Paul Lee, co-founder and CEO of Patlytics, joins Greg Lambert to explain how an AI platform built specifically for intellectual property work is changing the patent lifecycle. Patlytics supports workflows spanning patent drafting, prior art analysis, office action responses, portfolio management, litigation readiness, and claim-chart preparation. Lee reports that the company now works with roughly 55 percent of the Am Law 100 and hundreds of corporations across technology, biotechnology, pharmaceuticals, and other patent-intensive industries.
Lee traces Patlytics’ origins to his experience as a venture capitalist and more than 100 conversations with patent attorneys. Those interviews exposed a practice filled with expensive, labor-intensive processes, from drafting detailed patent specifications to constructing claim charts for litigation. His interest also grew from the Apple and Samsung patent battles, the IP expenses faced by venture-backed companies, and conversations with Patlytics co-founder Arthur Jen and former Latham & Watkins patent litigator Bob Steinberg.
The conversation turns to Patlytics’ work involving USPTO patent examiners and the broader effect of placing AI on both sides of the examination process. While confidentiality limits the details Lee discusses, he identifies quality and the examination backlog as two areas where specialized technology offers meaningful assistance. He also contrasts Patlytics with broad legal AI platforms such as Harvey and Legora, arguing that patent professionals need tools designed for the precision, technical detail, and specialized workflows of IP practice.
Human judgment stays central to Lee’s vision. Patent attorneys still own the work product, approve key decisions, and remain responsible when an AI-generated analysis falls short. At the same time, client expectations continue to rise. Clients want faster work, higher quality, and lower costs, while law firms need sustainable margins. Lee sees flat-fee arrangements and more predictable workflows as one route toward sharing the “AI dividend” between clients and their outside counsel. In-house teams also gain more capacity for infringement analysis, patent-portfolio reviews during M&A, cross-licensing strategy, and litigation preparation.
Looking ahead, Lee describes a striking change in attitude among patent professionals, from widespread skepticism a year ago to broad optimism today. His crystal-ball concern is less about whether lawyers will adopt AI and more about whether its economics will hold together. As free experimentation gives way to consumption-based pricing, firms will need to measure the value of each workflow and avoid spending $50,000 in AI costs on a $5,000 matter. Token maxing had its moment. ROI gets the next meeting invitation.
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[Special Thanks to Legal Technology Hub for their sponsoring this episode.]
Email: geekinreviewpodcast@gmail.com
Music: Jerry David DeCicca
Transcript
Greg Lambert (00:00)
Hey, everybody. This week we have Paul Lee from Patlytics on the show, and we’ll be talking about patents, AI, the USPTO, and a whole lot more. But first up, here’s a word from our sponsor.
Marlene Gebauer (00:19)
Hi, I’m Marlene Gebauer from The Geek in Review. I have Sam Moore here from Legal Technology Hub. He’s going to tell us about AI governance from the advisory side.
Sam Moore (00:28)
Thank you. Good to see you again.
In the advisory team at Legal Technology Hub, we’ve been having a lot of conversations with clients about AI governance. Most law firms and departments have at least an AI policy of some kind, and there are some broad standards starting to emerge.
A trend we’re seeing at the moment is a shift away from policies written in 2023 and 2024, which largely stated what you couldn’t do, toward a more informative approach that defines different risk categories and makes distinctions between routine, low-risk uses of AI and other, more substantial use cases.
I think that’s appropriate for where we are. It’s also what clients are coming to expect from their advisors.
One of the bigger challenges we’re seeing right now is how you express an AI use policy in such a way that any member of your legal team could explain it to a client. I think that’s still a big challenge for the industry.
I don’t think clients are all that happy if their attorney says, “I’ll have to get back to you about that,” because the attorney is likely using AI day to day. They should be able to explain the policy themselves. How else do you know they’re complying with it?
We’re doing some interesting work at the moment helping law firms and law departments take those first-version AI use policies, which are often very much “thou shalt not,” and turn them into more readable, appropriate governance positions that inform the conversation with clients.
If anyone wants to find out more about our advisory services, they can find me or Cheryl Wilson Griffin on LinkedIn, or visit legaltechnologyhub.com.
Marlene Gebauer (02:09)
Thank you, Sam. It is important to have an AI policy in plain language so people understand it.
Sam Moore (02:16)
Absolutely.
Greg Lambert (02:17)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Greg Lambert, flying solo this week. Marlene is out.
This week I am joined by Paul Lee. Paul’s the co-founder of Patlytics. Paul came to legal technology from the venture capital side, and he and his team have built Patlytics specifically around patent workflows, from drafting and prior art analysis to office actions and litigation.
Paul, welcome to The Geek in Review. Good to have you here.
Paul Lee (02:52)
Hey, Greg. Thanks for the invite. Awesome to be here.
Greg Lambert (02:55)
Paul, before we jump into the questions, I wanted to see if you would give us a quick elevator pitch on what Patlytics does and what problems your customers come to you to solve.
Paul Lee (03:09)
Yeah, absolutely.
Patlytics’ mission since day one, and that hasn’t changed since founding the company, has always been to create exceptional value in the IP ecosystem.
The way we do that is to transform and reduce transactional inefficiency, from the creation of IP and patents, from drafting to office actions on the prosecution side, to portfolio management of IP and patents for corporations and the law firms we work with, to litigation readiness and litigation itself.
The whole lifecycle is something I saw as an opportunity where we could have a massive impact. I think we’ve done right by our customers one by one by making sure we help as much as possible.
Greg Lambert (04:12)
Great. Thank you for doing that.
Let’s jump in. Paul, before building Patlytics, you took the time to learn the industry. I think you spoke with more than 100 IP professionals about how patent work gets done.
Having worked with my IP lawyers, I understand it’s highly detailed work. Sometimes it is low-margin work, so efficiencies built into the process are likely welcome by the community.
When you were having those conversations, what did they reveal about patent practice that convinced you Patlytics would be a valuable tool?
Paul Lee (04:58)
Yeah, that’s a great question, Greg.
It’s funny to think about where we are now and where we were when it was only an idea and we were starting up.
Right now, we work with around 55 percent of the Am Law 100 firms. We work with hundreds of corporations, from massive biotech and pharma companies to technology companies across Asia, North America, Europe, and elsewhere.
To understand the momentum we have now, you need to go back to where we were when things were starting.
For background, I do not have a patent attorney background. I was a venture capitalist, as you mentioned. One thing you do learn in venture capital is how to talk with a lot of people and potentially see patterns and insights.
In the early days, while figuring out whether this was an interesting opportunity, I spoke with more than 100 patent attorneys. Confidently, I would say many of them told me at the time that a lot of the processes were quite inefficient.
The fact that you might spend more than 10 hours writing a detailed specification for an application was wild.
Creating a claim chart used for IP litigation, with the level of effort, scrutiny, and back-and-forth involved, was something I saw firsthand. I thought that was pretty wild as well.
When you compound all of these use cases and processes across different parts of the IP lifecycle, I thought there would be tremendous value if we figured out how to create one platform to help with each of those workflows.
Greg Lambert (07:09)
I’m curious, did this idea come to you because you were working around patents, or how did you stumble across patents as a particular industry that needed this?
Paul Lee (07:24)
I first heard about patents back when I was a student. I was in Waterloo, and there was that massive Apple versus Samsung patent litigation battle happening.
At the time, as a student, I was shocked by the dollar value of all that.
Fast forward a couple of years. As a venture capitalist, you tend to invest in a lot of IP companies. Some of those companies spend a lot of money on trade secrets, patents, trademarks, and so on.
From my seat in venture capital, I was curious about how the bill could get so high on certain IP litigation matters. That got me curious.
My co-founder and CTO, Arthur, while he was building Magic, dealt with patent litigation and patent infringement matters.
Then I think the idea took off when my friend Bob Steinberg, who was chair of the IP litigation practice at Latham & Watkins, invited me over to his place and we discussed where he saw the opportunities.
It was a trifecta. It wasn’t one apple-hitting-the-ground kind of moment. The topic kept coming up, and that’s how we got started.
Greg Lambert (09:03)
I know a lot of startups are told to build into their budget the possibility of patent litigation or IP litigation at some point because many of them will face that.
I want to talk about something everyone I’ve talked to about Patlytics mentions, the fact that the USPTO is using Patlytics for its patent examiners.
What changes about the patent system when AI becomes part of the workflow on both the applicant side and the examiner side? What’s AI bringing into the overall workflow?
Paul Lee (09:50)
Greg, I need to preface this with the hundreds of pages I had to sign around confidentiality and the details of what we may or may not do with government agencies.
There are public records out there that people can search, but for this podcast, let me stay pretty high level about what we aim to do.
Greg Lambert (10:19)
Absolutely.
Paul Lee (10:20)
Going back to my original point, the patent and IP ecosystem is highly laborious and transactionally inefficient.
A good chunk of that exists at the corporate and law firm side because there are a lot of use cases, a lot of work, less budget, and the need to do more with less.
From the examination and government side, historically there have been opportunities and room for improvement as well.
We believe the right technology has the opportunity to add a lot of value to the whole flow. It has been rewarding for us to work with Google to make sure we’re on the right footing with everything we’re doing with government agencies.
What I would say is that USPTO examiners do a lot, and there’s a backlog associated with the amount of work involved.
The opportunity we have is to help make sure things remain high quality while helping with the backlog as much as possible.
Greg Lambert (11:56)
I want to talk about the way people are looking at AI through these broad AI platforms, the Harveys, Legoras, Westlaw, Lexis, and others.
I want to talk about being a specialist versus being a platform. What does a specialized patent platform like yours need to do to stand out and earn a permanent place in the law firm’s technology stack? What makes Patlytics shine?
Paul Lee (12:40)
It’s something I think about all the time, and something all of us as a team spend a lot of time thinking about.
The answer is relevance, Greg.
The question is whether we’re earning the right to be 10 times better than the status quo and provide a much better experience than generalized legal tools or the previous way of doing things.
That’s always the benchmark. The benchmark is what people did previously.
For us, we want to make sure the experience and outcomes derived from Patlytics are delightful and 10 times better.
I have a lot of respect for Harvey, Legora, and some of the names you mentioned. There’s always going to be a place for them. Some of the most interesting transformation in law is happening right now because of players like that.
With that said, what I hear over and over again is that the IP practice felt unloved.
Greg Lambert (14:03)
It’s those scientists. They never feel loved.
Paul Lee (14:08)
Exactly.
The IP practice feels the generalized tools, even with the ability to create your own workflows and make things more customized, never fully get there. They get to 80 or 85 percent.
We think of the patent ecosystem like an F1 race. It’s about getting to 99 percent.
When we started the company, I thought getting to 85 or 90 percent would be enough. That’s no longer the benchmark, and the benchmark moves year over year.
From a product perspective, we think about this and act on it constantly.
Compared with the existing flow for creating charts, creating applications, handling office actions, or pruning a portfolio, is the experience we’re putting in front of our customers genuinely 10 times more valuable than the previous way of doing things?
Greg Lambert (15:23)
We joke about scientists not feeling loved, but there are a lot of technical processes involved in setting up a patent filing.
You are moving into work such as patent drafting, office action responses, claim analysis, and even patent figures.
Where do you think technology should deliberately stop and require the patent lawyer to exercise judgment?
Or, even more broadly, where does it need to stop so the USPTO doesn’t look at the work and say AI did so much of this that it’s no longer eligible for a patent? What are you hearing in those areas?
Paul Lee (16:23)
I’m hearing a lot of things, but if I were to pinpoint one or two topics that keep coming up, number one is the demand to make sure human judgment is placed in the right spots.
At the end of the day, the practitioner still signs off, gives approval, and owns the outcome of the work product.
At Patlytics, we fundamentally believe there will always be a human-computer symbiosis. The human part will focus on judgment, while the AI and computer side helps develop the most useful ways to deliver those outcomes.
But the workflow will always require human judgment at the most important points.
That’s why tools in the past that said, “You don’t need to lift a finger. Click a button and the application gets submitted,” never worked out.
There are similar examples where people say, “Type something in, it drafts a claim chart, and you submit it.”
In fact, there has been some recent evidence of people being pulled into malpractice matters because of cases like that in the patent system.
We’re diligent and careful about this. It starts with the philosophy of human-computer symbiosis and making sure there are appropriate checkpoints across workflows, from prosecution to portfolio management to litigation.
I would add that client expectations are becoming higher as well.
Maybe it’s because of AI, or perhaps something else, but the client expectation is, “Use AI, but make sure the result is as good as it would have been if you spent many hours on it. We also expect it to be done in a fraction of the time, with even higher quality.”
That level of expectation puts law firms, and IP attorneys especially, under enormous pressure.
Going back to what you said, in intellectual property and patents specifically, each word matters so much.
Greg Lambert (19:31)
Absolutely.
Paul Lee (19:31)
I know in law each word matters. There are redlines and all of that.
But from patent drafting to patent litigation, this has given me a new sense of respect for the type of work patent attorneys do.
Greg Lambert (19:50)
Pulling on the thread of client demands, one of the biggest topics around AI in legal practice is who gets the AI dividend at the end of the day.
If AI turns work that once cost thousands of dollars in attorney time into something much faster, who ultimately captures the economic value of that AI dividend?
Paul Lee (20:31)
That’s a fascinating point, Greg.
I don’t think the market has figured that piece out yet. We’re moving through a lot of exploration and back-and-forth around this as we speak.
What I would say with certainty is that the transactional inefficiency of the past has come down.
Clients want collaboration. Law firms want faster collaboration done the right way. A lot of that has been achieved through what legal AI has become over the past couple of years.
When it comes to the economics of the value chain, clients want to capture 100 percent of that efficiency. Law firms want to capture as much of those efficiencies as possible as well.
I think the answer is meeting somewhere in the middle.
If the business model of law changes in a way where both sides benefit tremendously, that’s great, and there are examples of that happening right now.
We work with many Am Law 100 firms. One firm we work with is in the top five and historically was known for rarely using flat-rate arrangements.
Greg Lambert (22:10)
Right.
Paul Lee (22:12)
With AI, IP, and the way Patlytics is serving its offices and clients across these practice areas, an interesting business model has emerged.
On certain matters, we’ve seen more flat-rate business models where lawyers benefit because they are better able to predict how long something will take. Margins become more predictable, and lawyers are able to cycle through matters faster when those efficiencies are in place and demand exists.
On the other side, corporations are getting what they have wanted all along, which is greater predictability and lower bills.
I’d call that a win-win situation, and I think we’ll continue to see more of it.
Greg Lambert (23:11)
We talked briefly earlier about Patlytics being used both in-house and by outside counsel.
Where are you seeing that division of labor going? How much more upfront work is the in-house team doing before turning it over, and how has the division of who does what changed when it comes to patents?
Paul Lee (23:47)
For us at Patlytics, everything started with law firms in the first year. That’s where the demand was.
Take the patent prosecution practice as an example.
Historically, it has been one area where costs kept rising over the past 10 years, including inflation, while pricing hasn’t changed much. If anything, pricing has gone down a little.
You have costs going up on one side while the top line comes down. That leaves extremely thin margins. It’s a tough business.
What we’ve been able to accomplish is lowering the cost component in terms of hours and the way work gets done.
Even if the flat rate goes down a little, the margin calculation becomes much better for the prosecution practices we work with.
I’ve had partners at law firms tell me, “I had a record year because of Patlytics and the tooling we’ve put in place.”
I don’t want to get into specific numbers, but this particular partner was extremely happy with the outcome in his practice.
To your question about corporations and law firms and what work gets done where, corporations now have the ability to do more with less. They want to do things they simply didn’t have time for in the past.
I think Patlytics has helped in-house counsel do things such as running infringement analysis before a continuation, looking at the market more quickly before deciding which actions to take in a patent strategy, assessing patent pool M&A opportunities, and having greater confidence about where they’re headed for litigation readiness or potential cross-licensing.
They’re able to get down to claim construction and element-by-element analysis and, eventually, perhaps have greater insight ahead of a Markman hearing.
Greg Lambert (26:30)
Mm-hmm.
Paul Lee (26:31)
Those are more sophisticated outcomes that in-house counsel simply didn’t have much time for before.
We haven’t even talked about R&D, inventions, and engineering.
One of the biggest pain points for patent attorneys is dealing with many engineers submitting material. Attorneys have to parse through a lot of noise to find the signal.
That’s a laborious process.
We saw tremendous value in bringing all of these use cases, or “jobs to be done,” under one umbrella to provide a one-plus-one-equals-three type of experience.
Greg Lambert (27:23)
The more I’ve gotten into patents over the years, the more I realize it isn’t a straight line. There are so many things going back and forth.
Paul Lee (27:35)
Yeah. It’s like drawing many different circles, then drawing 20 zigzag lines, and saying, “Hey, figure it out.”
Greg Lambert (27:45)
An economist’s chart dream right there.
All right. A lot has changed, but is there something true for you, Patlytics, or the industry today that wasn’t true a year ago?
Paul Lee (28:05)
Greg, I’ll talk about patent attorneys and in-house attorneys who deal with IP.
A year ago, the sentiment was perhaps 20 percent optimists and 80 percent still somewhat skeptical.
Now I’d say it’s 95 percent optimists and 5 percent skeptical.
Within one year, we’ve gone from conversations where a firm partner or managing director said, “We’re not going to look into this for the next five or 10 years, if at all,” to many instances where I get a call saying, “Scratch what I said. Could we start something tomorrow?”
This is truly one of the most interesting times in legal. There’s a transformation happening that I think is generational and will leave a lot of second-order and third-order consequences.
Because the whole ecosystem is becoming AI-forward, people are now asking, “What else should we do?”
Greg Lambert (29:29)
Yeah.
Paul Lee (29:30)
There’s much more creativity, more pushing of limits, and more willingness to take bigger shots.
Greg Lambert (29:36)
That leads perfectly into my last question.
We ask all of our guests to pull out their crystal ball and peer into the future for us.
You’ve taken us from last year to this year. What challenges or changes do you think the industry is going to face that we should prepare for over the next two to three years?
Paul Lee (30:05)
That’s a tough one.
Greg Lambert (30:06)
Predictions are hard, especially when they’re about the future.
Paul Lee (30:10)
Yeah. I hope I’m right, because five years from now I want to look back at this.
Greg Lambert (30:15)
We’ll bring you back in a year and see how well you’re doing.
Paul Lee (30:19)
Greg, I’d say there’s a lot of buzz around AI right now and the use of AI, sometimes without enough intention or purpose.
I think in the future its use will become much more intentional.
One thing I deal with right now is creating a layer of transparency so costs don’t go through the roof from an LLM perspective, both for suppliers and for us as a customer of the main suppliers fueling the entire system, the LLMs.
I don’t think the level of growth we’ve seen around LLMs is going to change. People will keep using them and become more intentional about their use, while making sure AI continues to prove value.
But the economics of AI, from the LLM to the end customer, are where I expect to see even more change.
From the venture side, the market has often viewed AI as great because, when you invest in a company, the more people use the product, the more revenue the company generates.
The flaw in that logic is that customers have a budget for this kind of thing. There’s always a budget.
Even if your business model depends on consumption continuing to grow, a budget is a budget. There is only so much a customer will spend.
I think we’re going to deal with that type of correction pretty soon.
It will increasingly come down to proving value and ROI in a meaningful way.
Greg Lambert (32:14)
I think we’ve come to the end of our free ride when it comes to LLMs, and now we’re looking at consumption-based pricing.
You want to make sure you’re not spending $50,000 pursuing a $5,000 matter.
I imagine on the business side you don’t have a “max token” scoreboard anymore. It’s more about being smart about how you’re using the technology.
Paul Lee (32:47)
Yeah. Token-maxing is likely not going to be a thing anymore, unless the LLM economics make sense for the whole value chain.
Greg Lambert (32:57)
I’m waiting for it to go the way of text messaging.
We’ll pay 10 cents a message for a couple of years, then hopefully you buy one plan and we go back to unlimited.
Paul Lee (33:06)
I remember that.
Being a Waterloo guy, that’s why BBM was so awesome. Did you have a BlackBerry back in the day?
Greg Lambert (33:13)
I did not.
Somehow I missed that and went straight from my Palm Pre to an iPhone.
Paul Lee (33:25)
That’s even better.
Greg Lambert (33:29)
Paul Lee from Patlytics, it’s been a pleasure having you on here and geeking out. I appreciate you being here.
Paul Lee (33:38)
Yeah, I had a lot of fun, Greg. Thanks for the invite.
Greg Lambert (33:41)
Absolutely.
And thanks to everyone who’s listening. If you enjoyed the show, please share it with a colleague. I’d love to hear from you on LinkedIn or Substack.
Paul, where’s the best place for people to learn more about Patlytics or the things you’re up to?
Paul Lee (33:59)
Definitely LinkedIn. Feel free to add me.
I’m in conversation with a lot of forward-thinking IP leaders and innovation folks from corporations and law firms, and I’d love to chat.
Also, feel free to give us a shout through the Patlytics homepage at patlytics.ai.
Greg Lambert (34:28)
All right. Thanks again.
And as always, the music you hear is from our friend Jerry David DeCicca.
Thank you, and talk to you later.
Paul Lee (34:37)
See you, Greg.