PatentsView (description below) will go offline on March 28th. This torrent includes all bulk downloadable tables from: , along with the data dictionaries and the published logic diagram. Zip files contain tab-delimited files that are considerably larger than the zip files when uncompressed. The data includes patent activity from 1976 to 2024. Description: PatentsView is an award-winning visualization, data dissemination, and analysis platform that focuses on intellectual property (IP) data. Support for the site and the team that works on it comes from the Office of the Chief Economist at the U.S. Patent & Trademark Office (USPTO). PatentsView serves students, educators, researchers, policymakers, small business owners, and the public. It offers a unique and valuable open data platform providing free data dissemination and value-added analyses to foster better knowledge of the IP system and drive new insights into invention and i

Context

The USPTO grants US patents to inventors and assignees all over the world. For researchers in particular, PatentsView is intended to encourage the study and understanding of the intellectual property (IP) and innovation system; to serve as a fundamental function of the government in creating “public good” platforms in these data; and to eliminate redundant cleaning, converting and matching of these data by individual researchers, thus freeing up researcher time to do what they do best—study IP, innovation, and technological change.

Content

PatentsView Data is a database that longitudinally links inventors, their organizations, locations, and overall patenting activity. The dataset uses data derived from USPTO bulk data files.

Fork this notebook to get started on accessing data in the BigQuery dataset using the BQhelper package to write SQL queries.

Acknowledgements

“PatentsView” by the USPTO, US Department of Agriculture (USDA), the Center for the Science of Science and Innovation Policy, New York University, the University of California at Berkeley, Twin Arch Technologies, and Periscopic, used under CC BY 4.0.

Data Origin: https://bigquery.cloud.google.com/dataset/patents-public-data:patentsview

Banner photo by rawpixel on Unsplash

The DISCERN dataset was developed to support academic research on corporate innovation by linking data on U.S. publicly listed firms from Standard & Poor’s Compustat database to their patents and scientific publications. A key feature of DISCERN is its comprehensive coverage of firms’ subsidiaries and their ownership changes over time, which is crucial for accurately mapping corporate innovation. Patents and publications may be assigned to various legal entities within a firm’s organizational structure. Subsidiaries may change ownership in M&A events. By accounting for these ownership linkages over time, DISCERN enables researchers to construct more precise measures of firms’ knowledge production and examine the factors influencing their R&D investment decisions.

Version 2.0 incorporates several key improvements over the previous version of DISCERN. First, we shift to using the PatentsView database as the main source of patent data and OpenAlex as the main source of scientific publication data. PatentsView is publicly available and continuously maintained directly by the United States Patents & Trademarks Office (USPTO). OpenAlex is currently the only open data source of scientific publication metadata. Using freely available data sources allows us to share both the patent and the publication datasets openly. This enhances data access, which was previously limited due to the use of propriety data. Second, the updated dataset now covers the period from 1980 to 2021, providing an additional six years of data. Third, we transition to using Securities and Exchange Commission (SEC) filings as the primary source of subsidiary data, allowing us to trace ownership linkages further back to the mid-1990s and ensuring a higher degree of reliability compared to the Orbis data used in the original version, which was less reliable and had comprehensive coverage only from 2008. Finally, by transitioning to PatentsView and additional data sourced from the USPTO, we expand the scope of the dataset to include pre-grant patent applications and patent re-assignment information. This addition allows users to study patent applications regardless of grant status and to observe ownership transitions beyond those related to mergers and acquisitions.

A special thanks and appreciation go to Sanskriti Purohit and Ron Rabi for their diligent work and dedication to this effort.

The dataset is freely available under the O-UDA-1.0 License, permitting unrestricted use for research and commercial purposes. We request that users provide proper citations when utilizing the dataset. The license also allows for the creation of derivative datasets based on DISCERN, with the condition that creators ask their downstream users to cite the original authors appropriately.

If you use the data, please add these citations:

1. Arora, A., Belenzon, S., Cioaca, L., Sheer, L, Shin, H.M. & Shvadron, D. (2024). DISCERN 2.0: Duke Innovation & SCientific Enterprises Research Network [Dataset]. In Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.3594642

2. Arora, A., Belenzon, S., Cioaca, L., Sheer, L, & Shvadron, D. (2024). Back to the Future: Are Big Firms Regaining their Scientific and Technological Dominance? Evidence from DISCERN 2.0 (available soon)

Policymakers are increasingly concerned that incumbent acquisitions of small or young firms may slow down rather than speed up innovation, but it is difficult to identify which firms are related in the fast-changing space of technological innovation. This paper proposes a new, data-driven method to classify patent data into tech-business zones on a probabilistic basis, using patent assignee information. After combining M&A data from S&P Global Market Intelligence with PatentsView data from the US Patent and Trademark Office, we discuss how the zone classification can aid merger reviews and other lines of research.

This database collects and links U.S. federal funded awards to U.S. utility patents, and such patents to virtual patent marking (VPM) pages, in line with two related project: 3PFL and IPRoduct. Specifically, this database looks at awards provided by the U.S. Department of Defense (DOD) within the Small Business Innovation Research (SBIR) and Small Business Technology Transfer Program (STTR) programs from 1984 to 2018.

The database is part of a project, IRIS - Insights on the "Real" Impact of Science. The project aims at assessing how public investment in research and development (R&D) translates into commercial products for the final consumer.

The database is composed of three main elements: awards; patents; and web pages. The database provides several information pieces. This has been possible by making use of several sources, that has been properly combined and further elaborated in a convenient way. Information about the awards comes from the Defense Contract Action Data System (DCADS), for the years 1984--2001, and from USAspending.gov, for the years 2001--2018. Most information about the patents is provided by PatentsView, while specific information comes from the Patent Examination Research Dataset (PatEx) or from PATSTAT.

The file geoc_inv.txt contains identifiers for patent first filings (corresponding to appln_id in PATSTAT), latitude, longitude, city, region, and country of the inventor. Missing coordinates have been imputed from equivalents and other second filings or from information on the location of applicants. The file also contains a variable indicating the source of information ('source'): 1: information comes from the first filing itself 2: information comes from direct equivalent 3: information comes from other subsequent filings 4: information comes from the applicant’s location in first filings 5: information comes from the applicant’s location in the equivalent 6: information comes from the applicant’s location in other subsequent filings; the column 'coord_source' indicates the source of coordinates (whether they come from geolocalisation services, from geonames, or from PatentsView). It is possible to select certain types of first filings based on column 'type'. For example, Paris Convention priority filings can be retrieved by specifying type=priority. The file geoc_app.txt contains location information of applicants. Sources of information (first filings, equivalents, etc.) are thus browsed in reverse order. A detailed data description can be found in de Rassenfosse, Kozak, Seliger 2019: Geocoding of worldwide patent data, published in 'Scientific Data' and available at https://doi.org/10.1038/s41597-019-0264-6. Please note the following: The files geoc_inv_person.txt and geoc_app_person.txt contain person IDs for inventors and applicants, respectively, whenever the location information comes from PATSTAT. If not, the person_id is = 0. These files are not described in the paper. They have been made accessible to improve interoperability with PATSTAT data. Some files had to be zipped in order to upload them to Harvard Dataverse.