Purpose： This paper suggests a framework to identify important patents for building potential patent portfolios based on patents owned by different assignees so as to highlight the value of individual patents in tech...Purpose： This paper suggests a framework to identify important patents for building potential patent portfolios based on patents owned by different assignees so as to highlight the value of individual patents in technology transfer and identify potential collaborators for patent assignees. Design/methodology/approach： The analysis framework includes the following steps： l） co-classification analysis based on the International Patent Classification （IPC） codes and Derwent Manual Codes （DMC） to detect sub-tech fields, 2） keyword co-occurrence analysis aiming to understand the core technology information in each patent, and 3） social network analysis used for identifying important technologies and partnerships of key assignees. A case study was conducted with 27,401 chemistry patents filed by a Chinese national research institute. Findings： The results show that this framework is effective in building potential technological patent portfolios based on patents owned by different assignees and identifying future collaborators for the assignees. This integrated approach based on topic identification and correlation analysis that combines network-based analysis with keyword-based analysis can reveal important patented technologies and their connections and help understand detailed technological information mentioned in patents. Research limitations： In keywords analysis, only titles and abstracts of patent documents were used and weights of keywords in different parts of the documents were not considered.Practical implications： The analysis framework provides valuable information for decision- makers of large institutions which have many patents with broad application prospects. Originality/value： Different from previous patent portfolio studies based on the use of a combination of patent analysis indicators, this study provides insights into a method of building patent portfolios to discover the potential of individual patents in technology transfer and promote cooperation among different patent assignees.展开更多
Emerging topics in app reviews highlight the topics(e.g.,software bugs)with which users are concerned during certain periods.Identifying emerging topics accurately,and in a timely manner,could help developers more eff...Emerging topics in app reviews highlight the topics(e.g.,software bugs)with which users are concerned during certain periods.Identifying emerging topics accurately,and in a timely manner,could help developers more effectively update apps.Methods for identifying emerging topics in app reviews based on topic models or clustering methods have been proposed in the literature.However,the accuracy of emerging topic identification is reduced because reviews are short in length and offer limited information.To solve this problem,an improved emerging topic identification(IETI)approach is proposed in this work.Specifically,we adopt natural language processing techniques to reduce noisy data,and identify emerging topics in app reviews using the adaptive online biterm topic model.Then we interpret the implicature of emerging topics through relevant phrases and sentences.We adopt the official app changelogs as ground truth,and evaluate IETI in six common apps.The experimental results indicate that IETI is more accurate than the baseline in identifying emerging topics,with improvements in the F1 score of 0.126 for phrase labels and 0.061 for sentence labels.Finally,we release the codes of IETI on Github(https://github.com/wanizhou/IETI).展开更多
As blockchain technology advances,non-fungible tokens(NFTs)are emerging as unconventional assets in the commercial market.However,it is necessary to establish a comprehensive NFT ecosystem that addresses the prevailin...As blockchain technology advances,non-fungible tokens(NFTs)are emerging as unconventional assets in the commercial market.However,it is necessary to establish a comprehensive NFT ecosystem that addresses the prevailing public concerns.This study aimed to bridge this gap by analyzing user-generated content on prominent social media platforms such as Twitter,Weibo,and Reddit.Employing text clustering and topic modeling techniques,such as Latent Dirichlet Allocation,we constructed an analytical framework to delve into the intricacies of the NFT ecosystem.Our investigation revealed seven distinct topics from Twitter and Reddit data and eight topics from Weibo data.Weibo users predominantly engaged in reviews and critiques,whereas Twitter and Reddit users emphasized personal experiences and perceptions.The NFT ecosystem encompasses several crucial elements,including transactions,customers,infrastructure,products,environments,and perceptions.By identifying the prevailing trends and common issues,this study offers valuable guidance for the development of NFT ecosystems.展开更多
基金supported by the Science and Technology Service Network Initiative of Chinese Academy of Sciences(Grant No.:KFJ-EW-STS-032)the West Light Foundation of Chinese Academy of Sciences(Grant No.:Y4C0091001)the National Social Science Foundation of China(Grant No.:14CTQ033)
文摘Purpose： This paper suggests a framework to identify important patents for building potential patent portfolios based on patents owned by different assignees so as to highlight the value of individual patents in technology transfer and identify potential collaborators for patent assignees. Design/methodology/approach： The analysis framework includes the following steps： l） co-classification analysis based on the International Patent Classification （IPC） codes and Derwent Manual Codes （DMC） to detect sub-tech fields, 2） keyword co-occurrence analysis aiming to understand the core technology information in each patent, and 3） social network analysis used for identifying important technologies and partnerships of key assignees. A case study was conducted with 27,401 chemistry patents filed by a Chinese national research institute. Findings： The results show that this framework is effective in building potential technological patent portfolios based on patents owned by different assignees and identifying future collaborators for the assignees. This integrated approach based on topic identification and correlation analysis that combines network-based analysis with keyword-based analysis can reveal important patented technologies and their connections and help understand detailed technological information mentioned in patents. Research limitations： In keywords analysis, only titles and abstracts of patent documents were used and weights of keywords in different parts of the documents were not considered.Practical implications： The analysis framework provides valuable information for decision- makers of large institutions which have many patents with broad application prospects. Originality/value： Different from previous patent portfolio studies based on the use of a combination of patent analysis indicators, this study provides insights into a method of building patent portfolios to discover the potential of individual patents in technology transfer and promote cooperation among different patent assignees.
基金Project supported by the Anhui Provincial Natural Science Foundation of China(No.1908085MF183)the National Natural Science Foundation of China(Nos.62002084and 61976005)+4 种基金the Training Program for Young and MiddleAged Top Talents of Anhui Polytechnic University,China(No.201812)the Zhejiang Provincial Natural Science Foundation of China(No.LQ21F020004)the State Key Laboratory for Novel Software Technology(Nanjing University)Research Program,China(No.KFKT2019B23)the Open Research Fund of Anhui Key Laboratory of Detection Technology and Energy Saving Devices,Anhui Polytechnic University,China(No.DTESD2020B03)the Stable Support Plan for Colleges and Universities in Shenzhen,China(No.GXWD20201230155427003-20200730101839009)。
文摘Emerging topics in app reviews highlight the topics(e.g.,software bugs)with which users are concerned during certain periods.Identifying emerging topics accurately,and in a timely manner,could help developers more effectively update apps.Methods for identifying emerging topics in app reviews based on topic models or clustering methods have been proposed in the literature.However,the accuracy of emerging topic identification is reduced because reviews are short in length and offer limited information.To solve this problem,an improved emerging topic identification(IETI)approach is proposed in this work.Specifically,we adopt natural language processing techniques to reduce noisy data,and identify emerging topics in app reviews using the adaptive online biterm topic model.Then we interpret the implicature of emerging topics through relevant phrases and sentences.We adopt the official app changelogs as ground truth,and evaluate IETI in six common apps.The experimental results indicate that IETI is more accurate than the baseline in identifying emerging topics,with improvements in the F1 score of 0.126 for phrase labels and 0.061 for sentence labels.Finally,we release the codes of IETI on Github(https://github.com/wanizhou/IETI).
基金funded by the National Social Science Fund of China(22CTQ019).
文摘As blockchain technology advances,non-fungible tokens(NFTs)are emerging as unconventional assets in the commercial market.However,it is necessary to establish a comprehensive NFT ecosystem that addresses the prevailing public concerns.This study aimed to bridge this gap by analyzing user-generated content on prominent social media platforms such as Twitter,Weibo,and Reddit.Employing text clustering and topic modeling techniques,such as Latent Dirichlet Allocation,we constructed an analytical framework to delve into the intricacies of the NFT ecosystem.Our investigation revealed seven distinct topics from Twitter and Reddit data and eight topics from Weibo data.Weibo users predominantly engaged in reviews and critiques,whereas Twitter and Reddit users emphasized personal experiences and perceptions.The NFT ecosystem encompasses several crucial elements,including transactions,customers,infrastructure,products,environments,and perceptions.By identifying the prevailing trends and common issues,this study offers valuable guidance for the development of NFT ecosystems.
