Navigating the AI Frontier in Intellectual Property Law – transcript

Ideas to Innovation - Season Three

[intro music]

Jay Myers: You see hints of what AI will be doing. I think it will increase the quality of the work and the decision making that goes in because I think an AI tool has a capability that we humans are biased toward a few particular ways in which we look at a search, a search issue or a watch issue. We look at and we’re biased toward the factors that we think are most important. An AI system will help us to broaden our scope in the same way that an AI system is a better chess player than a human chess player now. It will be better at identifying every argument that could be made and may pick out things that are subtle that humans are not as capable of picking out.

Intro: Ideas to Innovation. From Clarivate.

Neville Hobson: The relentless and transformative impact of technology in the professional sphere is nowhere more evident than in the intricate world of intellectual property and trademark law, a sector where the subtleties of language intertwine with the meticulousness of rights protection. Trademarks are the sentinels of brand identity, safeguarding the intangible assets that underpin market presence and value on a global stage.

In this rapidly evolving domain, artificial intelligence stands at the forefront of a paradigm shift, challenging established business norms. AI heralds a new chapter in IP law, brandishing tools capable of sifting through extensive data troves with a speed and precision that eclipse human capabilities.
This technological vanguard offers a promise of streamlined trademark searches, vigilant real-time infringement monitoring and visionary analytics that could shape strategic brand management. Yet the march of AI brings with it a suite of quandaries. The fidelity of AI in complex legal scenarios, ethical implications surrounding the stewardship of data, and the potential reconfiguration of conventional legal roles present significant considerations.

As we assimilate these intelligent systems, it’s imperative to balance the allure of automation and sophisticated analytics with the indispensable human elements of discernment and expertise.
Welcome to Ideas to Innovation, a podcast from Clarivate, with conversations that explore how innovation spurs people and organizations to think forward and achieve their full potential in areas such as science, business, academia, technology, sport, and more. I’m Neville Hobson.

Our guest in this episode is a distinguished trademark lawyer and an expert in US trademark and intellectual property law. He brings a wealth of knowledge and experience in leveraging Clarivate’s suite of IP management software and services to enhance service delivery to his firm’s clients. These tools not only streamline complex processes, but also significantly reduce costs and improve the quality of outcomes for those clients.

It’s my pleasure to welcome Jay Myers to Ideas to Innovation. Jay is the Director of Innovation for the Intellectual Property Practice Group at the Seyfarth Shaw international law firm, headquartered in Chicago. Jay is based in the firm’s Atlanta office and leads teams of legal, technology, docketing and administrative personnel, foreign counsel, and IP service vendors like Clarivate in the management of large international trademark portfolios.

Welcome Jay. It’s great to have you on the show.

Jay Myers: Thanks, Neville, nice to be here!

Neville Hobson: And let me introduce Arun Hill at Clarivate. Arun is a senior consultant working closely with Ed White, who heads the Clarivate Center for IP and Innovation Research and is based in London. Arun has a background in law and is an expert on innovation intelligence and ethico-legal issues. Thanks for joining us, Arun.

Arun Hill: Thanks Neville, glad to be here.

Neville Hobson: So Jay, innovation, IP and AI are three terms that I think perfectly focus on your current areas of interest. But first, tell us about your career with Seyfarth Shaw. What drew you into this area of law?

Jay Myers: Okay, yeah, thank you Neville. I started out as a corporate attorney actually many, many years ago and began by doing M&A deals and venture capital deals in the 90s and so forth. But over time, my practice emerged in with intellectual property and I became a trademark attorney probably 25 years ago fully, and focus on global trademark portfolio management.

So that’s what I’ve been doing lately and for the past many years. And it’s a very interesting practice that involves the clearance, searching, filing, maintenance, watching, disputes, internet issues, transactional issues and so forth. So that’s a pretty good summary of what we handle.

Neville Hobson: Tell us a bit about how your relationship with Clarivate developed because you have nice things to say about Clarivate, I must admit. So I’m just curious about the origin and how you see that going forward.

Jay Myers: Gosh, it goes back to the 90s actually with the searching. Before we had electronic searches, we would order the green books from Clarivate. Back then it was called Thompson and Thompson. And since then I’ve expanded my use of Clarivate tools to, we still use a lot of searching, hundreds per year. We do watching services through Clarivate system. We’ve used Clarivate for audits, large scale trademark portfolio audits.

We’ve used Clarivate services for large assignment projects. And we also, on the patent side, we use Clarivate for patent annuities as well.

Neville Hobson: That’s quite a great deal, quite a strong relationship. We were talking earlier and you mentioned the overall technology journey you’ve been on for the past 15 years or so and Lean Six Sigma. What can you tell us about that?

Jay Myers: Yeah, so back in 2008, our firm started exploring different ways of improving legal service delivery and that led to an exploration of Six Sigma and Lean Six Sigma, which are both defect and waste reduction methodologies. We then incorporated that into legal practice and we called it Seyfarth Lean, which was a way of incorporating these existing methodologies into legal practice.

And then about 2010 or so, 2009, I started exploring how to implement these types of methodologies into global trademark portfolio management. And so this led to, it started with a lot of process mapping, which I’m sure you’ve heard a lot of, and that then moved into data analytics. We did a lot of, a lot of metrical analysis of our processes and how to make things faster and more efficient.

An example of that would be first action approval rates. We did a study of first action approval rates from the USPTO and tried to design a system to allow us to achieve first action approval rates more often. That allowed us to then start thinking about fixed fee relationships with our clients. And then finally, over the course of time, we developed a lot of robust technology solutions that improved our practice as well.

Neville Hobson: Okay, that’s a great background. I know you love tech and building solutions for your clients, don’t you? Can you give us a specific example of that in action?

Jay Myers: Absolutely. Well, it starts the backbone of the system is a database, a status database, but we also have a document repository. And then on top of those tools, which are built in SharePoint, we have some more customized tools for trademark practice. And some of those are a watch automation tool and a search intake automation tool.

And both of those involve workflow automation that enables the client to have a more streamlined and transparent flow of work from the initiation of a search all the way to the conclusion and to the filing of a trademark application, most likely if it’s a successful search. And then from the perspective of watching, managing the various watches that come in every week and having an automated workflow for how to handle those watches.

And the Clarivate data that we use… as we have evolved over time, Clarivate has evolved over time and this data is now delivered electronically, it’s easily exportable, and we’ve worked with Clarivate to allow the data that we get from these search and watch tools to be fed into and integrated into our own workflow system.

Neville Hobson: Great. Thanks, Jay. I think that sets up a good moment for us to talk about those three terms specifically innovation, IP and AI as we continue this conversation.

Arun, let me turn to you. A few months ago, Clarivate published a report on AI perception and integration in IP. I guess you could describe it, or I would describe it certainly, as an explainer on how IP practice meets the coming wave of AI. Would that be right? Do you think?

Arun Hill: Yeah, I think that’s a fairly good synopsis. So we know that there’s quite a lot of interesting questions that sit at that intersection of AI and intellectual property. In fact, perhaps too many questions.

It’s not really surprising in the sense that both of those elements or the way that I think about it, both of those elements share a common trait. So if we think about IP as broadly referring to kind of creations of the mind, but then we put that up against what’s the objectives of modern computing? Well, a lot of modern computing is trying to replicate aspects of human cognition. So I don’t think it takes a lot of steps to think about why those two things might be intentional or cause potential problems as well.

So we look at it from different perspectives in terms of law, practice, but also in terms of policy as well. So what our research did is track what the downstream impact of AI was on IP professionals specifically. And we looked at attitudes or kind of sentiment, but we did it for a specific reason. I guess first of all, you could say to understand deployment and what are some of the barriers to getting AI into the firm or into corporations, but also because of the role that these professionals have in steering AI development.

And I think all of this comes back to this idea that AI applications should be human centric or they should be built with the human in mind and maybe work back from particular challenges or problems that we might want to solve. So it’s maybe less about AI everywhere all the time and more about AI where you most need it at the times that you most need it as well.

So we ran that survey. And it collected quite a decent amount of responses. So we got 600 responses from across IP and R&D professionals as a kind of contrast. And I would say, I would summarize the results as compelling, but maybe not surprising. So there was roughly an even split in terms of people that were using AI, people were not using AI. The majority felt like it would not impact their role. R&D respondents slightly felt that it would have more of an impact on their function. But ultimately, the preference was pretty clear.

The biggest appetite for using AI was in these low-risk, easily automated tasks. So nearly 60%, 70% expressed excitement about using AI for manual and laborious tasks. And using AI for things like some of the examples that Jay mentioned as data intelligence, using AI for trademark availability research. These were the things that came out on top.

And then we have things like interacting with the trademark and patent offices and PTO interaction in general, kind of dropping it to the bottom of the list, which makes sense. So it gave us a general impression of what’s going on in the IP community when it comes to AI development.

Neville Hobson: That’s most interesting. And I was thinking about just one thing where you mentioned the preferences of those you surveyed about incorporating AI into low risk, easily automated tasks for manual and laborious tasks. Can you give us an idea? What would those be like? Is that like number crunching big quantities of data, for instance? Or what, how would you understand what that actually means?

Arun Hill: Yes, it’s a really good question. Ultimately, I think the biggest misconception that we face about AI is that it’s an entirely new concept. In reality, it’s something that can be traced back to the 1950s and 1960s all the way to the Dartmouth Conference. And when we talk about AI in relation to the legal sector or legal professionals, that has been quite a budding ecosystem now for, I’d say, the last 10 years or so.

And some of those applications are already there. So if we think about automation, about contract analysis, about some of the applications of using AI for data, so these are kind of comfort areas or kind of hotspots where AI is developing. And that’s where we maybe see the most utility is because these are real world challenges.

And I don’t think it’s a kind of tied narrative, but maybe it will resonate with Jay. It’s this whole notion of, freeing up the lawyers to do more strategic lawyering is the way I would frame it. And I think that’s how we’re now looking at AI applications. But ultimately, it shouldn’t be AI for the sake of it. It should be based on a specific application or context.

Also, the other aspect that I would kind of caution against is taking one application or one area of legal practice and trying to apply the same AI model to another. It might not operate in exactly the same way. There’s definitely synergies to be had and efficiencies to be gained, but we also have to think about how it specifically interfaces with particular practice areas and particular nuances within the law.

Neville Hobson: Great. Thanks very much, Arun. This actually leads me, a good point, to the Clarivate Center for IP and Innovation Research that was announced in February. It’s described on the website as a new expert unit that can help organizations transform IP creation, protection, and management. Sounds like something Jay would be interested in, definitely. What can you tell us about it, Arun?

Arun Hill: Yeah, maybe Jay and I should have a talk. So it is pretty exciting. And to give a bit of context, I am a fellow within that research center, I’m kind of affiliated with it, if you like. And what it does is it rolls together our expertise, our operational experience, our data models and puts them under one roof.
And so it is something that we’ve been building up for the last 60 years. But it was just sitting in different places and what that has done is kind of bring it under the same umbrella. There’s a very kind of simple philosophy behind our research centre and ultimately it’s to help decision makers and innovators tackle some of the IP challenges that we see head on.

There is two strands to that or two different flavours to what we do within that research centre. We have more of a management consulting focus arm and that’s about optimizing IP portfolios. So my way of seeing that is the kind of people process and technology related to the IP lifecycle. And then on the other side we have, and this links quite nicely to the AI survey, we have more intelligence-based applications. So analytics, data models, the use of data infrastructure for decision-making in the IP world. So that’s the other component of it.

And as you kind of imagine, AI is an important piece of that puzzle, it’s an important piece that fits into that. So we’re kind of keen to share some of our experiences of curating content for almost 60 years, but also our research and ultimately to understand a lot more about how the IP community, IP professionals, are using AI themselves.

Neville Hobson: So, so far, we’ve considered what I would call foundational matters that connect innovation with intellectual property with a touch of AI. Basically, what’s happening today and what’s now in place for tomorrow. And that I think is a very significant place to be, frankly. So let’s look at what the future role of human expertise is, to your point earlier, in an increasingly automated field. And much of the excitement that we read about daily in the mainstream media, never mind our professional journals on what’s happening with AI. Is it gonna replace all our jobs and that kind of narrative? Let’s consider AI’s burgeoning role in IP law.

So Jay, let me ask you, what does the future landscape look like to you if we say we got a foundation here and we’re kind of set up now to really maximize this and I’m not sure what even that means exactly, but this is where we’re at as it were. We’ve got practical developments such as we’ve just been discussing it, particularly what Arun just outlined to us. But what picture do you see between now and say 2030, which let’s face it, it’s only six years away. What does it look like in your mind?

Jay Myers: Well, I think it’s a very interesting question. And I think you see hints of what AI will be doing . As Arun said, AI has been around for a long time. The first shoots we saw in the trademark practice were the way in which Clarivate was using AI to rank search results. And of course, this was done by humans manually for many decades. And so there are decades of data from humans doing search.

Then algorithms were created by Clarivate that now give you color coding of this is the ranking of this hit relative to your search. And the same goes with watching. And so what the algorithms are doing is analyzing likelihood of confusion under the factors that make up the analysis of likelihood of confusion, and then ranking with a color coding scheme, which search hits and which watch hits are important.

So that’s kind of the beginning. You combine that with text with the LLMs and you get generative capabilities, and all of a sudden you’re combining human data with algorithmic data, together with a text generation tool. And then finally, when you combine that with the Dart’s IP data, which is, I understand, it is a complete data set of all IP cases around the world, you’ve got a very smart and robust AI system that can not only generate color coding to analyze hits, but can also write your report to the client, help you write your report to the client, help you analyze the data, help you write the demand letter to the other side, help you write the brief in your opposition, help the other side write its brief, help the decision makers at the trademark offices decide on things. You’ve got a whole possibility range that will, in effect, well, in one sense, it will increase the efficiency at a minimum.

But more importantly than that, I think it will increase the quality of the work and the decision making that goes in because I think an AI tool has a capability that we humans are biased toward a few particular ways in which we look at a search, a search issue or a watch issue. We look at and we’re biased toward the factors that we think are most important. An AI system will help us to broaden our scope in the same way that an AI system is a better chess player than a human chess player now. It will be better at identifying every argument that could be made and may pick out things that are subtle that humans are not as capable of picking out. So you’ll get a much more comprehensive and robust product, qualitatively better in addition to being more efficient and more cost efficient.

Neville Hobson: That sounds an excellent explainer, I think, Jay, of what the future, the near future, the immediate future is likely to look like.

Jay Myers: And Neville, if I can interrupt for one second, I wanna make clear, this again is not a replacement for humans, but an augmentation, an augmentation of the human role by removing and amplifying, removing tasks that are redundant and take up time, but also amplifying the qualitative analysis.

Neville Hobson: Yeah, I’m with you on that 100%. And in fact, I often use the phrase myself when I’m trying to explain some of these things to people who are asking questions about it is think of the term AI is ‘augmenting intelligence’ as opposed to ‘artificial intelligence’. And that is wholly about enabling the humans to perform better for want of a better way of putting it in helping them, well, augment, basically what their work is all about.

So let me immediately go to you, Arun. What are your thoughts on what Jay said and how can you augment that a bit?

Arun Hill: Yeah, I think there’s a lot of similarities in mine and Jay’s perspectives, which I’m sure will be of great comfort to the both of us. One of the things that Jay touched upon, which I think is really key is not just the possibility that AI might bake in bias or embed bias, but the fact that it has an important role to play in removing it, and actually supplementing the tasks done by humans by providing that kind of objective lens. So often that’s not a way that we view AI, but it is an equally important application.
My feeling is that specifically when it comes to generative AI and large language models, in the short term, we tend to overstate the impact, but we perhaps underestimate the long-term impact of those same technologies.

So I think one of the peculiarities of AI is that there’s no finishing line in sight. So there’s no such thing as the kind of intelligent enough system where the developer says, that’s it, you know, I’m checked out, it’s smart enough, it’s doing everything that I want to do. So I guess my hope is that we can kind of ride this wave, but do it responsibly.

So one of my esteemed colleagues, Peter Keyngnaert, who’s our director of data science, one of the things that he talks about is the importance of leaving room for play and experimentation. And I actually think that that’s a really valuable thing to say, is that we need to leave room to kind of play around with these technologies and find out where they could be developed. But at the same time, I think we shouldn’t avoid asking hard questions either.

So I think Jay mentioned this already, but one of the fundamental questions for me and something that we think about a lot is how this changes the delivery of legal services and creates alternative ways of working and operating for organizations. So what does this mean for the billable hour? What does this mean for the operating model of law firms?

These are important questions that maybe we don’t wanna engage with prematurely, but it doesn’t mean that we should avoid them altogether. So often in the discussions that we’re having around AI, we’re looking at how to mitigate some of the negative impact of what the technology would be. So designing it with the kind of human in mind.

So there are some areas where we might not want to put AI. So an example of that, or to give a kind of unrelated example, would be many aspects of the kind of client-attorney relationship have nothing to do with legal advice and everything to do with the other sort of value that’s delivered in that exchange.
So if you think about going through a divorce or you think about approaching the family law system, maybe in that scenario, the last thing you would want to do is engage with an AI system. So the exchange of value in the legal system is slightly different in that case. So that might be an area where we don’t want to put AI, a similar thing where the information is privileged as well.

That’s a kind of extreme example, but it does filter its way down into the world of IP. So we’ve already had this in the survey where maybe PTO interaction and also prosecution errors, maybe we don’t want to potentially put AI. So there is already aspects of this conversation already happening. I think that maybe using data for strategic and tactical purposes is a really good start where the risk tolerance is a little bit higher. So I think we all know that legal professionals probably have quite a big role to play in AI development itself.

But it’s clients that stand to potentially benefit the most. One of the things that always strikes me as very strange is that if we invented a new therapy or cure for a particular disease, we would be talking about that in terms of the value that it’s going to deliver patients, not the value that it will deliver doctors itself.

I think that that’s potentially a framing that we have to take into account when we’re thinking about AI and what it actually does and who benefits from it. So I think that’s kind of ultimately my conclusion is thinking about the downstream impact of new technologies and working our way back from real world challenges. So we should start with our everyday issues that affect our jobs and trace that back and see where we could possibly apply AI, because that’s where I think the real power of these type of technologies is likely to be realized.

Neville Hobson: That’s excellent, Arun. Appreciate it. Jay, let me come to you with a literally the question if you anything you want to add to what Arun said, or do you have a different perspective you want to bring in?

Jay Myers: No, I think Aaron’s exactly right. I think that business people, normal business people that are not lawyers are not all of a sudden going to decide to become trademark prosecutors or litigators. But the people that stand to benefit greatly are friends in the in-house community. They really can. The lawyers who are in-house who really are driving for efficiency and driving for better outcomes and better results and being a one-stop shop for their corporate and business clients internally. They are the ones that really stand to benefit enormously.

Again, there will always be, I think, a need for outside counsel who are very fine-tuned specialists in particular areas, but this democratization of process and data and knowledge will help the in-house practitioner greatly to expand their capabilities.

Neville Hobson: Okay, that’s really good. And I’m going to ask you both a final additional question. That’s very brief, just from each of you, starting with you, Jay.

If there’s one issue you think is probably the most significant that we need to pay attention to as a risk or a threat or a challenge in the profession, generally, but specifically related to intellectual property law, what would you say to that?

Jay Myers: That’s a tough question. I think that the obvious thing that comes to mind immediately is the capacity for hallucinations within the AI systems. Getting it wrong. I mean, I guess we’re gonna have to have the watcher watching the watcher so to speak. In other words, we’re having an AI system check over to make sure that we’re doing the best job we can but then we have to make sure that the AI system is not making mistakes and errors too. So, there will be a learning curve in that sense.

And then of course, the customary things that we hear about such as confidentiality and privacy and personal information rights and so forth. I think those are, specifically to AI, it’s this hallucination or legal practice, it’s the hallucination thing with respect to the broader world. It’s this privacy confidentiality issue, I would say. Those are probably the two I would focus on.

Neville Hobson: Right. Okay. How about you, Arun?

Arun Hill: Yeah, I guess the first thing I would say is that we have to take into account that, you know, at least within the legal profession or the IP ecosystem more generally, we already have a set of values that we adhere to, or we know what we’re about. And I think that makes us quite special or unique. So a lot of other industries are ultimately facing the issue of what values do we want to embed into an AI system.

But we have an IP regime, we also have professional liability, we have codes of conduct. So our values, if you like, are already quite well defined. So I think that gives us a kind of unique opportunity to think about how we might wanna put things into AI systems.

At the same time, the effects of AI are quite unknown and some of the impact isn’t going to be invisible. So we should still be very conscious of holding up things like the rule of law and respect for data privacy and security, these are principles that are quite dear to our profession, I suppose. And so we have to be very, very conscious of that when we’re building these systems. Often how that manifests itself is as something technical. So as accuracy, as the design of the system and so on.

So I think a really key thing to bear in mind is that even as legal professionals, as innovators, as people from the IP community, we should not be afraid to engage in conversations about AI and have a more active role in steering its development because ultimately we are the people going to have to use it. And so I think that that’s quite important from my side is that we’re actually able to have a seat at the table and participate in those conversations rather than having an AI system imposed on us.

Neville Hobson: Jay and Arun, thank you both for sharing your thoughts and insights into this huge topic at the intersection of innovation, IP and AI. Thank you both.

Jay Myers: Thank you.

Arun Hill: Thank you.

Neville Hobson: You’ve been listening to a conversation with our guests, Jay Myers of the Seafarth Shaw law firm and Aaron Hill at Clarivate, about the advantages and challenges of AI’s expanding influence on innovation and intellectual property law.

For information about the Clarivate Center for IP and Innovation Research, visit clarivate dot com and search innovation research.

In a few weeks, we’ll release our next episode. Visit slash podcasts for information about Ideas to Innovation.

And for this episode, please consider sharing it with your friends and colleagues, rating us on your favorite podcast app, or leaving a review.

Until next time, thanks for listening.

Outro: Ideas to Innovation. From Clarivate.