to 2010 and divides the development of the industry into five stages.It also lists the patent application of the world top 20 countries,where the top 5 countries are compared.The patents of the world top 10 companies of machine tools manufacturers are mapped according to the international patent classification(IPC),and the future trends of world machine tools industry are discussed.Finally conclusions and suggestions are presented.展开更多
基金This work was supported by national funds through FCT(Fundação para a Ciência e a Tecnologia),under the project-UIDB/04152/2020-Centro de Investigação em Gestão de Informação(MagIC)/NOVA IMS.
文摘Purpose:Patent classification is one of the areas in Intellectual Property Analytics(IPA),and a growing use case since the number of patent applications has been increasing worldwide.We propose using machine learning algorithms to classify Portuguese patents and evaluate the performance of transfer learning methodologies to solve this task.Design/methodology/approach:We applied three different approaches in this paper.First,we used a dataset available by INPI to explore traditional machine learning algorithms and ensemble methods.After preprocessing data by applying TF-IDF,FastText and Doc2Vec,the models were evaluated by cross-validation in 5 folds.In a second approach,we used two different Neural Networks architectures,a Convolutional Neural Network(CNN)and a bi-directional Long Short-Term Memory(BiLSTM).Finally,we used pre-trained BERT,DistilBERT,and ULMFiT models in the third approach.Findings:BERTTimbau,a BERT architecture model pre-trained on a large Portuguese corpus,presented the best results for the task,even though with a performance of only 4%superior to a LinearSVC model using TF-IDF feature engineering.Research limitations:The dataset was highly imbalanced,as usual in patent applications,so the classes with the lowest samples were expected to present the worst performance.That result happened in some cases,especially in classes with less than 60 training samples.Practical implications:Patent classification is challenging because of the hierarchical classification system,the context overlap,and the underrepresentation of the classes.However,the final model presented an acceptable performance given the size of the dataset and the task complexity.This model can support the decision and improve the time by proposing a category in the second level of ICP,which is one of the critical phases of the grant patent process.Originality/value:To our knowledge,the proposed models were never implemented for Portuguese patent classification.
基金Supported by Scientific Monitoring and Key Areas in-Depth Investigation and Research(No.ZD2012-4-2)Special Project of Scientific and Technological Basic Works(No.2009FY241000)Science and Technology Major Specific Project Core Electronic Elements,High-General Chips and Basic Software(No.2013XM01)
文摘As the foundation of an industrialized country nowadays,machine tools industry is regarded as the engine of industrial development of a country.The developed countries,such as USA,Germany and Japan,have widely deployed the technology of using the patent in order to keep their strength in various fields.This research examins the CNC machine tools industry in the world by using the patent analysis method.It first gives an overview about the world patent application in CNC machine tools industry from 1963 to 2010 and divides the development of the industry into five stages.It also lists the patent application of the world top 20 countries,where the top 5 countries are compared.The patents of the world top 10 companies of machine tools manufacturers are mapped according to the international patent classification(IPC),and the future trends of world machine tools industry are discussed.Finally conclusions and suggestions are presented.
