Over 500 years ago, in Venice, the first patent statute was enacted. Through the next half-millennium Patent systems evolved across the world from there. In the US, the constitution specifically authorizes a patent system through language found in Article 1, Section 8, Clause 8, “To promote the progress of science and useful arts, by securing for limited times to authors and inventors the exclusive right to their respective writings and discoveries.” A basically patent represents a trade; a limited monopoly in exchange for promotion of science.
What does this have to do with AI? Data. It is a requirement that every patent application must include a detailed technical description of the invention to be protected. This is in turn published publicly so others can read and learn from the inventor. Moreover, the patent also carries some very useful additional metadata – the inventor name/names, addresses, the companies they work for (the patent owner), the date of the patent filing, a list of related patents/applications, and more. Between this metadata and the technical description of the invention, you have quite an amazing data set identifying research and development activity that has been deemed valuable enough to spend money on for a chance at patent protection (not cheap). In many ways, if you want to analyze a technology space, patent data can be better than analyzing products – it provides much more detail and identifies technology that did not or has not hit the market.
Drawing on an in-house data set of over 100 million global patent matters and their metadata, we will dig into the tens of thousands of patents in force and pending before world patent offices which demarcate the field of AI.
This rich data set includes ownership, inventorship, importance indicators, custom categorization into technology types and application fields. Additional categorization and analytical data is generated from a balanced combination of expert and AI-assisted analysis.
We will dive into this data to see technology development trends, who owns what, where this IP originated, where it is held, and how this data lines up with to the current rhetoric around AI superpower development.
US and China AI happen to be a hot topic for discussion these days. Today China stands toe to toe with the US in its value from a patent holding and enforcement venue. What this means for AI data is that there are a lot of patent filings in China and we have great data to work off of in comparing US and Chinese development and the value of technology in each jurisdiction. While in the past, we have seen Chinese companies re-purpose Silicon Valley technologies for the China domestic market, we may start to see the pendulum swing back based on data showing AI implementation technology patented by domestic Chinese companies in China but not the US. The publication of Chinese patents on technologies not concurrently protected in the US (which makes that technology public domain – able to be freely used) – has the potential to drive a copying of Chinese-originated AI tech in the US. We will pull back the curtain and take a look at those technologies.
Specific topics addressed:
Global IP landscape
-Top players in each key jurisdiction
-Trends across countries
-Deep learning patent progression across China, US & Europe
IP impact on future development and roll-out of AI technologies
-How does this data affect development and market strategy?
-How does foundational and implementational patents affect partnering and licensing opportunities for market access and technology advancement?
Comparative US & Chinese IP landscapes: US and China represent the largest caches of IP in the world.
-IP and assertion infrastructures (US remains the IP juggernaut, but over the last decade, China has grown to become one of the busiest and most desirable IP jurisdictions in the world)
-Impact on patent holdings & filings
-Categories by foundational and implementational AI
-Chinese companies that are leading implementational AI in areas like neural networks, manufacturing AI, Consumer AI, Enterprise AI
-Who is the next Chinese AI unicorn?
-Who is the next Chinese AI Mark Zuckerberg or Steve Jobs?
-Trends illustrating US-AI companies and solutions in China while Chinese companies are decreasingly interested in entering the US market and reasons why
-Who is leveraging state-of-the art AI technology in China?
-What cities, areas and people
Tom Marlow is the CTO for Black Hills IP, where he works tirelessly to drive IP legal services into that age of AI and automation. Relying on data wherever it can be found Tom and the Black Hills team build products to power internal staff and legal professional customers. Tom comes from a background of technology and analytics. Being exposed to massive amounts of patent data very early in his career, he learned techniques to quickly build data sets and evaluate the results to drive strategy. This work led to a corporate leadership position driving global IP operation and strategy for the renowned Fairchild Semiconductor Corporation. In addition to maintaining a portfolio of several thousand patents, Tom managed patent development, litigation, licensing and acquisition across the US, Europe and Asia. Around this time, Tom co-authored a desk reference for patent attorneys which provided an indexed analysis of appeals decisions for use in prosecuting patent applications (now in its 7th edition).
Tom is a registered patent attorney and electrical engineer with a passion for IP systems and previously served as co-chair of the patent analytics and portfolio management department at the Minneapolis patent firm Schwegman, Lundberg & Woessner, P.A. Tom has advised companies from startups to household name multinationals on IP strategy, operations and policy.
Tom has spoken before a diverse audiences from patent attorneys, to C-suite executives to engineers to startup founders on patent management, analysis, and strategy over the years. He received his law degree from Franklin Pierce Law Center, and Bachelors of Science from the University of Notre Dame.
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