We Need an Open-Source Approach to Weed Out Bad Quality Patents
“The open-source approach is the only way to ensure the creation of world-class patent tools that are assistive to innovators, examiners and other patent professionals alike, leading to only truly promising patents being granted.”
Much has been written about patent quality. But many authors approach this problem with a bias against the very idea of a patent system.
These critics would “solve” the patent quality problem by cutting down the total number of issued patents rather than focusing on problem patents. They suggest increasing examiners and examination time will weed out bad quality patents. And this might throw up additional roadblocks to inventors obtaining a patent by increasing the time and cost of securing an allowance. But this does not necessarily improve patent quality. Instead, it merely reduces the total number of patents issued.
Rather than “more examination,” solutions to the patent quality problem need to focus on “better examination.” In theory, “better examination” should stop invalid claims from ever getting issued while simultaneously streamlining allowance for valid claims.
One proposal for improving patent quality is to use artificial intelligence (AI) to find better and more relevant prior art references. Unfortunately, the proliferation of prior art sources has made high-quality patent searching more difficult. Moreover, many of the best sources of prior art, such as technical literature, are not stored and indexed in a single database the way patent documents are.
An AI search tool can search more broadly and provide more relevant prior art references to human examiners. This allows examiners to focus on understanding and applying the prior art to the claimed invention instead of spending all their time searching for prior art.
This necessarily raises the question: What is the best approach for leveraging the potential of AI to improve patent quality? There are several compelling reasons to believe that an open-source approach will produce the fastest, fairest, and most complete AI search engine.
The Problems of a Fragmented Approach
Everyone is affected by the patent quality problem. Those who disagree with the premise of the patent system must understand that refusing to participate in the solution will only allow the system to produce more low-quality patents. And those who believe in the patent system know that a distrusted system will call their patents into question, thereby increasing the costs of obtaining and defending their patents.
Diverse groups of people, including corporations in the business of patent tools, researchers and academicians at universities, tech enthusiasts, and patent offices across the globe, are exploring and developing patent search AI. While these groups of people might have different motivations, they share an interest in ensuring that only truly innovative technology gets patented.
But a piecemeal approach will have the following results.
Slower Development of a World-Class Patent Search AI
A cooperative development effort can direct greater resources and more diverse talents to develop a near-perfect patent search AI. A fragmented approach, by contrast, will result in smaller and less diverse teams that will take longer to solve the inevitable problems that will arise.
But the patent quality problem is not small. According to the U.S. Patent and Trademark Office’s (USPTO) patent quality metrics, roughly 8.2% of allowances were erroneously granted in 2021. Given that the USPTO grants over 350,000 patents per year, even a brief delay in developing a high-quality patent search AI could result in tens or even hundreds of thousands of invalid patents.
Possibility of Blind Spots Due to Biases in Training AI
After developing an AI, it must be trained. But biases in training will create blind spots in the AI’s capabilities.
These blind spots are not immediately apparent. Biases were found in AI products as diverse as facial recognition to loan processing software only after they produced flawed outcomes.
A fragmented approach could cement blind spots in an AI patent search engine’s learning that produces a hopelessly flawed product.
Proprietary Products that Give Unfair Advantages to Bigger Players
A high-quality patent search AI should be available to all users, whether they are patent offices, patent owners, or patent skeptics. Whether you use patent search engines to examine patents, obtain patents or invalidate them, you should have access to the highest-quality search results possible.
A fragmented approach will produce proprietary products that come with a cost to use. This approach could allow one group of developers to corner the market for patent searching. Worse yet, a developer might only use the product internally to give itself an advantage in obtaining its patents and invalidating its competitor’s patents.
The Game-Changing Open-Source Approach
Open-source development can solve these problems. Everyone has an interest in ensuring that only valid patents get issued. Patent owners cannot continue to spend millions of dollars defending their patents. And accused infringers cannot continue to face the decision to modify their products, spend millions in litigation or risk billions in patent damages.
Solving this problem in a fair, fast, and thorough way will require a cooperative effort. This immediately brings to mind an open-source approach. Open-source software (OSS) is a significant driver of freedom, trust and innovation in the digital age.
No one company will own the fruits of AI-powered patent searching. Instead, advanced patent tools will remain available to those needing them, including patent offices and solo inventors worldwide.
All developers will have an opportunity to contribute to a patent search AI and ensure it avoids biases, thereby producing a better product.
All businesses will have the opportunity to utilize a patent search AI internally to make better patenting decisions. They will also have the ability to commercialize patent search AI created through open source. Like Mozilla and Linux, an open-source patent search AI can create spin-off products that fill needs no one even knew existed.
Advanced Patent Tools: Open Source and Open Access
The open-source approach is the only way to ensure the creation of world-class patent tools that are assistive to innovators, examiners and other patent professionals alike, leading to only truly promising patents being granted.
Additionally, no one should have a monopoly on these advanced patent tools. Open access to these tools will level the playing field for innovators especially. Innovators, no matter how small, should be able to determine if they have invented something deserving of a patent. Currently, there is a huge gap where examiners have access to advanced patent tools; some under-resourced inventors cannot even afford a professional patent search.
Moreover, the USPTO is expanding initiatives for under-resourced inventors and first-time filers. Open access to advanced patent tools created through open source can significantly assist these initiatives. We hope that open access will drive more diversity and inclusion in the patent space.
