Fork this notebook to get started on accessing data in the BigQuery dataset by writing SQL queries using the BQhelper module.

Context

Google Patents Public Data, provided by IFI CLAIMS Patent Services, is a worldwide bibliographic and US full-text dataset of patent publications. Patent information accessibility is critical for examining new patents, informing public policy decisions, managing corporate investment in intellectual property, and promoting future scientific innovation. The growing number of available patent data sources means researchers often spend more time downloading, parsing, loading, syncing and managing local databases than conducting analysis. With these new datasets, researchers and companies can access the data they need from multiple sources in one place, thus spending more time on analysis than data preparation.

Content

The Google Patents Public Data dataset contains a collection of publicly accessible, connected database tables for empirical analysis of the international patent system.

Acknowledgements

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

For more info, see the documentation at https://developers.google.com/web/tools/chrome-user-experience-report/

“Google Patents Public Data” by IFI CLAIMS Patent Services and Google is licensed under a Creative Commons Attribution 4.0 International License.

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The dataset contains the details of Patent Litigation Cases in the United States from 2000 to 2021. The team collected the litigation data in two phases. The first phase looked at data from 2010, specifically within Texas's Western and Eastern Districts. Unified Patent's Portal includes litigation data that each plaintiff has been marked as NPE (Patent Assertion Entity), NPE (Small Company), or NPE (Individual).

Using the definitions, Unified first focused on identifying what NPEs were aggregators and then if they involved third-party financing. NPE aggregators were defined as NPEs with more than one affiliated subsidiary bringing patent litigation. An example of this would be IP Edge and the various limited liability companies underneath IP Edge's control that have brought numerous litigations against operating companies. Third-party financing was defined as evidence of any third party with a financial interest other than the assertors.

With a narrow focus on the Western and Eastern District of Texas, Unified then used several public databases, such as Edgar, USPTO Assignment Records, the NPE Stanford Database, press releases, and its database of NPEs to identify any aggregator and any third-party financial interest, as well as various secretary of state corporate filings or court-ordered disclosures. After these two districts were identified, Unified expanded the data to cover the top five most litigious venues for patents, including the Western and Eastern Districts of Texas, Delaware, and the North and Central Districts of California. (On average, over the past five years, these districts have seen about 70% of all patent litigation.) Once that was completed, that dataset was then expanded to include all jurisdictions from 2010 and on.

The final step was to complete the data set from 2000 to 2009. The team followed a similar data collection process using Lex Machina, the NPE Stanford Database, and Unified's Portal. Unified identified all of the litigation known to be NPE-related. Using the top five jurisdictions' aggregation and financing data, aggregator entities—such as Intellectual Ventures—were identified using the same methodology. The current dataset covers 2000-2021, determines who is an NPE, notes which NPEs are aggregators, and identifies which aggregators are known to have third-party financing.

Note: there are currently no reporting requirements Federally, at the state level, or in the courts to publicly disclose the financing details of nonpublic entities. Thus, any data analysis of which litigations are funded or financed is incomplete, as many of these arrangements are closely held, private, and unknown even to the courts and the parties to the actions. This data set describes the minimum known amount of third-party-funded patent litigation. It is necessarily underinclusive of all nonpublic deals for which there is no available evidence or insight. For further generalized industry information on the size and scope of litigation funding for patent litigations, private sources often report on the size and scope of the burgeoning industry in the aggregate. For example, see Westfleet Advisor's 2021 Litigation Finance Report, available at https://www.westfleetadvisors.com/publications/2021-litigation-finance-report/.

This data is already available to the public in weekly XML snapshots via our online register but has been compiled into a snapshot to make it easier for statistical research to be undertaken.

The attached documents describe the data.

Please note: The GB patent data file may not open with older spreadsheet software.

Contains four research datasets containing time series and micro-level data by National Bureau of Economic Research (NBER) technology sub-category on applications, grants, and in-force patents spanning two centuries of innovation.

This page provides the data resulting from linking assignees and assignors in the USPTO Patent Assignment Dataset to Compustat gvkeys. We work with a version of the USPTO PAD that was gracefully shared with us by Stuart Graham. Such version precedes by one year the first release available at the USPTO website (https://www.uspto.gov/ip-policy/economic-research/research-datasets/patent-assignment-dataset). The version that we use covers 5,534,135 transactions recorded at the USPTO between January 1970 and January 2013 (inclusive). While the first transaction date is January 1970, the number of transactions recorded in the initial years is negligible. Data coverage seems sufficient for the years 1981-2012.

If you use the code or data, please cite the following two papers:

Arque-Castells, P., and Spulber, D. (2022). Measuring the Private and Social Returns to R&D: Unintended Spillovers versus Technology Markets. Journal of Political Economy. https://doi.org/10.1086/719908

Arqué Castells, Pere and Spulber, Daniel F., Firm Matching in the Market for Technology: Business Stealing and Business Creation (September 17, 2021). Northwestern Law & Econ Research Paper No. 18-14, Available at SSRN: https://ssrn.com/abstract=3041558 or http://dx.doi.org/10.2139/ssrn.3041558

Contains detailed information on 10.0 million patent assignments and other transactions recorded at the USPTO since 1970 and involving roughly 17.8 million patents and patent applications. It is derived from the recording of patent transfers by parties with the USPTO.

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

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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)

PatentCity is a dataset that provides information on each individual patents filed in the US patent office since 1836, on the UK patent office since 1894, on the French patent office since 1903 and on the German patent office (including East Germany) since 1877. Each entry is a patent publication along with standard information taken from patent offices (publication number, date of publication, technological classes…) which are enriched with additional details processed from the text of the patents. This includes: the name of each patentee (assignees or inventors), its geocoded address and when applicable its occupation and citizenship. PatentCity can be used in a variety of disciplines, geography, economics, history of science… and has been designed to be easily merged with existing geographical or technological sources. Github of the project: github.com/cverluise/patentcity Documentation: cverluise.github.io/patentcity Descriptive paper: www.longtermproductivity.com/perso/Patentcity_desc.pdf

Contains Artificial Intelligence Patent Landscape data classifying 13,244,037 granted patents and PGPubs published from 1976 through 2023 in eight AI component technologies using state-of-the art machine learning based models.

Graph and download economic data for U.S. Granted Patents: Design Patents Originating in the District of Columbia (PATENTUSDCDESIGN) from 1992 to 2020 about patent granted, patents, intellectual property, origination, DC, and USA.

A bulk data set consisting of XML-tagged titles, abstracts, descriptions, claims and search reports of European Patent (EP) publications, designed to facilitate natural language processing work.

The global commercial patent database market is experiencing robust growth, driven by the increasing need for intellectual property (IP) management and competitive intelligence among businesses. The market's expansion is fueled by several key factors, including rising R&D investments across various industries (pharmaceuticals, technology, etc.), a surge in patent filings worldwide, and the growing adoption of sophisticated analytical tools for patent data mining. This necessitates comprehensive and user-friendly databases that offer advanced search functionalities, allowing businesses to identify opportunities, track competitors, and protect their own innovations effectively. The market's segmentation reflects the diverse needs of users, encompassing solutions tailored to specific industries and IP management tasks. Leading players are continuously innovating, integrating AI and machine learning capabilities to enhance search precision and data analysis, creating more efficient and insightful platforms. The competitive landscape is characterized by a mix of established players and emerging technology companies, each striving for differentiation through superior user experience, data quality, and analytical features. We estimate the market size to be approximately $2.5 billion in 2025, growing at a compound annual growth rate (CAGR) of 12% between 2025 and 2033. This strong growth is projected to continue throughout the forecast period, primarily due to the ongoing digital transformation across sectors and the increasing reliance on data-driven decision-making. However, challenges remain, including the high cost of access to premium database features and the complex nature of patent data, requiring specialized expertise to interpret effectively. The market will see continued consolidation, with larger players acquiring smaller companies to expand their market reach and product offerings. Furthermore, the focus on user experience and the development of more intuitive interfaces will be critical to broaden the appeal of these databases to a wider range of users, from IP professionals to business strategists. Geographic expansion, particularly in emerging economies with growing R&D activities, will also be a key driver of market growth in the coming years.

This repository provides the Government Patent Register, a dataset of U.S. government interest patents sourced from administrative records, accompanying the following article: Gross, Daniel P. and Bhaven N. Sampat. 2024. “The Government Patent Register: A new resource for measuring U.S. government-funded patenting." NBER working paper no. 32136. Please sign up here if you'd like to be notified of future updates: https://forms.gle/3fuqUW7jPyLxySLF8.

Contains the full text of each patent application (non-provisional utility and plant) published weekly (Thursdays) from March 15, 2001 to present (excludes images/drawings). Subset of the Patent Application Full Text Data with Embedded TIFF Images.

Patent information of listed companies

Contains four research datasets containing time series and micro-level data by National Bureau of Economic Research (NBER) technology sub-category on applications, grants, and in-force patents spanning two centuries of innovation. For more information: https://www.uspto.gov/learning-and-resources/ip-policy/economic-research/research-datasets

Patent data is aggregated across multiple Intellectual Property (IP) registries, including USPTO, CIPO, EUIPO and WIPO (USA, Canada, Europe). Our complete dataset of active patent records is updated weekly. Customized reports available based on company lists, or full dataset via raw feed or one-off reports. Full bibliographic data provided for each IP record; including filing date, grant date, expiry date, inventor(s), IPC, full text abstract, title, etc. Ownership/entity relationship mapping, ticker mapping, ISIN mapping, Crunchbase uuid mapping, Crunchbase domain mapping. We also provide our proprietary IP Activity Score for each owner, which can assist to compare recent innovation activity amongst owners, as reflected in their Intellectual Property filings.

Ipqwery's Patent data is also available as a combined dataset with our Trademark dataset, enabling full IP profiles for corporate entities.

Fork this notebook to get started on accessing data in the BigQuery dataset by writing SQL queries using the BQhelper module.

Context

The Patent Claims Research Dataset contain detailed information on claims from U.S. patents granted between 1976 and 2014 and U.S. patent applications published between 2001 and 2014. The dataset is derived from the Patent Application Publication Full-Text and Patent Grant Full Text files, available at https://bulkdata.uspto.gov/, to which the Office of Chief Economist (OCE) applied a Python algorithm to identify individual claims as well as the dependency relationship between claims. From the parsed claims text, OCE created six data files containing individually-parsed claims, claim-level statistics, and document-level statistics, including newly-developed measures of patent scope.

Content

USPTO OCE Patent Claims Research data contains detailed information on claims from U.S. patents granted between 1976 and 2014 and U.S. patent applications published between 2001 and 2014.

Acknowledgements

"USPTO OCE Patent Claims Research Data" by the USPTO, for public use. Marco, Alan C. and Sarnoff, Joshua D. and deGrazia, Charles, "Patent Claims and Patent Scope" (October 2016). USPTO Economic Working Paper 2016-04.

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

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Innovation is the engine of long-term growth.

Moat provides structured patent data rolled up to an ultimate parent and mapped to ticker symbols. Patent ownership is time aware of asset transfers and corporate hierarchy changes. Data is mapped to actual markets and products (not a CPC schema). Patent data can also be combined with market, risk, and/or product data to quantify company and sector specific innovation behavior and trends.

Dataset creates queryable relationships among products, technologies, patents, entities, investment, risk, talent, and value.

Datasets can be used for such things as: - Enterprise Valuation - Validate or ascertain enterprise value through intangible asset aligned enterprise values. - Patent Valuation - Estimate of the dollar value of the cost to rebuild a patent portfolio - IP Risk and Litigation - Quantifies risks to each patent and patent portfolio through strength, validity, and litigation metrics. - Innovation Tracking and Analysis - Maps financial, product, and risk data to patents to facilitate comparative analysis and to reveal demonstrated innovation behavior. - Patent Lifecycle and Expiration - Data that estimates the lifecycle and expirations of technology areas and products protected by complex patent strategies.

Patent data is time-aware and 20 years of historical data is available. Data is updated daily. In depth usage examples can be provided on request.