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首页> 外文会议>Portland International Center for Management of Engineering and Technology >Unsupervised learning based patent landscapes using full-text patent data
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Unsupervised learning based patent landscapes using full-text patent data

机译：使用全文专利数据的无监督学习式专利态势

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The complexity technologies require that companies have in-depth knowledge of the nature and effect of knowledge - its depth and breadth. Companies need to master expanding technological knowledge bases creating tensions for MOT. We examine how big data in patent landscaping creates insights into MOT. Using big data to manage Competitive Technical Intelligence, companies can foster new forms of adaptive learning processes in MOT. This however requires that managers augment human judgment with machine-learning tools, prompting challenges to management traditions. We demonstrate how unsupervised learning creates insight into MOT by identifying topical knowledge foci and showing the dynamics of knowledge domains among companies. Using unsupervised learning and network analysis; we show how a semantic analysis leads to the identification of opportunities in complex environments. We illustrate this using a case in globally operating telecommunication companies using a full-text copy of USPTO-database with approximately 6 million patents data. Our results show the landscape of the companies and the underlying knowledge embedded in the companies. We discuss how managers can evaluate their technological knowledge against competitors, balancing current needs with the adoption of new knowledge. We further discuss how a semantic analysis can lead to the discovery of latent patterns and identification of opportunities.
机译：复杂性技术要求公司对知识的性质和作用有深入的了解，即知识的深度和广度。公司需要掌握不断扩展的技术知识基础，从而为MOT带来压力。我们研究了专利环境美化中的大数据如何产生对MOT的见解。利用大数据来管理竞争技术情报，公司可以在MOT中培育新形式的适应性学习过程。但是，这要求管理人员使用机器学习工具来增强人的判断力，从而对管理传统提出挑战。我们通过识别主题知识焦点并显示公司之间知识领域的动态，展示了无监督学习如何创建MOT的见解。使用无监督学习和网络分析；我们展示了语义分析如何导致复杂环境中机会的识别。我们以全球运营的电信公司中的案例为例进行说明，该案例使用USPTO数据库的全文本以及大约600万项专利数据。我们的结果显示了公司的概况以及公司中嵌入的基础知识。我们讨论了管理者如何针对竞争对手评估其技术知识，从而在当前需求与采用新知识之间取得平衡。我们将进一步讨论语义分析如何导致潜在模式的发现和机会的识别。

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