Machine learning and natural language processing on the patent corpus: Data, tools, and new measures. Issue 3 (31st July 2018)

• Record Type: Journal Article
• Authors: Balsmeier, Benjamin; Assaf, Mohamad; Chesebro, Tyler; Fierro, Gabe; Johnson, Kevin; Johnson, Scott; Li, Guan‐Cheng; Lück, Sonja; O'Reagan, Doug; Yeh, Bill; Zang, Guangzheng; Fleming, Lee
• Is Part Of: Journal of economics & management strategy. Volume 27:Issue 3(2018)
• Page Start: 535
• Page End: 553
• Publication Date: 2018-07-31
• Subjects: database -- disambiguation -- machine learning -- natural language processing -- patent -- social networks -- C80 -- C81 -- C88 -- O33 -- O34; Economics -- Periodicals; Business -- Periodicals; Management -- Periodicals; Strategic planning -- Periodicals; 330.1
• DOI: 10.1111/jems.12259
• Languages: English
• ISSNs: 1058-6407

Abstract

Abstract: Drawing upon recent advances in machine learning and natural language processing, we introduce new tools that automatically ingest, parse, disambiguate, and build an updated database using U.S. patent data. The tools identify unique inventor, assignee, and location entities mentioned on each granted U.S. patent from 1976 to 2016. We describe data flow, algorithms, user interfaces, descriptive statistics, and a novelty measure based on the first appearance of a word in the patent corpus. We illustrate an automated coinventor network mapping tool and visualize trends in patenting over the last 40 years. Data and documentation can be found athttps://console.cloud.google.com/launcher/partners/patents-public-data .