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28th October 2022

Understanding Patent Value

Season 2 Episode 10

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Episode 10 in Season 2 of the Cipher Vision Podcast series features Jay Yonamine, Product Manager, GEO at Google.

He shared with us his perspective on patent value, his thoughts on existing valuation methods and what the future could look like, given the possibilities and limitations of ML and AI.

He chats to the hosts of the Cipher Vision podcast, who are:

We take a deep-dive into the data that underpins Jay’s perspective, shared in the podcast.

Conversation highlights

On bringing data scientists into the patent world

There are definitely a couple of folks scattered in industry who are essentially embedded within a patent or a legal organisation, or full-time doing data science work, but it’s rare. I don’t know of any dedicated teams in industry. 

I think when you just look at what you can do with advanced analytics, advanced machine learning, artificial intelligence, it’s tremendous, and the potential impact is massive. I think every team should be thinking about this.

I would really make sure and highly advise doing two things. First, making sure that senior leadership is really bought into the idea of bringing on someone to do this type of work, knowing that it may disrupt the status quo, may provide a new way of looking at things. It may just be a little bit of a culture shock, both to the data science person or team coming in, as well as the existing patent counsel and legal ops folks. 

Secondly, I would advise if you’re wanting to make that jump to bring someone on, get someone senior, I think a lot of folks that I talked to want to test the waters, maybe hire one junior person, and I think that’s just not going to work. It’s too complex of a substantive domain. 

The technology is strong, but it’s not super well understood by everybody yet, and you’re going to face hurdles, I think you really want someone who sees it, who’s tested, who’s been through the wringer and to really give yourself a fighting chance.

Why is patent value important?

I think everybody involved in the patent ecosystem, either implicitly or explicitly wants some sense of what their patents are worth, and what other people’s patents are worth. And because of that understanding, an estimation of value is critical in essentially all transactions, right? 

It’s critical in how much we’re willing to pay for a licence or how much we’re willing to pay or accept in a portfolio transaction, how aggressively you’re willing to fight in the litigation, how the settlement discussions go, how you navigate damages experts. So I think the idea of a value is just ubiquitous within almost every aspect of the patenting ecosystem.

Can you adopt the same approach as other asset classes?

There are very accurate, well understood ways to generate automated pricing. So houses are a great example, right? Most people are probably familiar with Zillow, and they have this way where you enter in some information about a house and you get a price and it’s pretty good. Even when it’s bad, you’re maybe off by 10%. 

Patents are different. If I’m a large operating company, and I have, you know, a 20,000 patent portfolio, I may not even know about one of my patents. I may never do anything with it. The inventors may have left the company, I may just be paying the maintenance fees in perpetuity and do nothing with it, they may be essentially worth zero to me.

If that patent disappeared, my portfolio might not even know. If you gave that same asset to a patent assertion entity, or a law firm that specialises in patent litigation, they may be able to extract tens of thousands, millions of dollars in licencing revenue from that patent if they pursue a revenue generating model very aggressively. 

Who owns a patent and what they do with it is fundamentally important to the value.

Further limitations with this approach

Almost every machine learning problem needs training data, whether it’s real or whether it’s synthetic, generated programmatically. And patents, we just don’t have it right? 

How much the patent was sold for and how much licensing revenue a specific asset has generated, or what a settlement amount has been for an individual asset, that data is just extremely sensitive, no one shares it. 

The distribution of patent value is so extreme and so skewed, that the prediction of value still likely wouldn’t be important on an asset by asset basis.

Thinking contextually about value

A large operating company that doesn’t have an aggressive licencing programme, clearly isn’t trying to generate revenue from their portfolio. So why do they have a patent portfolio? Well, defensive purposes, right? Being able to counter assert.

And as needed, reputational value, it’d be seen as a hub of innovation, right? A recruiting tool, where engineers who are interested in filing patents and demonstrating in a tangible way that they’re innovating in the space may want to go to a company that has a well established patenting programme. 

The benefit of AI 

At a large operating company with a large portfolio, you may have 10,000 assets, 20,000 assets, 50,000 or 100,000. When you’re thinking about analysing the portfolio, comparing your portfolio against other companies portfolios, thinking about long-term strategic portfolio management, it’s just not feasible to rely on human review intensively for that many assets.

On the other side of the equation, if you’re a small firm doing patent enforcement, the primary mode of analysis is going to be manual, eyes on, writing claim charts, getting second opinions on the claim charts, haggling over every word and specific office action. So it’s a much more manual approach. But that manual approach can be greatly enhanced, if you understand how to use automation.

​​What is Ciphers approach?

What we’ve done with our new paper, which is called The contribution of patents to enterprise value, is to take it one stage further. To use fundamental finance analysis which says, if your portfolio impacts cash flow, positively or negatively, then you can take one of Jay’s traditional models and the impact on cash flow to calculate that contribution made by that portfolio to that owner on the enterprise value.

On what the future holds

It’s probably quite likely that five years from now, ten years from now, we won’t even be able to predict or have any sense of the innovation in the AI / ML space that will be coming in the next decade. 

And companies and practitioners who do their best to stay up to date the best they can, and are going to see their value to an org and their value in industry to disproportionately outpace the folks who just aren’t staying up to speed with the latest technologies. Because that technology is becoming so powerful.

And so if there’s any one single point of advice I would give is at least have somebody in your org who’s dedicated and committed to staying up to date on the latest technology and advancements in the AI/ ML space. And ideally, someone who actually has hands on practitioner experience, because it’s just going to be such a differentiating skill set in years to come. 

Jay’s Key Takeaway

It’s easy to understand how to use AI enabled tools, even in the patent ecosystem. It’s hard to understand how they actually work, and how the underlying technology works. 

If you can take the time and have the energy to really understand how the technology actually works, the core ML at the guts of the system, you’ll have a disproportionate advantage over the folks who know how to use the tools but don’t fundamentally understand them. 

And so I would encourage folks, if there’s any inkling of interest in understanding how these ML algorithms actually work, that are part of all these tools, go for it. Take a Coursera course, watch the YouTube videos, write some code, it’ll pay dividends.

I think you’ll have a profoundly different understanding of how to leverage technology in ways that should generate a tonne of value in your day to day operations.

Nigel’s Key Takeaway

Patents are how the world’s technology companies choose to protect over $1.5 trillion of investment every year, at a cost of over $40 billion a year on the patents alone. With that quantity of input, it’s not surprising that people are asking, what’s the value of patents? 

And first, Jay’s research suggests that you can’t ask data science to algorithmically value individual patents but in line with the very best of academic research, it forces us to reflect on whether we’re asking the right question. 

Cipher’s contribution to the discussion builds on two fundamentals. First, that the value of patents depends on who owns them, when and why. Secondly, there are many situations whether it’s better to assess the portfolio as a whole, rather than taking it apart brick by brick. 

Now is the time to recognise and quantify the significant contribution patents make to the enterprise value of their owners.

 

The contribution of patents to enterprise value

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Read our detailed article

This is a detailed new report outlining a groundbreaking approach to patent valuation.

Nigel Swycher (Cipher CEO) and Steve Harris (CTO) describe a valuation methodology for calculating the contribution that an entire portfolio makes to the overall value of a company.

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