March Updates

This month, PatentsView released the third quarter of 2021 data complete with the new algorithm and data structure updates initiated last fall. The release notes web page holds detailed information on this release and historical releases.

Also released this month are annualized gender data files with new documentation and an updated data dictionary from the Office of the Chief Economist (OCE) at the United States Patent and Trademark Office (USPTO). These datasets are designed for use in quick exploratory data analysis as well as read programmatically for more longitudinally focused data users. The annual files contain information from the assignee, inventor, location, application, and patent tables all in one place for a more comprehensive picture of patenting teams. In addition to pulling in variables from these separate PatentsView data tables, the datasets contain novel variables including the total number of inventors on a given patent, the total number of inventors listed on a given patent that were assigned a gender, the number of men inventors on each patent, the number of women inventors on each patent, and a flag for demonstrating whether inventor information is available for that patent.

To read more about these data files and the inspiration for their generation, visit the Gender & Innovation page and navigate to the DATA section located under the interactive visualization of gender data from 2000 to 2020.

Looking Ahead

The next PatentsView data update is gearing up this March and will result in a double release of 2021 quarter four and 2022 quarter one data come early-summer. The team is working with OCE this spring to improve and optimize the assignee disambiguation and gender attribution algorithms. The anticipated result of this dive into algorithm repair and improvement is higher quality data. As always, please reach out to our team with data questions and suggestions. Your exploration of the data and reporting of discrepancies and errors helps support our team to return the highest quality data to the public.

To receive regular updates on what the PatentsView team is working on in distributing patent data and reading about patenting literature, subscribe to our bi-monthly newsletter. Happy Spring!

In 1986, Patricia Bath filed for a medical patent for a novel method to remove eye cataracts. Bath was the first African American female physician to acquire a patent. Her patent has been referenced  over 100 times since its filing and has been cited as recently as February 2022 by Gregg Scheller and Matthew N. Zeid in their steerable laser probe patent.  

The patent data alone will not tell you that Bath was the first African American female physician to acquire a patent. The race of inventors, like gender, is not part of any data collected by the USPTO and would require attribution algorithms similar to the gender attribution currently conducted by the PatentsView team.  

Dig Deeper into Patent Data 

PatentsView provides an opportunity to look at women in innovation more broadly. With PatentsView’s bulk downloads data, you can now query the data to see counts and types of inventions by male and female inventors in the aggregate.  

Last Fall, PatentsView hosted a symposium on the attribution of demographic information to inventors listed on patents with the USPTO’s Office of the Chief Economist. This symposium included updates on predicting gender and race using artificial intelligence and machine learning approaches, as well as insights on economic implications of these predictions to innovation policy.  

These methods show how researchers can dig deeper into the data to reveal trends and opportunity gaps for inventors and entrepreneurs.  

Looking Toward the Future 

The breadth of PatentsView’s mission has evolved as the project matures. Beginning in 2012 with an endeavor to connect and show the work of unique inventors over time and place, the PatentsView project has expanded the scope of its connection and discernment efforts to the assignees, locations, attorneys, and gender of inventors involved in patenting the country’s latest innovations.  

With the pursuit of disambiguation algorithms becoming more advanced in what they can identify from publicly available information on inventors and their patents, there is a need to consider the methods and implications for this line of inquiry.  

For gender attribution, the algorithm assigns the likelihood of the inventor being “male” based on the person’s name and their location in the world. The other options for the inventor are “not male,” aka female in this dichotomous view of gender, and “unassignable,” meaning that the algorithm was not able to confidently assign male or not-male to the inventor.  

A similar method could be applied to the likelihood of an inventor being of a certain race, nationality, or ethnicity. There are a variety of algorithms available using numerous different methodologies and each has unique advantages and disadvantages in terms of accuracy, expense, and time.  

What do you think about the future of race attribution in innovation? Tell us in the forum. 

Social science research starts with a commitment to using our time and resources in addressing problems most affecting society and the human experience. One urgent and globally important area of social science research is renewable energy, specifically, understanding the rate of innovation and adoption in the sector.

At the American Institutes for Research (AIR), we have a team of data scientists that work on transforming, disambiguating, normalizing, and quality assuring all data on the patents granted in the United States. This unleashes opportunity for us to use the data in research and analysis. We chose to develop classification models that predict which patents are related to renewable energy and present our findings at the Conference for Women in Data Science and Statistics this year on 10/8/2022 in St. Louis, MI. We used the Cooperative Patent Classification (CPC) labeling system to find renewable energy patents and look at the most common words used in patent abstracts and titles for this type of patent, demonstrated in the word cloud below.

We built random forest, logistic regression, and naïve bayes machine-learning classification models on the granted Renewable Energy (RE) patents (CPC subclasses under Y02) to predict whether a given patent was RE-related or not. Our efforts focused on searching for the model construction method and parameter choices that optimized the F1-score for Class 1 (predicted as RE-related). Our best-performing model was a random forest classifier and a CountVectorizer (a program to break down sentences into countable parts) on patent abstracts to achieve an F1-score of almost .85 as shown in figure 2.

Figure 3 is the confusion matrix for the model described in Fig 2. This matrix shows where correct/incorrect predictions occur. For example, 21,622 patents were predicted to be RE but were not given the Y02 CPC classification.

Enabled by the PatentsView project developed at AIR under the supervision of the Office of the Chief Economist at the USPTO, patent data usage is paramount to holding the federal government accountable for investing and encouraging innovation in science and technology in the areas important to scientists and the public. While challenges to increased domestic and international adoption of solar, wind, and other innovations are interwoven and interdisciplinary, the rate of innovation in renewable energy sector is an important component to analyze and understand as we push to transition away from fossil-fueled power.

What’s new with PatentsView: Our Algorithm is getting better!

Over the last few months, PatentsView has been improving its disambiguation algorithms. These improvements give researchers, students, inventors, intellectual property enthusiasts, and anyone else with an interest in patent information more accurate data to work with.

What has changed?

Our algorithms have been updated to better represent patent trends by location and assignee. The updated algorithms increase accuracy in clustering — the grouping of raw information into similar organizations — and incorporate Open Street Mapping as an additional source. This results in better, more accurate data and analysis.

These changes apply to all PatentsView data, including bulk downloads, legacy and Elasticsearch APIs, query builder tool, and list searches.  

What are disambiguation algorithms?

PatentsView’s data visualizations and analysis rely on a series of algorithms and post-processing techniques to sort inventors and assignees by name and place. We need this process, known as disambiguation, because patent data is often incomplete or inconclusive.

For instance, the U.S. Patent and Trademark Office does not collect data on an inventor’s gender. So, PatentsView uses an algorithm to make an educated guess about gender based on an inventor’s name and location.

In other cases, one inventor may apply for multiple patents using different variations on their name, like John Smith, J. Smith, and Johnny P. Smith. Our algorithms help determine if these are all the same inventor or three different inventors.

Why is this important?

Innovations and inventions benefit all of society, and that benefit is increased when every inventor can fully participate in the process. Accurate analysis of patent data helps identify gaps, and thus provides a first step toward closing those gaps.

PatentsView’s goal is to provide the most accurate, up-to-date, and complete analysis of intellectual property data to foster better knowledge of the IP system and drive new insights into invention and innovation. Updates like this put us one step closer to that goal.

You can learn more about our methods and sources at https://patentsview.org.

At the end of August, PatentsView and the United States Patent and Trademark Office (USPTO) convened a group of 10 researchers, developers, and analysts to discuss the best and newest practices in identifying inventor demographics, including the use of gender and race. The event was structured as an all-day symposium with seven individual presentations and a three-person panel conversation on the social and economic implications of advancing our understanding of inventor demographics.

Recordings from each session of the symposium are now available online.

View the recordings now.

Why Study Inventor Demographics?

Quite simply, greater diversity and participation among inventors fuels more innovation and improves economic welfare. However, the current data suggest many people, particularly from diverse communities, are not represented among inventors. This means that challenges exist at various points along the pathway from inspiration, to inventor, to innovator and entrepreneur. Learning about who is and is not  participating as inventors and why is a critical step toward ensuring that all people can contribute equally to the innovation landscape and improve economic outcomes at both a personal and societal level.

Unfortunately, the research into identifying and addressing such gaps is still in its infancy. One of the largest barriers is the lack of information about the demographics of inventors who apply for and ultimately receive patents.

Disambiguation Methods and Practices

Researchers have been using several methods and practices to shed light on inventor demographics when that information is not self-reported — the main processes are disambiguation and attribution.

Each of these different processes has its own strengths and weaknesses. Speakers at the Advancing Research on Inventor Demographics Symposium, held on August 26, 2022, discussed the societal consequences of inequities in innovation. They also provided an in-depth discussion of the methods used to identify an inventor’s gender, race, ethnicity, and country of origin when that information is not reported in publicly available datasets.

Leading Experts Discuss Cutting-Edge Research

The symposium brought together economists, computer scientists, and others to discuss research methods, applied examples, and new ideas. The experts included:

• Julio Raffo, a researcher in the Economics and Statistics Division at the World Intellectual Property Organization, who discussed gender attribution and the World Gender Name Dictionary 2.0.
• Ernest Miguelez, a research fellow at the French National Centre for Scientific Research (CNRS), who discussed the PatentsView approach to inventor gender.
• Michelle Saksena, a senior research economist at USPTO, who discussed assessing approaches for identifying the gender of inventors on patents.
• Fangzhou Xie, a Ph.D. student at the Department of Economics at Rutgers University, who discussed using an R package he developed, called rethnicity, to predict ethnicity from names.
• Francesco Lissoni, a professor of economics at the Bordeaux School of Economics within the University of Bordeaux, who discussed ways to determine inventors’ foreign-origin status using name analysis.
• Jay Budzick, the CTO of Zest AI, who discussed using the Zest Race Predictor to uncover hidden disparities.
• Marc Elliot, Distinguished Chair in Statistics and senior principal researcher at the RAND Corporation, who discussed methods to estimate race and ethnicity, and associated disparities, when records do not include self-reported data.
• Trevon Logan, a Distinguished Professor of Economics at Ohio State University; Adam Gailey, a principal in the Financial Economics Practice of Charles River Associates; and Jason Dietrich, section chief for compliance and analytics policy at the Consumer Financial Protection Bureau, who participated in a panel discussion about gaps in innovation by race/ethnicity and gender.