COVID-19 disrupted all our lives, but it also opened the door to innovation. The speed at which researchers, companies, and universities developed new tests, treatments, and vaccines was unprecedented. A new report from the U.S. Patent and Trademark Office (USPTO) Office of the Chief Economist found that much of the innovation around diagnosing COVID-19 was led by universities and small companies.

Diagnosing COVID-19: A perspective from U.S. patenting activity looks at patents filed that relate to diagnosing the COVID-19 as it emerged and spread. It contributes to a growing body of research that examines how innovation responds to crisis. The report was released on October 23.

The research team used PatentsView to identify patents that include a government interest statement. This helped them to identify which patents were funded by government agencies.

Key Findings from the Report

The report looked at patents filed or issued through April 2023 that helped identify and detect the SARS-CoV-2 virus. Some of the key findings in the report included:

• The number of COVID-19 diagnostic patent filings that were published by the USPTO surged and then receded in the months following the emergence of the coronavirus—such publications peaked in the fourth quarter of 2021, which generally reflects applications filed in April, May, and June of 2020.

• COVID-19 diagnostic filings make up about 30% of all COVID-19 public patent filings, hovering at about one out of every three COVID-19 filings at the USPTO.

• Small companies and universities led the way in COVID-19 diagnostic public patent filings at the USPTO with the top filer being a diagnostic startup company.

• U.S. government financial support helped spur COVID-19 diagnostic inventions, as indicated by government interest statements contained in the filings. About 10.7% of all COVID-19 public patent filings show government support, with the National Institutes of Health leading other agencies.

• U.S.-based applicants are leading those from other countries in U.S. COVID-19 diagnostic public patent filings, making up most of the volume, including most of the top 21 applicants.

• COVID-19 diagnostic public patent filings are concentrated in a few technologies such as analyzing materials and measuring enzymes, nucleic acids, and microorganisms.

• Many applications for inventions directed at COVID-19 diagnostics also disclose methods of treatment (about 8.6%). For instance, inventions for antibodies may diagnose and treat COVID-19.

• Among 5,585 global COVID-19 diagnostic patent families found in the study, 47% have at least one filing at the China National Intellectual Property Administration (CNIPA), the most of any jurisdiction.

Read the Full Report

The full report is available on the USPTO website.

by Pablo Galaso and Sergio Palomeque

Patent data have been extensively used in academic research in several knowledge areas. This kind of data is particularly useful for studying knowledge flows and factors affecting innovation. In this sense, detailed information on inventions registered by patents and the possibility of studying the interactions between agents are among their main advantages.

In Latin America, academic research using this type of data is limited compared to other parts of the world, due to the absence of an international office that allows comparability between countries by unifying the patent regulatory framework. In addition, historically, patent offices worldwide have ensured the identification of each registration but, for various reasons, not unique identifiers to the actors involved.

PatentsView Helps Fill in Gaps in Patents Data for Researchers

To address these difficulties, researchers at the Institute of Economics of the UdelaR have used the data provided by PatentsView to carry out academic research since 2017. This research contributes to understanding the characteristics and limitations of innovation systems in Latin America, at regional, national and subnational levels (https://spwebfcea.wixsite.com/inventioninla).

The relevance of obtaining intellectual property protection in the United States for frontier innovations makes the USPTO records useful for analysing innovation processes in different regions of the world, particularly in Latin America, where there is no international agency. The use of USPTO data allows an adequate comparison of inventive activities between countries, avoiding problems associated with the institutional differences among national patent offices.

On the other hand, the process of systematising information and disambiguating actors carried out by PatentsView allows the use of patent data on a much larger scale than in the past.

In-depth Learning and Applications

To support and disseminate the use of this data, we have conducted a series of activities, including:

A webinar that sought to:

• Introduce participants to the advantages of using USPTO patent data for research on collaboration networks in cities and regions of developing countries, especially in Latin America. The webinar presented the main features of this data source, the advantages of accessing it through the PatentsView platform and some examples of research articles using this data.

A workshop where:

• The participants learned more in-depth about the applications and methods that use this data and practiced how to use R for processing and analysis of collaboration networks.

These activities were carried out within the Regional Studies Association Research Network on Knowledge, Innovation and Regional Development in South America (KIRDSA).

Further Reading

Below is a list of papers we have published in this line of research, which may provide a better idea of the possibilities for Latin America and other world regions.

• Bianchi, C., Galaso, P., & Palomeque, S. (forthcoming). Absorptive capacities and external openness in underdeveloped Innovation Systems: A patent network analysis for Latin American countries 1970-2017. Cambridge Journal of Economics. https://doi.org/https://doi.org/10.1093/cje/bead034
• Bianchi, C., Galaso, P., & Palomeque, S. (2023). Knowledge complexity and brokerage in inter-city networks. The Journal of Technology Transfer. https://doi.org/10.1007/s10961-023-10025-x
• Bianchi, C., Galaso, P., & Palomeque, S. (2023). The trade-offs of brokerage in inter-city innovation networks. Regional Studies, 57(2), 225–238. https://doi.org/10.1080/00343404.2021.1973664
• Bianchi, C., Galaso, P., & Palomeque, S. (2021). Patent Collaboration Networks in Latin America: Extra-regional Orientation and Core-Periphery Structure. Journal of Scientometric Research, 10(1s), s59–s70. https://doi.org/10.5530/jscires.10.1s.22
• Bianchi, C., Galaso, P., & Palomeque, S. (2020). Invention and Collaboration Networks in Latin America: Evidence from Patent Data (DT 04/2020). Serie Documentos de Trabajo. Montevideo.

Inter-city collaboration network in Latin America

By Siddharth Engineer

A skill relatedness network is an interconnected system which shows similarities between industries.

Imagine there are many employees who transition from industry A to industry B. This would suggest that the two industries require similar skillsets. A skill-relatedness network provides a broad view of such labor flows to better understand the similarities between fields.

This can be valuable information to economists, firms seeking to leverage human capital, and people seeking employment opportunities. Let us look at one example a little bit more closely. Labor mobility, referring to a worker’s ability to move between jobs and industries, is critical in the personal/financial growth of workers. This can lead to reductions in poverty and an overall stronger economy.

Transportation Limits Worker Mobility in Columbia

In Colombia, an analysis of transportation systems revealed that commute times were significantly limiting the ability of firms to make use of a diverse pool of skills.

When employers in similar industries are grouped geographically, this limits labor mobility because workers with limited transportation options cannot move between industries. Instead, we can map skill-relatedness networks to geographic regions to capture the employment opportunities that sector classifications would otherwise overlook (O’Clery et al. 2019).

Below: an example skill relatedness network for labor markets

Looking at Skill Relatedness Networks Differently

More recently, we have been able to apply skill-relatedness networks to innovation. Let us adapt our prior definition of skill-relatedness. Instead of focusing on employees who change work, let us look at inventors who change fields. At the end of the day, both employment and patents are applications of an individual’s skill. By identifying inventors with patents in multiple fields, we can get a better picture of the human capital available for innovation specifically.

Using PatentsView's disambiguated inventor data, we can mathematically define this new skill-relatedness network. Imagine transition matrices (F) between technologies of dimensions N x N where N represents the total number of technologies. Each element F_i,j = 1 if an inventor transitioned from technology i to technology j.

A Case Study

Sergio Palomeque constructed a skill-relatedness network by aggregating these matrices, comparing it to a null model, and normalizing the data. The results revealed that the diameter of the network has decreased over time, particularly in the last 10 years.

A decreasing diameter indicates more links between existing technologies than new ones are being introduced. While the reasons for this trend are still unclear, further research in skill-related networks could offer valuable insights into innovation, as demonstrated in the context of transportation in Colombia.

June Updates 

This month in PatentsView news, the data team will release quarter four data for 2022 and the quarter one data for 2023. The disambiguated and processed data will include patents and published pre-grant patent applications from September 30, 2022, to March 30, 2023. In addition to bulk downloadable data for granted patents and pre-grant application publications, the legacy MySQL API, Elasticsearch beta API, and site visualizations will also be updated with data through March 30, 2023. To celebrate the completion of processing for the year 2022, we're lighting sparklers just in time for the independence and Emancipation Day celebrations in the United States!

In our previous data updates, PatentsView gender data was attributed through a partnership with faculty at the University of Bordeaux. Starting from the final quarter of 2022 up to the present, our PatentsView data scientists have attributed gender to inventors using World Intellectual Property’s (WIPO’s) Genderit Method algorithm, which has been adjusted by our team. The new attribution method has been applied to all historic records and assigned to disambiguated inventors based on the majority gender of raw inventor records that combine to make the disambiguated inventor. For instance, if over 50% of raw records for a given inventor are marked female, then the inventor is attributed as female. In cases where exactly 50% of raw inventor records are marked as both female and male (which did occur), the gender remains unattributed.

PatentsView has brought the inventor gender algorithm in house starting with the next data release. We aim to simplify processes and improve the timeliness of the data releases while maintaining data quality. Our new method outperforms the old method in terms of attribution rate based on a comparison of a sample week of quarter of data by 4%. In summary, the inclusion of gender attribution in the PatentsView internal data pipeline will ultimately result in faster and more accurate gender information for researchers, economists, students, inventors, and other users.

Looking Ahead

In pursuit of a faster and more efficient data processing pipeline that does not deter the current quality of PatentsView data, the data team also invested in weekly parsing of the raw XML data files from the United States Patent and Trademark Office (USPTO). Incremental conversion of the XML data into tsv format allows the data team to catch errors in the process before they lead to data quality issues or impede the disambiguation and attribution data processes further along the pipeline.

Here's to diving into 2022 annual data and beginning our exploration with 2023!