本文基于一篇关于利用大数据进行预测的专利分析论文，探讨了如何利用文本挖掘技术分析专利数据，预测大数据在预测分析领域的发展趋势。通过分析2013年至2017年期间的专利数据，研究人员利用WordStat等软件，对专利摘要中的关键词进行分析，发现主题、聚类和关联关系，从而帮助投资者和发明家了解特定技术的未来发展方向，并做出更明智的投资或研发决策。该研究方法涉及专利检索、时间线分析、地理来源分析、IPC分类分析以及文本挖掘等多个环节，最终能够回答关于大数据预测分析领域的关键问题，例如何时、何地、谁以及什么等。

🤔**专利数据分析：**研究人员利用PatSeer数据库，收集了2013年至2017年期间与大数据预测分析相关的专利数据，并进行了纵向分析。

🔎**文本挖掘技术应用：**采用WordStat等文本分析软件，对专利摘要进行分析，提取最多包含5个词的短语，并进行聚类分析，识别出最常出现的主题。

📊**主题关联分析：**利用邻近图和网络图分析关键词之间的关联关系，揭示关键词共现模式和潜在结构，帮助理解技术发展趋势。

💡**投资者和发明家决策支持：**通过专利分析，回答关于大数据预测分析领域的关键问题，例如何时、何地、谁以及什么，为投资者和发明家提供决策支持。

From time-to-time in this Blog we draw your attention to some of the applications of text analytics. In the last few years one of those areas has been predictive analysis, using text mining to explore large databases. Using text mining to learn from past behavior, discover patterns and identify trends, allows researchers are able to make predictions in different fields. This blog is based on the paper Big Data for Prediction: Patent Analysis. The analysis of patents using text mining can help investors and inventors get a better sense of where a certain technology is going and help them make decisions about where to invest or where to concentrate their efforts. In their paper the authors used two text analytics software one of which was WordStat. This is a brief summary of the paper.

There are many databases of patents around the world and many commercial patent platforms. The authors chose to examine patents from the PatSeer database related to big data usage for prediction analytics from 2013 to October 13, 2017. Patents were analyzed, using the longitudinal approach in combination with text mining techniques. The patent analysis consists of four phases related to (i) the patent search and selection, (ii) timeline, geographic origin and patents assignees analysis, (iii) patents analysis according to IPC system patent area, and (iv) text mining to discover the topics emerging most often in the abstracts of the patents.

Once the authors had done their initial search and selection of active simple patent families they analyzed the technical content based on the International Patent Classification (IPC) system. They then used WordStat to find phrases of a maximum of five words that occurred in more than five simple family patent abstracts. Next they performed cluster analysis on the phrases to find topics. They used proximity plots to see which phrases occurred with the most frequent and most important phrases. They also used network graphs of clusters to explore connections between keywords and to detect underlying patterns and structures of co-occurrences.

 

The authors then looked at the questions that can be answered for investors and inventors interested in Big Data solutions for predictive analytics and how patent analysis can provide some answers to basic questions such as; when, where, who and what.

You can read the complete paper here  https://bit.ly/2sqQ5br