Andrew A. Toole, Christina Jones, Sarvothaman Madhavan
What are the connections between science and improvements in economic and social outcomes that drive the “value” of science? Most of the time, these connections are circuitous, varied, diffuse, and opaque. Patent data offer an opportunity to expose new connections between science and technology as well as exposing links to downstream economic and social outcomes. The PatentsView open data platform performs data preparation, visualization, and adds helpful features to USPTO’s administrative data. PatentsView is fundamentally a free “intermediate good” that provides the needed materials for researchers, policymakers, and students to conduct their own analyses, make their own linkages, and derive their own insights. This paper provides a quick tour of the PatentsView platform.
{"title":"PatentsView: An Open Data Platform to Advance Science and Technology Policy","authors":"Andrew A. Toole, Christina Jones, Sarvothaman Madhavan","doi":"10.2139/ssrn.3874213","DOIUrl":"https://doi.org/10.2139/ssrn.3874213","url":null,"abstract":"What are the connections between science and improvements in economic and social outcomes that drive the “value” of science? Most of the time, these connections are circuitous, varied, diffuse, and opaque. Patent data offer an opportunity to expose new connections between science and technology as well as exposing links to downstream economic and social outcomes. The PatentsView open data platform performs data preparation, visualization, and adds helpful features to USPTO’s administrative data. PatentsView is fundamentally a free “intermediate good” that provides the needed materials for researchers, policymakers, and students to conduct their own analyses, make their own linkages, and derive their own insights. This paper provides a quick tour of the PatentsView platform.","PeriodicalId":246105,"journal":{"name":"US Patent & Trademark Office (USPTO) Economic Research Paper Series","volume":"268 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132697306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jake Dubbert, Alexander V. Giczy, Nicholas A. Pairolero, Andrew A. Toole
This paper surveys the landscape of empirical studies on cross-border trade in knowledge that use IPRs data. Based on a thorough search of the literature, we identify and categorize the types and uses of IPRs data. Our discussion critically evaluates whether these data support the empirical findings in the studies by identifying where IPRs data are particularly useful and where these data have limitations. The final section of the paper discusses the potential value of making greater use of IPRs assignment data. The goal is to provide a reference to help policymakers evaluate the trade in knowledge literature, particularly the interpretation of IPRs-based evidence for policy decisions.
{"title":"Using Intellectual Property Data to Measure Cross-border Knowledge Flows","authors":"Jake Dubbert, Alexander V. Giczy, Nicholas A. Pairolero, Andrew A. Toole","doi":"10.2139/ssrn.3386326","DOIUrl":"https://doi.org/10.2139/ssrn.3386326","url":null,"abstract":"This paper surveys the landscape of empirical studies on cross-border trade in knowledge that use IPRs data. Based on a thorough search of the literature, we identify and categorize the types and uses of IPRs data. Our discussion critically evaluates whether these data support the empirical findings in the studies by identifying where IPRs data are particularly useful and where these data have limitations. The final section of the paper discusses the potential value of making greater use of IPRs assignment data. The goal is to provide a reference to help policymakers evaluate the trade in knowledge literature, particularly the interpretation of IPRs-based evidence for policy decisions.","PeriodicalId":246105,"journal":{"name":"US Patent & Trademark Office (USPTO) Economic Research Paper Series","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123767385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Release of the United States Patent and Trademark Office (USPTO) Office Action Research Dataset for Patents marks the first time that comprehensive data on examiner-issued rejections are readily available to the research community. An “Office action” is a written notification to the applicant of the examiner’s decision on patentability and generally discloses information, such as the grounds for a rejection, the claims affected, and the pertinent prior art. The relative inaccessibility of Office actions and the considerable effort required to obtain meaningful data therefrom has largely prevented researchers from fully exploiting this valuable information. We aim to rectify this situation by using natural language processing and machine learning techniques to systematically extract information from Office actions and construct a relational database of key data elements. This paper describes our methods and provides an overview of the main data files and variables. This data release consists of three files derived from 4.4 million Office actions mailed during the 2008 to mid-2017 period from USPTO examiners to the applicants of 2.2 million unique patent applications.
{"title":"USPTO Patent Prosecution Research Data: Unlocking Office Action Traits","authors":"Qiang Lu, Amanda F. Myers, Scott Beliveau","doi":"10.2139/SSRN.3024621","DOIUrl":"https://doi.org/10.2139/SSRN.3024621","url":null,"abstract":"Release of the United States Patent and Trademark Office (USPTO) Office Action Research Dataset for Patents marks the first time that comprehensive data on examiner-issued rejections are readily available to the research community. An “Office action” is a written notification to the applicant of the examiner’s decision on patentability and generally discloses information, such as the grounds for a rejection, the claims affected, and the pertinent prior art. The relative inaccessibility of Office actions and the considerable effort required to obtain meaningful data therefrom has largely prevented researchers from fully exploiting this valuable information. We aim to rectify this situation by using natural language processing and machine learning techniques to systematically extract information from Office actions and construct a relational database of key data elements. This paper describes our methods and provides an overview of the main data files and variables. This data release consists of three files derived from 4.4 million Office actions mailed during the 2008 to mid-2017 period from USPTO examiners to the applicants of 2.2 million unique patent applications.","PeriodicalId":246105,"journal":{"name":"US Patent & Trademark Office (USPTO) Economic Research Paper Series","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125732489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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