“To modernize our patent infrastructure, we need better tools to help inventors and examiners find relevant prior art.”
Patent categories were established more than 100 years ago. There are dozens of categories that reflect industry at the time: gears, sewing machines, and bicycles, to name a few.
While these are certainly useful categories, the patent classification system has not kept up with the times. It leaves out many modern technologies, like inventions that are based on machine learning or blockchain. There are no categories for these innovations, which are reshaping our world in real-time.
The problem? When patent classifications don’t actually classify inventions, we have no way of knowing how many inventions in these categories are being registered.
Even if there are no specific categories for them, they can still be protected with a patent. This makes it difficult for innovators to be sure their ideas are patentable because it’s not always clear in which category to search.
This problem is particularly acute in machine learning, which is an especially “hot” sector with many competing patents that may or may not be classified correctly. Let’s look at the data—and propose a solution.
The Murky Patent Waters
If you read reports related to machine learning patents, you’ll find unclear information that can be misleading.
For instance, one patent data analytics platform recently analyzed published U.S. patent applications to find that machine learning patents were ranked at #4 in 2020. They considered machine learning patents to be only those in the patent class G06N 20, with an asterisk: “group G06N 20/00 is impacted by reclassification into groups G06N 20/10 and G06N 20/20.”
The warning adds: “All groups listed in this warning should be considered in order to perform a complete search.” Each of these groups includes machine learning patents, and an incomplete analysis can’t be conclusive.
Doing our own research, we found out these are the top current assignees of machine learning patents in the G06N 20 class:
We see familiar companies like IBM, Microsoft, and Google, but some notable names are absent, such as Apple and Amazon.
So, what happens if we expand our analysis to see if this classification was missing some patents or patent holders? We’re confident that the current patent classifications are no longer fit for purpose, so we decided to do some sleuthing.
Looking Elsewhere for Machine Learning Patents
We did a simple keyword search to look for machine learning patents and applications that may not appear in the single classification.
We found 42,623 patents/applications that mention “machine learning” have been published since 2011. Of these, 8,529 were filed in 2020, or 18.3%. The overall filing year distribution is as follows:
Of these patents/applications mentioning “machine learning,” 33,121 aren’t part of the G06N 20 class. That’s 77.7%.
There’s a massive gap between the analysis based on the G06N 20 classification and the semantic search for mentions of machine learning. This discrepancy is the result of our outdated patent classification system that hasn’t caught up with the times.
And remember Apple’s notable absence from the earlier graphic? Well, it turns out that the company was actually active in machine learning in 2020: it filed 276 of the patents/applications outside of the G06N 20 class that mention “machine learning.”
Under these criteria, Apple is one of the top filers in the machine learning area. Of course, because the referenced study only considers one category, Apple is not even mentioned among the top patent assignees.
Looking a bit deeper into Apple’s patents, we can see that they have quite a few machine learning patents, but they aren’t in the G06N 20 class.
Clearly, Apple has been active in machine learning, and our broken classification system doesn’t allow competitors, the public, or the media to easily track which patents are filed under which category. This glaring discrepancy is just one of the reasons why we need to reform our patent classification system.
Rebooting Our Patent Classifications for Today’s Technology
To modernize our patent infrastructure, we need better tools to help inventors and examiners find relevant prior art. This is vital because inventors rely on patent classification systems to conduct searches. If the patent classification system doesn’t work (as we’ve seen here), an inventor doesn’t find existing relevant prior art. This omission may prevent inventors from getting a patent.
It also means that the inventor will invest a great deal of time and money in developing and marketing a product on the assumption that it will be protected with a patent. Just imagine how difficult this becomes for an independent inventor or a small business with very limited resources – which are already being failed by the USPTO.
So, what can we do? How can we better support innovators?
Semantic search is a starting point. We can use NLP (natural language processing) to find relevant patents and applications quickly and easily. Currently available semantic search tools are very expensive. They’re out of reach of independent inventors and small businesses, which is slowly changing.
Another problem is that semantic search tools don’t always work well. They need to be improved to be easy to use for those who aren’t patent professionals.
We may need to continue with the current patent classification system to avoid major disruptions to the patent process. But we can certainly use technology to keep the current classification system from overwhelming independent inventors and small businesses and making patent searches more accurate and equitable for all.
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