Graph and download economic data for U.S. Granted Patents: Total Patents Originating in the United States (PATENTUSALLTOTAL) from 1992 to 2020 about patent granted, intellectual property, origination, patents, and USA.

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

This tab-delimited file, assignees2015_5yr.txt, prepared from the U.S. Patent and Trademark Office (USPTO), Technology Assessment and Forecast (TAF) database, displays a listing of organizations receiving the most utility patents (i.e., patents for invention) during the indicated 5 year time period. Each displayed annual count corresponds to the count of patents received in a calendar year (January 1 to December 31). It will import into a Microsoft Excel spreadsheet. This file generally contains the contents of the PTMT report, DRILL-DOWN Utility Patent Report, Patenting by Geographic Origin (State and Country) - Breakout By Organization, available on the USPTO Web Site at: https://www.uspto.gov/web/offices/ac/ido/oeip/taf/stcasg/regions_stcorg.htm

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.

Banner photo by Helloquence on Unsplash

PATCIT: A Comprehensive Dataset of Patent Citations [Website, Newsletter, GitHub]

Patents are at the crossroads of many innovation nodes: science, industry, products, competition, etc. Such interactions can be identified through citations in a broad sense.

It is now common to use front-page patent citations to study some aspects of the innovation system. However, there is much more buried in the Non Patent Literature (NPL) citations and in the patent text itself.

Good news: Natural Language Processing (NLP) tools now enable social scientists to excavate and structure this long hidden information. That's the purpose of this project

IN PRACTICE

A detailed presentation of the current state of the project is available in our March 2020 presentation.

So far, we have:

1. classified the 40 million NPL citations reported in the DOCDB database in 9 distinct research oriented classes with a 90% accuracy rate.

2. parsed and consolidated the 27 million NPL citations classified as bibliographical references.

3. extracted, parsed and consolidated in-text bibliographical references and patent citations from the body of all time USPTO patents.

The latest version of the dataset is the v0.15. It is made of the v0.1 of the US contextual citations dataset and v0.2 of the front-page NPL citations dataset.

Give it a try! The dataset is publicly available on Google Cloud BigQuery, just click here.

FEATURES

Open

• The code is licensed under MIT-2 and the dataset is licensed under CC4. Two highly permissive licenses.
• The project is thought to be dynamically improved by and for the community. Anyone should feel free to open discussions, raise issues, request features and contribute to the project.

Comprehensive

• We address worldwide patents, as long as the data is available.
• We address all classes of citations, not only bibliographical references.
• We address front-page and in-text citations.

Highest standards

• We use and implement state-of-the art machine learning solutions.
• We take great care to implement only the most efficient solutions. We believe that computational resources should be used sparsely, for both environmental sustainability and long term financial sustainability of the project.

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

Context

The Office of the Chief Economist (OCE) is responsible for advising the Under Secretary of Commerce for Intellectual Property and Director of the USPTO on the economic implications of policies and programs affecting the U.S. intellectual property (IP) system. The office disseminates detailed patent and trademark data, undertakes research, and conducts economic analysis on a variety of IP issues. OCE works with policy makers, collaborates with academics, and engages the public more generally through conferences it organizes, the publicly accessible research datasets it provides, and its publications.

Content

The USPTO OCE Patent Assignment Dataset contains detailed data patent assignments and other transactions recorded at the USPTO since 1970.

Acknowledgements

"USPTO OCE Patent Assignment Data" by the USPTO, for public use. Marco, Alan C., Graham, Stuart J.H., Myers, Amanda F., D'Agostino, Paul A and Apple, Kirsten, "The USPTO Patent Assignment Dataset: Descriptions and Analysis" (July 27, 2015).

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

Banner photo by Jeff Sheldon on Unsplash

Graph and download economic data for U.S. Granted Patents: Total Patents Originating in Florida (PATENTUSFLTOTAL) from 1992 to 2020 about patent granted, intellectual property, origination, patents, FL, and USA.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Holland Patent. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Holland Patent. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Holland Patent, the median household income stands at $121,016 for householders within the 45 to 64 years age group, followed by $88,750 for the 25 to 44 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $74,375.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Holland Patent median household income by age. You can refer the same here

Reactions extracted by text-mining from United States patents published between 1976 and September 2016. The reactions are available as CML or reaction SMILES. Note that the reactions SMILES are derived from the CML. The files can be unzipped using a program like 7-Zip.The reactions were extracted using an enhanced version of the reaction extraction code described in https://www.repository.cam.ac.uk/handle/1810/244727with LeadMine (https://www.nextmovesoftware.com/leadmine.html) used for chemical entity recognition.General tips:Duplicate reactions are frequent due to the same or highly similar text occurring in multiple patents, this is especially true when combining the applications and grant datasets, many reactions from applications will later appear in patent grants.Paragraph numbers are only present for 2005+ patent grants and patent applications.Multiple reactions can be extracted from the same paragraph.Atom maps in the reactions SMILES are derived using Epam's Indigo toolkit. While typically correct, the atom-maps are wrong in many cases and hence should not be entirely relied on.The reactions have been filtered to remove common cases of incorrectly extracted reactions:All product atoms must be accounted for by the atom-mappingThe product(s) must have >8 heavy atomsThe product must not be charged if it is a single componentThe number of products must be

These are the data for two published articles, “Drug patenting in India: looking back and looking forward” (Nature Reviews Drug Discovery [NRDD], 2015) and "TRIPS implementation and secondary pharmaceutical patenting in Brazil and India" (Studies in Comparative International Development [SCID], 2015). The NRDD dataset includes the new drug application number, drug approval year, drug name (where available), and patent priority year (based on first patent) for new molecular entities approved by the U.S. FDA between 1995 and 2013. The SCID datasets include the patent application number, active ingredient, category (primary or secondary patent application), and the country specific outcome for each application. There are two files, one for Indian applications (scid_indianapplications.csv) and one for the Brazilian applications (scid_brazilianapplications.csv).This project analyses developing country strategies to limit the issuance of secondary patents in pharmaceuticals. Secondary patents can extend periods of exclusivity, delay generic entry, and contribute to high drug prices and reduced access to medicines. Accordingly, several developing countries have adopted policies to restrict the grants of such patents; such measures are widely promoted by analysts and international organisations alike. However, little is known about which countries are adopting what sorts of measures, and to what effect. The project develops a typology of approaches toward secondary patenting, and maps countries according to the typology. Empirical analyses focus on five countries with specific measures to limit secondary patenting and two without. The research involves assembling novel datasets of pharmaceutical patents filed in the seven countries, coding each application as primary or secondary, and examining outcomes for each application in each country. The analysis will not only generate cross-national data on grant rates for different types of drug applications, but also improve understanding of the effectiveness of different approaches to secondary patents. Quantitative analyses are complemented with case studies to reveal the social and political factors conditioning how patent policies work in practice. For the NRDD article, we explain data collection and construction in the supplementary information included with the article (see Related Resources). For the SCID article, we explain the data collection in detail on pages 237-240 of the article.

Amazon's patent filings grew for most of the 2000s. With the start of the following decade the number of filed patent families increased noticeably. Growth was especially visible in 2010 and 2012, when the company crossed 500 and 1,000 filings for the first time, respectively. The number of filings reached its peak in 2014 and has been declining since then. The timeline is based on data provided by PatentSight. Amazon.com, an American electronic commerce company originally founded in 1994, is the world’s largest online retailer of books, clothing, electronics, music and many more goods.

About this dataset

Context

The dataset tabulates the Holland Patent population by age. The dataset can be utilized to understand the age distribution and demographics of Holland Patent.

Content

The dataset constitues the following three datasets

• Holland Patent, NY Age Group Population Dataset: A complete breakdown of Holland Patent age demographics from 0 to 85 years, distributed across 18 age groups
• Holland Patent, NY Age Cohorts Dataset: Children, Working Adults, and Seniors in Holland Patent - Population and Percentage Analysis
• Holland Patent, NY Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Contains Cooperative Patent Classification (CPC) classification information for all Utility patent grants issued by the U.S. Patent and Trademark Office (USPTO) from 1790 to present. It is available as XML with schemas or text monthly (usually by the 15th of the month).

This tab-delimited file, stc2015_5yr.txt, prepared from the U.S. Patent and Trademark Office (USPTO), Technology Assessment and Forecast (TAF) database, displays a listing of geographic regions from which utility patents (i.e., patents for invention) originated during the indicated 5 year time period and a corresponding count of patents for each of the years of the period. Each displayed annual count corresponds to the count of patents received in a calendar year (January 1 to December 31). It will import into a Microsoft Excel spreadsheet. This file generally contains the contents of the PTMT report, Patenting In Technology Classes Breakout By Geographic Origin (State and Country), CLASS ALL, ALL CLASSES, available on the USPTO Web Site at: https://www.uspto.gov/web/offices/ac/ido/oeip/taf/tecstc/allclstc_gd.htm

Pat J. Federico has compiled a data collection that includes historical data on patent statistics for 44 states. He draws on official publications of each country. The data-tables begin in 1791 with the patent statistics of France, Great Britain, the Netherlands and the United States. From 1812 then the total numbers of all in the German States granted patents are added to the date-tables of the other nations. From 1877 the information source of the German patent statistics is the German Imperial Patent Office (Deutsches Kaiserliches Patentamt).

Datatables in the search- and downloadsystem HISTAT (Subject: Innovation):

A.01 Amount of granted patents by countries (1791 – 1900) A.02 Amount of patent application and granted patents, amount of domestic applicants by countries (1901-1950) A.03 Amount of patent applications by countries (1951 – 1961) B. Amount of patent applications and granted patents by provenance of the patent owners (1951-1961)

Graph and download economic data for U.S. Granted Patents: Utility Patents Originating in the United States (PATENTUSALLUTILITY) from 1992 to 2020 about patent granted, intellectual property, origination, patents, and USA.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Holland Patent: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 4(3.08%) households where the householder is under 25 years old, 14(10.77%) households with a householder aged between 25 and 44 years, 56(43.08%) households with a householder aged between 45 and 64 years, and 56(43.08%) households where the householder is over 65 years old.
• In Holland Patent, the age group of 45 to 64 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Holland Patent median household income by age. You can refer the same here

Using a Bayesian supervised learning approach, we identify individual inventors from the U.S. utility patent database, from 1975 to the present. An interface to calculate and illustrate patent co-authorship networks and social network measures is also provided. The network representation does not require bounding the social network beforehand. We provide descriptive statistics of individual and collaborative vari ables and illustrate examples of networks for an individual, an organization, a technology, and a region. The paper provides an overview of the technical algorithms and pointers to the data, code, and documentation, with the hope of further open development by the research community. Go here for theNBER pdpass file -- https://sites.google.com/site/patentdataproject/Home/downloads. It's old and hasn't been updated

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Holland Patent, NY, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 23,844, while the mean income for the highest quintile (20% of households with the highest income) is 198,011. This indicates that the top earners earn 8 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 288,687, which is 145.79% higher compared to the highest quintile, and 1210.73% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Holland Patent median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Holland Patent by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Holland Patent across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 63.25% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Holland Patent is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Holland Patent total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Holland Patent Population by Gender. You can refer the same here