In the world of software startups, patents are often seen as a golden ticket to securing investment. But do they really hold that much sway? A recent study by James Hicks looks into this question, focusing on the impact of a landmark Supreme Court decision, Alice Corp. v. CLS Bank International, on software patents and investments.

The study found that patents on certain types of software did not necessarily increase investment and other positive business outcomes. The findings encourage us to look more closely at when patents are needed to protect intellectual property.

What is the Alice decision?

The Alice decision changed the criteria for software inventions to be eligible for patents. The case involved an electronic service that implemented financial trading systems. The Alice ruling stated that the service was based on an abstract idea, and therefore was not patentable. In practice, businesses applied this principle to any software involving business methods, which led to a dramatic drop in investment in this type of patent.

Despite this, Hicks' study found no evidence that this drop in the number of patents led to less early-stage venture capital investment or affected outcomes such as acquisitions and initial public offerings (IPOs).

The results indicated that patents may not be necessary for investment in the software industry. While patents are needed to protect certain types of intellectual property, these findings suggest that we may need to take another look at the value of patents in other areas.

PatentsView Data: Unraveling the Relationship

The study used data from PatentsView to identify companies that applied for patents and compare the number of patents with investment in the same company. This helped researchers understand the impact of the Alice decision on patent approval and subsequent investment in business-methods software firms. The results indicate that patents did not have a discernible effect on funding or successful outcomes in the software industry.

Reevaluating the Role of Patents in Software Innovation

The relationship between patents and investment in innovation is complex. This study suggests that the costs of obtaining patents may not always justify the benefits and invites more research into understanding when patents add value to a business. PatentsView data can help future researchers highlight connections and provide insight that will help inventors and businesses make better decisions about their intellectual property.

Disclaimer: This post was written with the assistance of artificial intelligence.

Artificial Intelligence (AI) is making waves in every corner of the world, and women are playing a significant role in this revolution, especially in the field of biotechnology AI, according to a recent study published in Nature. The study, titled Discovering value: women’s participation in university and commercial AI invention, used PatentsView data to identify the gender of inventors before using World Intellectual Property Organization categories to determine which patents contain AI. 

It was written by Alexander Giczy, Nicholas Pairolero, and Andrew Toole.

More Women are Participating in AI Innovation

The study found that women are making significant contributions to AI innovation, but there is still work to be done to ensure that people from many different backgrounds are participating in the development of AI.

The authors analyzed the women as inventors rate (WIR), which is a measure of the number and share of people receiving patents who are women. They found that the overall number of women inventors who received AI-related patents has increased from around 500 per year in 1995 to around 9,000 in 2020. The WIR for AI inventions increased from 10.1% to 12.6% in the same time frame.

“These results show that women are not only participating in AI invention but are doing so at a slightly higher rate than in non-AI technologies,” the authors said in the study.

They found differences in the AI WIR based on sector (e.g., AI and biotechnology), and whether the inventors worked for companies or universities.

The Value of Women Inventors

The authors noted research showing that diverse teams are more likely to improve innovation and business performance by looking at problems from different perspectives. Testing that theory, they found that teams with a higher proportion of women tended to be associated with higher economic value of granted patents. 

“While such an analysis does not imply causation, the value of the patent is higher for teams with relatively more women: a patent with an equal number of male and female inventors has a value approximately $1.038 million higher than a patent with all-male inventors,” the authors said in the study.

How PatentsView Can Help

This study contributes to a growing body of research that shows how important diversity is for innovation and emerging technologies. To thrive and grow in the future, everyone must be able to participate in innovation and patenting, regardless of their background. Tools like PatentsView can help us track our progress and identify areas where we can improve.

You can explore more on this topic on PatentsView’s Gender & Innovation and AI & Innovation pages.

PatentsView provides researchers, inventors, and others with easy-to-use patent data and award-winning visualizations. This data helps people to discover trends, identify gaps, and recommend policy changes to improve patent and intellectual property systems. As the platform’s popularity grows, people are finding new ways to manipulate and analyze PatentsView data to achieve their goals.

For instance, author Raj Bridgelall used PatentsView data to conduct a systematic patent review (SPR) to analyze how innovation was advancing in the field of transportation in a recent Future Transportation article titled “A Systematic Patent Review of Connected Vehicle Technology Trends.” Specifically, the review found that patents related to vehicle deployments were focused in the areas of improving safety and secure wireless communications.

What is SPR?

SPR is a methodological framework that Bridgelall adapted from the systematic literature review (SLR) method. In the paper, he says that “SPR offers detailed insights into both the thematic and temporal trajectories of innovation in any technology field.”

The SPR borrows from the SLR framework in its method of collecting data, selecting relevant information for analysis, and analyzing and interpreting the data with key themes and a focused objective in mind. However, where SLR typically centers qualitative methods to analyze titles and abstracts, SPR also incorporates a quantitative approach that relies on how frequently specific terms are used.

Bridgelall used PatentsView data, among other sources, to identify 220 U.S. patents from 2018-2022 related to automotive technology. His review separated them into categories, such as computing resources, cyber security, and driving safety. He found that patents are increasingly focused on driving safety and wireless communications, which he said, “aligns with broader goals of enhancing safety and situational awareness in transportation.”

The Benefits of SPR

In the paper, Bridgelall writes that most studies related to innovation in automotive technology are focused on technological aspects of the work and practical applications. His review provides a broader analysis that he says will help researchers identify gaps in the existing research and pinpoint areas for potential future innovation.

This research can also help policymakers understand where changes in policy and standardization might have an impact on the field. For instance, Bridgelall highlighted a 2020 move by the U.S. Federal Communications Commission that repurposed a large portion of a safety band dedicated to vehicle use. Bridgelall said that doing so caused uncertainty and stalled investments in connected vehicle technology, which has “the potential to reduce accidents, optimize traffic flow, and enhance the driving experience by communicating with each other (V2V) and with everything else (V2X).”

He hopes that his introduction of the SPR methodology will lay the groundwork for future research by himself and others to expand upon his analysis and identify international and long-term trends.

You can download the full paper on mdpi.com using the link above.

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.

The U.S. Patents and Trademarks Office receives thousands of patent applications every year. Often, the same inventor will apply for multiple patents. Other times, multiple inventors with similar names will each apply for a patent.  

The issue researchers and innovation enthusiasts have run into is that, when analyzing patent data, there is no standard way to tell whether an inventor named on multiple patents is the same person or different people with a similar name. 

PatentsView uses algorithms to make that determination, a process known as entity resolution or disambiguation. The process is not perfect, and the PatentsView team is constantly working to make the algorithm more accurate.  

The first step in any improvement process is to evaluate how well the current system works. Olivier Binette, a PhD candidate in Statistical Science at Duke University, explored this question in his publication Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org.  

Challenges for the PatentsView algorithm 

Binette notes in his paper that the PatentsView entity resolution algorithm faces three main challenges in accurately determining whether the names on multiple patent applications belong to one or more than one inventor. 

First, when researchers apply the PatentsView algorithm to benchmark datasets — smaller subsets of larger datasets that are used to train and test algorithms — the results tend to be more accurate then when the algorithm is applied to the larger, real-world data. This is likely because many of the false links between inventors with similar names do not appear in the benchmark dataset. 

Second, the number of patents that share a common inventor is relatively small compared to the larger number of patents. This creates a challenge for training the PatentsView algorithm to classify pairs of records as either sharing an inventor or not sharing an inventor. 

Finally, there are many different methods researchers have used to sample the benchmark data sets and adjust their estimates according to those samples. This creates an additional challenge in training the PatentsView algorithm. 

Binette’s method 

Binette argues that his method for estimating the performance of the PatentsView algorithm addresses all three challenges.  

His method uses three different representations of precision and recall. Precision is the fraction of pairs that are put into the same group for analysis and recall is the fraction of pairs that are correctly identified. So, an algorithm with high precision would correctly identify two similar names and put them together for analysis most of the time. An algorithm with high recall would, most of the time, correctly identify which of those similar names belonged to the same inventor. 

He tested each representation using PatentsView’s current disambiguated inventor data. For the test, he treated that data as the ground truth, then randomly added in errors before calculating precision and recall.  

He repeated the process 100 times. Then, he performed additional tests on two existing benchmark datasets and a disambiguation set done by hand. 

Using this method, Binette found that the PatentsView’s inventor disambiguation algorithm had a precision between 79%-91% and a recall between 91%-95%, which is much lower than the 100% found by previous testing on benchmark datasets. This shows that PatentsView’s current entity resolution algorithm over-estimates matching pairs.  

Future uses 

Binette’s evaluation method gives PatentsView a way to reliably analyze the effectiveness of changes made to the entity resolution algorithm in the future. Dive deeper into Binette’s method and review his code on his PatentsView Evaluation page on Github. 

As climate events and changes increase globally, how could this affect innovation and patenting of intellectual property? Luis Ballesteros of the Questrom School of Business at Boston University explored this question with his research on Hurricane Katrina published in late 2021.

A different perspective

While there are geographical studies of innovation and patents that focus on social features like how close a person is to human and material resources and institutions, Ballesteros is interested in a different perspective – what he calls, “exposure to large shocks.” Ballesteros used PatentsView’s disambiguated inventor and location data to write Can natural disasters affect innovation? Evidence from Hurricane Katrina. The publication describes the effects of natural disasters on patents and patenting.

How Hurricane Katrina affected innovation

Evidence suggests that large societal shocks produce lasting variations in human risk-aversion behaviors. Based on that evidence, Ballesteros proposes that Hurricane Katrina in the U.S. would have changed innovation outcomes.

More specifically, Ballesteros and supporting literature suggest that after an immediate shock, affected counties have much more patenting activity and the quality of innovation increases compared to non-exposed counties. This correlation has been shown to persist for roughly 10 years after the initial shock.

Methodology

Ballesteros’ methods involved constructing a history of inventors between 1999 and 2015 that allowed him to follow the “Katrina effect” across geographies. The estimates he found imply that shock-affected people were not only more likely to patent, but became more skewed toward high-technology sectors.

Ballesteros controlled for natural variation versus shock-related variance in several ways, which he illustrated in section four, Empirical Strategy, of the publication. In section three, Data, Ballesteros provides insights on the challenges and nuance of working with patent data, including the consideration for average processing time between application date and granting of a patent (which was reported as 23 months on average by USPTO in 2021) and how this relates to conducting longitudinal research with patent data.

Read the full publication to learn more about Ballesteros’ methods and insights on working with patent and PatentsView data.

How are you using PatentsView data?

If you have used PatentsView data in your own research, organization, or classroom and would like to be highlighted in a Data-in-Action spotlight piece, please visit our service desk.

Citation for Luis Ballesteros work: Ballesteros, Luis, Can natural disasters affect innovation? Evidence from Hurricane Katrina (December 13, 2021). Available at SSRN: https://ssrn.com/abstract=3980107 or http://dx.doi.org/10.2139/ssrn.3980107

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.

Throughout recent decades, the role of artificial intelligence (AI) in the modern world and the lives of its inhabitants has increased drastically. From advancements in cybersecurity and military technologies to AI-driven greenhouses and algorithms that help health-care workers develop better treatments, AI has made its way into nearly every sector of society.

The future of AI innovation and its influence on society will only continue to grow. It is with this in mind that the U.S. Patent and Trademark Office (USPTO), Office of the Chief Economist released a new report, titled Inventing AI: Tracing the diffusion of artificial intelligence with U.S. patents. This report details research conducted by USPTO in which a machine-learning AI algorithm was used to determine the volume, nature, and evolution of AI and its component technologies as contained in U.S. patents from 1976 through 2018.

A main goal of USPTO’s research was to measure the technology diffusion of AI with patent data. Technology diffusion is the process by which a technology is adopted by inventors, organizations, and other innovators as it spreads across different markets. In this report, USPTO details the methods it developed to identify the scope of such diffusion as it relates to AI and its component technologies.

Patent data are extremely useful for such an analysis as they can give direct insight into the spread and adaptation of a technology or method. When a new, powerful innovation or technology such as AI is created, the speed at which it is adopted by inventors and organizations alike can partially be seen by the increase in patent applications filed and granted with reference to said technology. Figure 1 shows the growth of AI-related patents as a percentage of all U.S. patents by year. In the figure we can see a dramatic increase in AI patents, from less than 5% in 1980 to greater than 20% in 2018—a truly staggering growth in just under 40 years.

Figure 1. U.S. Inventor and Owner of AI-Related Patents: Percentages From 1975 to 2020

In addition to the number of patents filed, patent data are useful in this research because each patent document contains detailed information and metadata. The person or organization that filed the patent, the technological classification of said patent, the location the patent was filed in, and so forth can all be found in one document.

The role of AI moving forward will be determined by the willingness and ability of inventors to continue working with and innovating on the technologies of today. Although we cannot know for sure just how much of an impact AI will have on our future, research into the scope and diffusion of its technologies can give us a glimpse of what is to come.

More information on the diffusion of AI as well as the AI method used to identify AI patents is available in the USPTO report.