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Innovation

Patents and Licensing: Why It Matters

Jud Cary
Deputy General Counsel

Oct 18, 2018

Hi, Jud here. A lawyer, writing a blog. Why? Because I’ve been asked so many times about how patents and licensing works that I decided to write it all down as an element in our CableLabs Innovation Series. I tend to spend a lot of my time (understatement!) working on both patents and licensing. So, here it goes.

In this blog, I’ll use the DOCSIS cable modem specification as an example because it’s foundational to broadband -- and broadband is life, right? Those of you who’ve been around since the early days of DOCSIS, know that it’s evolved in a lot of different ways. When DOCSIS was developed it was a method for delivering Internet speeds of 37 Mbps downstream (toward homes) and 8 Mbps upstream (toward the Internet.) These days, devices based on the newest DOCSIS flavors can blast data at a rate of 10 Gigabits per second, both ways.

But that didn’t just happen. It took process, development, governance, patents and licenses.

Why are Patents Important?

Patents are actually called out in the original U.S Constitution!

Patents and Licensing

In theory and in practice, a patent protects inventive ideas for a specified and exclusive period of time. In return, the inventor agrees to disclose the invention to the public. Patents advance the general knowledge about the invention to the public. After the period of exclusivity, the public is free to use the invention to advance the technology further. As you can probably imagine, patents abound in complex technology areas such as telecommunications.

Why is Licensing Important?

Almost all standards and specifications carry an associated patent licensing requirement. That is to say, if you agree to work on and contribute to a specification, you also agree to license your patents that are essential to the implementation of that specification. This reality exists to prevent parties from inserting patented technology into a specification, then later say “gotcha!” by alleging the existence of patents that obviate your products, even though they conform to the specification. In legal-speak, this is known as a “patent hold-up.” If you’re thinking “hold up” as in “stick’’em up!” you’re in the zone.

Patents and Licensing Importance

Ideally, for the sake of technological cooperation, interoperability, and advancements in innovation, players in the industry adhere to a patent licensing policy that sets up an environment of “leave your guns at the door.” In essence, manufacturers can then implement the specifications, without the fear of being sued for patent infringement by other DOCSIS manufacturers.

Again, using DOCSIS as an example, this is precisely what its licensing agreement, arranged long ago by CableLabs, provides: all signators agree to license their standard essential patents (SEPs) to CableLabs, including the right for CableLabs to the sublicense the SEPs to all other licensed signators - all on a royalty-free basis.

How Licensing Benefits Innovation

Licensing arrangements also create a nurturing environment for technological development, within which all parties can innovate, because all are free to build on everyone else’s technology -- again, without fear of later having to pony up unforeseen patent royalties.

The DOCSIS licensing arrangement fostered by CableLabs in the mid-’90s was and is a significant catalyst in the development, implementation, and widespread adoption of the DOCSIS platform. In all, this gun-free, royalty-free DOCSIS licensing environment consists of more than 200 signators!  (In licensing terms, that’s a lot.)

Taking another approach: RAND

Many other typical telecommunications standards (e.g., 3GPP, Wi-Fi and DSL) were generally developed under a “reasonable and non-discriminatory,” or “RAND” patent policy. A RAND patent policy allows participants to collect “reasonable” royalties on patents required to implement the standard.

It follows that manufacturers sometimes sue each other if they disagree on what is “reasonable.”  This can add a significant cost, as well as risk and uncertainty, to the development and deployment of these technologies. In case this isn’t patently obvious (see what I did there?), a RAND royalty rate is always greater than a royalty-free royalty rate! (And under the DOCSIS licensing arrangement, the RAND royalty is always $0.)

How Licensing Impacts the Bottom Line

How does this translate to the real-world bottom line? Since its inception, over 2.5 billion DOCSIS-based cable modems and gateways have been deployed. All with no royalties for patents necessary to implement the DOCSIS specification.

Cumulative number of cable modems

As the DOCSIS technology evolved to add additional features, faster speeds, and improved technology, so did the “patent pool” set up by the license arrangement expand. Think about it this way: if there were 1,611 technical requirements in DOCSIS version 1.0, there are 5,758 requirements in DOCSIS 3.1. That’s a pretty big expansion, and a lot of intellectual property, all protected from infringement litigation.

Number of DOCSIS Requirements

Licensing Arrangement as the Unsung Hero

It’s true that probably only lawyers think licensing is sexy, and that’s ok. But know that even if licensing doesn’t necessarily “wow” you with revolutionary advancements in cable/internet/wireless technology (see our Full Duplex DOCSIS and Coherent Optics technology pages), it’s still an important mechanism for business as usual.

From this lawyer’s perspective, the CableLabs’ royalty-free DOCSIS licensing arrangement has been the unsung hero for the last 20+ years, in terms of fostering implementation and rapid technological innovation, reducing risk, advancing adoption, and expanding deployment worldwide. And, as DOCSIS continues to be the workhorse for all things broadband, it is legally positioned to succeed.

That’s it! Feel free to contact me for more information, and thanks for reading!


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Legal

Should Artificial Intelligence Practice Law?

Simon Krauss
Deputy General Counsel

Jun 27, 2018

As in many other professions, artificial intelligence (AI) has been making inroads into the legal profession. A service called Donotpay uses AI to defeat parking tickets and arrange flight refunds. Morgan Stanley reduced its legal staff and now uses AI to perform 360,000 hours of contract review in seconds and a number of legal services can conduct legal research (e.g., Ross Intelligence), perform contract analysis (e.g., Kira Systems, LawGeex, and help develop legal arguments in litigation (e.g., Case Text).

Many of these legal AI companies are just a few years old; clearly, there are more AI legal services to come. Current laws allow only humans that passed a bar exam to practice law. But if non-humans could practice law, should we have AI lawyers? The answer may depend on how we want our legal analysis performed.

AI Thinking

Today, when people talk about AI, they often refer to machine learning. Machine learning has been around for many years, but because it is computationally intensive, it has not been widely adopted until more recently. In years past, if you wanted a computer to perform an operation, you had to write the code that told the computer what to do step-by-step. If you wanted a computer to identify cat pictures, you had to code into the computer the visual elements that make up a cat, and the computer would match what it “saw” with those visual elements to identify a cat.

With machine learning, you provide the computer with a model that can learn what a cat looks like and then let the computer review millions of cat (and non-cat) pictures, stimulating the model when it correctly discerns a cat, and correcting it when it doesn’t properly identify a cat. Note that we have no idea how the computer structured the data it used in identifying a cat—just the results of the identification. The upshot is that the computer develops a probabilistic model of what a cat looks like, such as “if it has pointy ears, is furry, and has eyes that can penetrate your soul, there is a 95 percent chance that it is a cat.” And there is room for error. I’ve known people who fit that cat description. We all have.

Lawyer Thinking

If a lawyer applies legal reasoning to identifying cat pictures, that lawyer will become well versed in the legal requirements as to what pictorial elements (when taken together) make up a cat picture. The lawyer will then look at a proposed cat picture and review each of the elements in the picture as it relates to each of the legally cited elements that make up a cat and come up with a statement like, “Because the picture shows an entity with pointy ears, fur, and soul-penetrating eyes, this leads to the conclusion that this is a picture of a cat.”

In machine learning, the room for error does not lie in the probability of the correctness of the legally cited cat elements to the proposed cat picture. The room for error is in the lawyer’s interpretation of the cat elements as they relate to the proposed cat picture. This is because the lawyer is using a causal analysis to come to his or her conclusion—unlike AI, which uses probability. Law is causal. To win in a personal injury or contracts case, the plaintiff needs to show that a breach of duty or contractual performance caused damages.

For criminal cases, the prosecutor needs to demonstrate that a person with a certain mental intent took physical actions that caused a violation of law. Probability appears in the law only when it comes to picking the winner in a court case. In civil cases, the plaintiff wins with “a preponderance of the evidence” (51 percent or better). If it is a criminal case, the prosecution wins if the judge or jury is convinced “beyond a reasonable doubt” (roughly 98 percent or better). Unlike in machine learning, probability is used to determine the success of the causal reasoning, and is not used in place of causal reasoning.

Lawyer or Machine?

Whether a trial hinges on a causal or probabilistic analysis may seem like a philosophical exercise devoid of any practical impact. It’s not. A causal analysis looks at causation. A probabilistic analysis looks at correlation. Correlation does not equal causation. For example, just because there is a strong correlation between an increase in ice cream sales and an increase in murders doesn’t mean you should start cleaning out your freezer.

I don’t think we want legal analysis to change from causation to correlation, so until machine learning can manage a true causal analysis, I don’t think we want AI acting like lawyers. However, AI is still good at a lot of other things at Kyrio and CableLabs. Subscribe to our blog to learn more about what we are working on in the field of AI at CableLabs and Kyrio.


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