Navigating the AI Frontier in Intellectual Property Law

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 in which we look at a search issue or a watch issue, we look at how and we're biased toward the factors 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 chest player.

Will be better at identifying every argument that could be made and may pick out things that are subtle that humans are not as Ideas to Innovation from Clarivate The relentless and transformative impact of technology in the professional sphere is nowhere more evident. A sector which is Trademarks are the Brand identity. Artificial intelligence. Paradigm shift, challenging a step. Business norms. in IP law, brandishing tools capable of sifting through extensive data troves.

speed and precision that eclipse human capabilities. This technological vanguard offers the promise of streamlined trademark searches, vigilant real-time infringement monitoring, and visionary analytics Strategic brand management. Yet the march of AI brings with it a suite of quandaries, the fidelity of AI in complex legal scenarios.

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 Welcome to Ideas to Innovation, a podcast from Claravet.

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 Safeth Shore 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. Thanks, Novel. Nice to be here. And let me introduce Aaron Hill at Clarivate. Aaron is a senior consultant working closely with Ed White, who heads the Clarivate Centre for IP and Innovation Research and is based in London. Aaron has a background in law and is an expert on innovation intelligence and ethico-legal issues. Thanks for joining us, Aaron. Thanks, Neville. Glad to be here.

So Jay, innovation, IP, and AI are three terms that I think perfectly focused on your current areas of interest. But first, tell us about your career with C for Shaw. What drew you into this area of law? Okay. Yeah. I thank you, Neville. I um I started out as a corporate attorney actually many, many years ago.

and uh began by doing M and A deals and venture capital deals in the in the nineties and so forth. But uh over time my my practice uh merged in with um with intellectual property and I became a a a trademark attorney probably twenty-five years ago fully and focus on global trademark portfolio management.

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

Tell us a bit bit about how your relationship with Clarivate developed, because you 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. Gosh, it goes back to the nineties actually with the searching. Before we had electronic searches, we would order the green books from Clarivate. Uh back then it was called Thompson and Thompson. And uh and since then I've

expanded my use of Clarivate tools to a still 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.

Got it. That's that's quite a a great deal. Uh quite a strong relationship. We were talking earlier and you mentioned the overall technology journey you've been on for the past fifteen years or so and LeanSix Sigma. What can you tell us about that? Yeah, so back in two thousand eight 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

uh defect and waste reduction methodologies. We then incorporated that into legal practice and we called it Cypher Lean, which was a way of incorporating these existing methodologies into 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 and that that then moved into data analytics. We did a lot of a lot of metrical analysis of our processes and and how to make things faster and and uh more efficient. Um an example of that would be first action approval rates. We we did a study of first action approval rates from the USB TO and tried to design a system to allow us to achieve first action approval rates more often.

That that allowed us to then start thinking about fixed fee relationships with our clients. And then finally, uh over the course of time, we developed a lot of robust technology solutions that improved our practice as well. Okay, that's a great background. Uh I know y you love tech and building solutions for your clients, don't you? So c can you give us a specific example of that in action? Absolutely. Well the 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 we have uh 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 the trademark application, most likely if it's a successful search. And then from from the from the perspective of watching, managing the various watch hits that come in every week and having an automated workflow for how to handle those watches. And the clerobate the clairvate data that we use.

Um uh as we have evolved over time, Clarivate has evolved over time and m this data is now delivered electronically, it's easily exportable, and we've worked with Clarivate to to allow these tool these these the data that we get from these search and watch tools to be fed into and integrated into our own workflow systems.

Great. Thanks, Jay. I think that sets up a good moment for us to talk about uh those three terms specifically innovation, I uh IP and AI. as we continue this conversation. Aaron, let me turn to you. Um a few months ago, Claravate 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 I P practice meets the coming wave of AI. Wouldn't that be right, do you think?

Yeah, I think that's a fairly good uh synopsis. So we know that there's quite a lot of interesting questions that sit at that intersection of AI and intellectual property. Perhaps too many questions. Um it's not really surprising in the sense that 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 again

what's the objectives of of kind of modern computing? Well, a lot of modern computing is trying to replicate aspects of of human cognition. So I don't think it it takes a lot of steps to to think about why those two things might be be intentional or cause potential problems as well. So We kind of look at it from from different perspectives in terms of law, uh practice, but also in terms of uh uh policy as well. So what our our research did.

um is track what the downstream impact of AI was on on IP professionals specifically. Um and we looked at attitudes or kind of sentiment. Um, but we did it for for for a specific reason. I guess first of all you could say to to uh understand deployment and what are some of the barriers to to getting AI into the firm or into corporations.

but also because of the role that these professionals have in steering AI development. Um and I think all of this comes back to to this idea that AI applications should be human centric or they should build with the be built with hu 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, uh and more about AI where you most need it, um, at the times that you most need it as well. So

We've we ran that survey um and it and it collected quite quite a decent amount of responses. So we got six hundred responses from across IP and R D professionals as a kind of contrast. Um and I would say I would summarise the results as as compelling, but maybe not surprising. Uh so there was roughly an even split in terms of of people that were using AI, people were not using AI. the majority felt like it would not impact their role.

Um R and D respondents slightly felt that it would have more of an impact on their function. Uh, but ultimately the the preference was pretty clear. Um the biggest appetite for for using AI was in these kind of low risk, easily automated tar. Um so nearly 60, 70% um express excitement about using AI for manual and laborious tasks.

Um and using AI for things like, you know, some of the examples that Jay mentioned as well. So using AI for data intelligence, using A AI for trademark availability research, these were the things that came came out on top. And then we have things like pr uh uh interacting with the trademark. And patent offices and PTO interaction in general, kind of dropping out to the bottom of the list, which makes sense. So it gave us a general impression.

of what's going on uh in the IP community when it comes to to AI development. That's most interesting. And I was thinking about uh just one thing where where you mentioned um, the preferences of those you surveyed about incorporating II AI into low risk, easily automated tasks, uh, for manual and laborious tasks. Can you give us an idea of what what would those be like? Is that like number crunching big quantities of data, for instance, or

What do how would you understand what that actually means? Yes, it's a really good question. Um ultimately I think the biggest mis 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 professional,

That has been quite a budding ecosystem now for I'd say the last the last 10 years or so. Um, 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 developed.

And that's where we maybe see the most utility is because these are real world challenges. And um I don't think it's a a kind of tied narrative, but I'm you know, maybe it'll resonate with Jay. It's the you know, this whole notion of freeing up the lawyers to do more strategic lawyering um is is the way I would would frame it. And I think that that's 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. Um the also the the other aspect that I would kind of caution against is is taking one application or one area of legal practice and trying to apply the same AI model to to another. It might not operate in exactly the same way. There's definitely uh synergies to be had and efficiency be efficiencies to be gained, but we also have to think about how it specifically interfaces with particular practice areas and particular nuances within within the law.

Great. Thanks very much, Aaron. This actually leads me uh a good point to the Clarivate Center for IP and Innovation Research. That was announced in February. Uh it's described uh on the website as a new expert unit that can help organizations transform IP creation protection and management. Sounds like something Joey would be interested in definitely. What can you tell us about it, Aaron? Yeah, maybe Jay and I should have a a talk after it.

So it is pretty exciting. Um to give a bit of context, I am a a fellow within that research center. I'm I'm kind of affiliated it with And what it does is it rolls together our expertise, our operational experience, our data models, and puts them under one roof. Um, so it is something that we've building been building up for the last sixty years, um, 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 our research centre and ultimately it's to help. uh decision makers uh and innovators tackle some of the IP challenges that that we see head on. Um there is two strands to that or two different flavours to to what we do within that research center. 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 life cycle. And then on the other side we have uh and this links quite nicely to to the AI survey, um 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.

Um and as you can kind of imagine, um, AI is a is an important piece of that puzzle. It's an important piece that that fits into that. Um 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 the IP community, IP professionals, are using AI themselves.

So uh so far we've considered uh I what I would call foundational matters that uh connect innovation with intellectual property with a touch of AI. um basically what's happening today and what's now in place for tomorrow. And that I think is a is a very significant uh place to be, frankly.

Um, so let's look at what the future role of human expertise is, to your point earlier, uh in an increasingly automated field. And much of the excitement that we read about daily in the mainstream media, never mind our are professional journals. on what's happening with AI. Is it going to replace all our jobs and and that kind of narrative? Um let's consider uh AI's burgeoning role in IP law.

So Jay, uh let me ask you, what does a future landscape look like to you if we say we got a foundation here and we're kind of set up now? uh to really maximize this and I'm not sure what even that means exactly, but this is where we're where we're at, as it were. We've got practical developments such as we've just been discussing it, particularly what Aaron just just outlined to us. But what picture do you see?

um between now and say twenty thirty, which let's face it, it's only six years away. Um what w what does it what does it look like in your mind? Well I think i it's a very interesting question and I think you see hints of uh of what AI will be doing. As Aaron said, AI has been around for a long time. The the the first the first shoots we saw in the trademark practice were the way in which Clairvet was using AI to rank.

search results. And of course this was human done by humans manually for many decades. And so there's a d there are decades of of data from humans doing search. Then algorithms were created by ClairVate 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 confusion under the factors that make up the analysis of likelihood confusion and then ranking with a color coding scheme which 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 uh with algorithmic data w together with

um uh a a text generation tool. And then finally when you combine that with the Dart's IP data, which is the which as 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 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 at the trademark offices decide on things. You've got a you've got a whole possibility range that will In effect. Well, in in one sense it will increase the efficiency at a minimum. But 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

a search issue or a watch issue, we look at how 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 at identifying every argument that could be made and may pick out things that are subtle that 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 costly.

That sounds uh uh an excellent uh explainer, I think, Jay, of what what the future, the near future, the immediate future is likely to look like. And it never defined if I can interrupt for one second. I I want to 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.

Yeah, I I I'm with you on that a hundred percent. And in fact I 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 as 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 right ign uh well, augment basically what their work is all about. So, um let me immediately go to you, Aaron. What what are your thoughts on what Jay said and and How can you augment that a bit? Yeah, yeah.

Uh I I think there's a lot of similarities in in mine and Jay's perspectives, um, which I'm sure will be of great comfort to to the both of us. Um uh one of the things that 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

and actually um supplementing the tasks that are done by humans by by providing that kind of objective lens. So often that's not a way that that we view AI, but it is an equally important um application. My feeling is that specifically when it comes to generated AI and large language models. uh in the short term we tend to overstate um the impact, but we perhaps underestimate the long term uh impact of of 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 uh where the developer says, that's it, you know, I'm checked out, it's it's smart enough, it's doing everything that I want to do. Um so I guess my hope is that we can kind of ride this wave but do it responsibly.

Um, so one of my um esteemed colleagues, um Peter Kaynart, 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 that that we need to re leave room to kind of play around with these technologies and find out where they could be developed. Um but at the same time

Uh, I think we shouldn't avoid asking hard questions either. So I I think Jay mentioned this already, but one of the fundamental questions for me is and it's something that we think about a lot, is how this changes the delivery of legal services. um and creates creates alternative ways of working and operating for for organizations. So um what does this mean for the billable hour? What does this mean for the operating model of of law firms?

These are important questions that maybe we don't want to engage with prematurely, but it doesn't mean that we should avoid them altogether. Um so often in the discussions that we're having around AI, we're looking at how to mitigate some of the negative impact of of what the technology would be. Um so designing it with the kind of human in mind. So There there are some areas where we might not want to put AI. So an example of that, or or to give a kind of unrelated example.

would be many aspects of the kind of client attorney uh relationship have nothing to do with legal devo uh uh 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 in in the legal system is slightly different in that case. So that might be an area where we don't want to put AI, um, a similar thing where the information is privileged as well. Um 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 um and also um prosecution are errors, maybe we don't want to potentially put AI. So there is already aspects of this conversation already happening. Um I think that maybe using data for

strategic and and uh tactical purposes is a really good start uh where the the risk tolerance is a little bit higher. So, you know, we I think we all know the kind of legal professionals probably have quite a big role to play in in AI development itself. Um, but it's clients that stand to potentially benefit the most. So one of the things that always strikes me as very strange is that, you know, if we invented a new uh therapy or cure for a particular disease.

We would be talking about that in terms of the value that is going to deliver patients, not the value that will deliver doctors itself. So I think that that's potentially a framing that we have to take into account when we're we're thinking about AI and what it actually does and who benefits from Um so I think I think that's kind of ultimately my conclusion is is thinking about

the downstream impact of 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 the real power of of these type of technologies is likely to be realised.

That's excellent, Aaron. Appreciate it. Jay, let me come to you with a literally the question for you anything you want to add to what Aaron said or do you have a different perspective you want to bring in? No, I I think Aaron's exactly right. I think that uh I I I think that that business people, normal business people that are not lawyers, are not all of a sudden gonna decide to become trademark prosecutors or or or or litigators.

But the people that stand to benefit greatly are the our friends in the in house community. They really can the lawyers who are in house who are really are driving for efficiency and driving for better outcomes and better results and being uh

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 and knowledge will will help the the in-house practitioner greatly to to expand their capabilities.

Okay, that's really good. And I'm gonna ask you both a n final additional question. That's very brief, just uh 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?

Well, that's a tough question. I I think that the the obvious thing that comes to mind immediately is the capacity for hallucinations within the AI systems. getting it wrong. I 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

uh 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 and and and personal information rights and so forth. I think those are those are specifically to AI, it's this hallucination or or legal practice, it's the hallucination thing with respect to the broader world

It's this privacy confidentiality uh uh issue, I would say. Those are probably the two I would focus on. Okay. How about you, Aaron? Yeah, I guess the first thing I would say is that um we have to take into account that we're you know, at least within the legal profession or uh the IP ecosystem more generally. Um, we already have a set of values that we adhere to or we know what we're about.

Um and I think that 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. Um so our um values, if you like, are already quite well defined. Um so I think that that gives us a kind of unique opportunity to to think about how we might want to put things into two AI systems. Um at the same time the effects of AI

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

So I think a really key thing to 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 that's quite important uh from my side is that we're actually able to have a seat at the table and par participate in those conversations.

rather than having an AI system imposed Jay and Aaron, thank you both for sharing your thoughts and insights into this huge topic at the intersection of innovation, IP and AI. Thank you both. Thank you. Thank you. You've been listening to a conversation with our guests, Jay Myers of the Seaforth Shore 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 Claravate Center for IP and Innovation Research, visit claravate.com and search Innovation Research. In a few weeks, we'll release our next episode. Visit claravate.com 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. Ideas to Innovation from ClaraVate.