Although artificial intelligence (AI) has been around for some
time, the rapid development and widespread application of AI
technology over the last 10 years has resulted in a global AI
‘patent boom’.
This increase in patent activity has brought about questions
around the patentability of AI inventions as well as debate as to
whether patenting is the most suitable intellectual property (IP)
protection strategy.
In the fourth instalment of the ‘Tech Transfer and
Innovation in the GCC’ webinar series, our speakers
discuss:
- R&D and IP trends for emerging tech (AI, blockchain,
FinTech, NFTs etc.) - How to identify and evaluate software-implemented inventions
(with a focus on AI) - IP protection considerations for AI-related inventions
- Patenting considerations for software and AI-related
inventions - Protecting your data assets
- The challenges in commercialising software and AI-related
inventions - Takeaways for IP managers
self
Transcript
Tamara El-Shibib: So, good afternoon everyone.
My name is Tamara El-Shibib. I am a senior patent and tech transfer
consultant at Gowling WLG based in Dubai.
Thank you for joining the fourth session of our webinar series
on tech transfer and innovation. Before we start with panel
introductions, I wanted to introduce the webinar series for those
who are new to it. So, the purpose of this webinar series is to
discuss different topics within the umbrella of IP, tech transfer
and innovation. There are a lot of information gaps in this region
when it comes to these topics, so the focus is to really share
knowledge and best practices in these areas but also to discuss the
challenges and what we can learn from them.
For today’s discussion, we are focusing on IP strategies for
managing software-implemented innovations. We are going to focus on
AI because there is a massive surge in AI-based innovations. And
frankly, there is a lot to learn, especially if you do not come
from an AI background like me. So, having the right IP strategy is
key for getting your technology to market and giving it the best
chance of successfully getting to market as well.
Before we dive into the topics, I wanted to say there is an
opportunity for questions at the end of the session. So, please
type in into the chat, and we will get to them in the last five or
ten minutes of the session. You will also be prompted to complete a
short survey at the end of the session. We would appreciate any
feedback you can give us on the session. If there are any
particular topics you would like us to cover in future, please also
let us know.
With that, I am going to turn to the panel and ask them to
introduce themselves. One of our panellists is actually having a
bit of a tech issue joining the session so that is Matt Hervey, and
we will get him in as soon as we can. For now, left on my screen is
Vivian. Vivian, could you give us a little introduction to
yourself?
Vivian Wei Cheng: Hi good morning, good
afternoon and good evening everybody. My name is Vivian. I am a
Singapore patent attorney and head the patent and industrial design
group at Jurisasia LLC, which is the Singapore office and South
East Asia arm of the global law firm, Gowling WLG. I have a life
science and clinical medicine research background, and my daily job
involves drafting and prosecuting patent applications domestically
and in foreign jurisdictions as well as providing strategic advice
on IP portfolio and risk management as well as litigation support
and it is an honour for me to present here today as a
panellist.
Tamara: Thank you, Vivian. It is good to have
you. Next on my screen is Sean. Sean, can you give us an intro?
Sean Flanagan: Thank you. Good afternoon, I am
Sean Flanagan. I am the director of technology and
commercialisation at King Abdullah University of Science and
Technology or KAUST here in Saudi Arabia. Prior to that, I was in
the same role in the National University of Singapore, where I also
served the role of as director of commercialisation and IP for AI
Singapore, a national level AI research initiative. I have been in
the University tech transfer space for over 20 years, and I am
pleased to be sharing whatever insights I have today.
Tamara: Thank you Sean. We are pleased to have
you. Fawaz, could you give us an introduction?
Fawaz Al Qahtani: Good morning, good afternoon,
good evening everyone. My name is Fawaz Al Qahtani. I am acting
director of Tech transfer of research at Hamad Bin Khalifa
University, Qatar Foundation. I have been in the tech transfer
industry since 2017. And, prior to this experience, I was a
research scientist at Texas A&M for more than eight years. I
hold a PhD in electrical and computer engineering. Basically, my
daily activities that I do is just to oversee the IP activities
from IP capture until the commercialisation, so that is basically
what we do. Thank you.
Tamara: Thank you Fawaz. So today’s
discussion is going to focus on these topics. We have got about an
hour. So, we will try our best to cover all of them. This is sort
of an informal discussion, so we will sort of follow the flow of
the discussion. But basically, we are going to discuss trends in
R&D and IP for merging tech with a focus on AI. Processes for
identifying and evaluating software and AI inventions. We are going
to discuss IP protection considerations for software and AI
innovations and then patenting considerations for software in AI
because these are treated differently to traditional tech. We will
also cover data assets so, these can also be really valuable when
you are dealing with AI. How can you protect your data assets? And
then, we will briefly discuss the challenges and commercialising
software and AI-related inventions. There is a lot to talk about
when it comes to the challenges, so I think we will need a separate
session on that, but let’s just dive straight in.
When it comes to emerging high-tech areas so they are broadly
categorised as these five, and there is a lot of IP development in
these areas particularly because a lot of these are sort of
platform technologies. And you can apply them across various
industries. I think the fastest growing in terms of these top five
is definitely AI and machine learning. And there is a lot of stats
sort of backing that up, which we will cover in a second, but
before we do that, I want to turn to the panel.
Sorry, hi Matt, are you there?
Matt Hervey: My camera is showing me upside
down, let me just see.
Tamara: No worries. It is also showing you with
my name as well, but I’m just going to turn back to introduce
you. Hi everyone, just to get Matt up to speed. Matt Hervey is a
partner with Gowling and the head of our AI team based in the UK,
and Matt, we are just going through the topics for today, and then
I will loop you back in.
Matt: Fantastic.
Tamara: So turning to the panellists, I want to
start first by asking Sean and Fawaz in terms of R&D, what are
the trends that you are seeing when it comes to these areas? Sean,
do you want to take that first?
Sean: Sure, thanks very much. I think
definitely here in KAUST we are seeing a tremendous interest in
approaching almost all levels of research, whether it is desert
agriculture or biology; any of our traditional areas of research
are seeing an ever-increasing interest in developing AI or ML
solutions to go alongside of those technologies. We are seeing an
increased utilisation of the data that has been collected through
the physical manifestation of research often sensors or devices for
collecting information, and how we can use AI and ML in order to
process those mountains of data in order to determine what the
patterns are. So, what we are seeing is there is definitely an
uptake in the amount of software-enabled technologies or
software-enabled hardware. We are definitely seeing a lot of
standalone software, especially in the AI institute. And we are
seeing an ever-increasing interest in adapting new solutions in AI
and ML for historical approaches to, whether it’s drug
discovery or any other analysis that might have otherwise taken a
lot of significant repetitive research.
Tamara: And Fawaz, would you say that it’s
the same for QF as well?
Fawaz: Yeah, it is, exactly. I think right now,
for example, last year, we had more than 150 inventions
disclosures, so by looking like into the whole spectrum of IPs, we
identified more than 60 to 65% of the whole activities, they are in
the domain of AI. Some of the AI, like in the health, lots of even
material science right now, I see lots of activities they are using
AI, which is I mean this is for the first time I see it like this
year, and I see lots of involvement and a lot of engagement. And I
see even in the industry, there are like they want to explore more
about getting AI into the domain of material science. And you know
even if you looked at the AI last year, it is completely different
from this year. I mean, we have been discussing within the tech
transfer and within the research institute for the last three weeks
about some of the areas that we have been doing research for the
last seven or eight years, after the introduction of large language
models like chat GPT and the multimodal GPT4. So, right now, we are
going through the different workshops, talking to people, to the
scientists, and we are trying to explore all these. Shall we do
exactly the same as we do before or things are changing? So, I
think the industry is moving so fast, but definitely, AI, it is
getting into all of the disciplines, it is getting into all the
portfolios. We have Bayou, and we have health, we have Bayou, we
have energy and environment, and we have computing. And I can see
lots of the AI machine learning they are getting into these
different disciplines, and I believe things will be changing in the
future in terms of AI involvement in these areas.
Tamara: And I think the numbers here completely
correlated with what you are saying. So, there has also been a bit
of an AI patent boom over the last ten years, and a lot of that is
sort of focused on machine learning as well. So, here is just to
give the audience a little bit of background into what is going on
with IP with AI in the IP world. There has been sort of over
340,000 applications according to WIPO, that relate to AI, and half
of those have been filed in the last ten years. When it comes to
patent applicants, the majority of them are based in Japan, US,
China and the top five AI patent finders as you could guess it is
sort of the big tech giants. In terms of university research, a lot
of that is coming from China. So, they are leading the way in terms
of scientific publications and leading the way in terms of
university applicants as well when it comes to AI.
Generally speaking, and before I turn to Vivian and Matt, my
understanding is when you are talking about AI innovations, these
can be grouped into three areas. Either techniques used in AI, like
machine learning, or its functional applications of AI, so natural
language processing or computer vision and then there is the
application of AI in different areas. So, like I said, a third of
patent applications that are filed relate to machine learning. I am
going to turn to you, Matt. Is that one of the trends you are
seeing as well when it comes to AI, is it machine learning that is
taking over in terms of IP activity?
Matt: Yeah, 100%. So, AI has been a search area
since the 1940s and for the majority of that time, people would
presume what is now known as an expert system. Whereas, humans
trying to code a machine to replicate a human task. But really
since about 2010 the artificial neural network as a form of machine
learning really re-established the concept that machines can teach
themselves using training data, how to automate certain tasks. And
among those the two you have pulled out the natural language
processing computer vision have been incredibly significant
commercially because it allows you to automate a huge range of
human tasks, everything from autonomous vehicles to voice
assistance. And it is really that technology that has made AI
appear on the industrial strategy of almost every major economy.
And the only thing I would say about IP trends, in general, is you
are absolutely right, vast increases in IP filings. It has
certainly doubled in the US in about ten years. I read this morning
it is 6% of all filings in India relate to AI at the moment. I
think the other big trend is trade secrets, huge increase there. So
litigation in the States is up 14% annually over the last decade,
so very significant. And then the other area that has become
superhot now is generative AI, that is AI to create images and
texts and code and just about anything you can think of, and that
is not only becoming significant so open AI garnered 100 million
users and claimed to be the fastest growing technology in terms of
adoption, ever. But, it has also sparked litigation. So, in the US
and the UK, we are seeing the first litigation about generative AI
in terms of copyright infringement.
Tamara: I am going to come back to the point
about trade secrets because I think that is really interesting that
there is a rise in trade secret litigation at the same time as we
are having this AI boom as well. Before I do, Vivian, I’m going
to come you. Are you seeing similar trends, I guess?
Vivian: Yes, definitely. Definitely. We are
definitely seeing very rapid developments in this area, especially
covering Metaverse, NFTs block chain technologies. And I would like
to mention something interesting that, even in the trademark field
we have seen a surge of metaverse and web 3 trademark filing in
recent years as many companies would file applications for marks
for use in the virtual environments and in relation to NFTs. So, in
Singapore, there is an example concerning the company Nike. So back
in the 1990s, Nike obtained a single trademark registration for
that famous swoosh logo for footwear, clothing and headgear. But
recently, they have filed and obtained acceptance for another
single trademark application for the same logo, but it covers
various virtual goods and services. So, I think this shows that
they really expect more local users of the Metaverse, which is kind
of a promising sign for other businesses to start mapping out the
plans for Metaverse presence.
Then in the patent field, I would say that increasingly the life
science companies are also exploring the potential of AI. So, to
name a few applications of AI in life science, AI has been used to
assist drug discovery by identifying potential drug candidates. It
has also been used to diagnose disease using medical images, and it
has also been used for AI-driven robotic surgeons, intelligent drug
applicators or even intelligent logistics such as predicting demand
peaks and adapting the supply chain accordingly. So, I would say
that in Singapore generally, especially in life science area, and
assuming other areas, AI is a hot topic, and everybody is trying to
grasp the benefits from it.
Tamara: Yeah, when it comes to the UAE, it is
quite similar as well. A lot of the work that we are seeing is also
AI-related across different industries as well, health, ag-tech,
basically across the industries that are relevant in the region as
well. So, definitely mirroring that trend. When it comes to
managing sort of AI inventions, for me personally, I see a lot of
developers working on solutions using AI. But what is the first
step in terms of evaluating whether you have got an AI solution
versus an AI innovation? I am going to turn to Sean and ask you
what are the processes that you have internally to evaluate when
you have got sort of a new AI or software-related invention?
Sean: Well, the first thing we had to do was
develop an entire software disclosure system. We saw that with the
increasing numbers of non-patent related discoveries that were
taking place, we were not really prepared for handling that kind of
influx and while it collects a lot of the same data it critically
asks some important questions about the origination of the concept
and what tools were used in order to develop the solution. I agree,
we are seeing applications more than entirely new algorithms. We
are seeing approaches to using and understanding an adaptation, and
that creates a lot of opportunity for contamination of the original
work by outside source, whether it is a shared code or code that is
taken from or data that is taken from a source that is not
proprietary to the parties that are disposing it. So, our approach
is to gather as much information as we possibly can about the
software that is being disclosed. Understand what the pathway
through to the anticipated market because again, because there are
solutions driven, we are seeing that the anticipation is a very
fast market. So, we do not have the traditional timelines that we
can with patents, in order to evaluate which way we are going to
go. Because often we are looking at, do we have the right material
in order to move through to the venture funding to get through that
first mover advantage and a patent often is not going to be the
right pathway for these. So, I agree, we are taking trade secret a
lot more seriously which is kind of the antithesis of the
university, and we were in the business of not keeping secrets. We
are about publication. But increasingly, as some of these tools,
especially for student start-ups, are coming out, we are having to
find ways in order to make it available. So, I mean, I think it is
about collecting the most information you have about how the
solution was derived and then taking a critical evaluation as to
what they intend to do with it, so that it can survive whatever
diligence is going to come thereafter.
Tamara: Yeah, I think what you mentioned as
well about sort of figuring out the origins as well is really key.
We have seen a few cases where for example, it is very common to
use open-source software because it helps you save development time
and cost, but sometimes some of this open-source software is under
or has restrictions relating to patent ownership or commercially
use, especially if it like a subscription-based software that you
are using. So, I think it is really key that before you invest time
in trying to patent or commercialise something, you need to
understand where it has come from and what you can do with any
derivative work. Fawaz, I am going to turn to you quickly. Do you
have a similar process in place as well?
Fawaz: I mean after capturing the new, we have
the new invention disclosure, we try to look at the technology
itself. Sometimes we gather more information, and for me and the
office, we have these inventor meetings. We sit with them, we
discuss with them. So what I realise there are two types of AI.
When I discuss with the guys who come in from the AI background, it
is different from the solution that is coming from a different,
like from health they are using AI, and in health and environment
and telecoms they are coming from AI, and I can see there are lots
of activities they do is off the shelf type of techniques. And
usually, we try to discuss with them, hey guys this is not
patentable subject matter. But in the meanwhile, they are providing
great solutions, so we tell them, hey guys since it has a great
commercial value, then I think it is better off to have it as a
trade secret, and then from there, we can further develop the
technology to take it to the market. And then, for the AI solution,
for example, that has come from the computing research institute,
you know, machine learning is just only one block of the whole
end-to-end solution. So you have the pre-processing, you have
machine learning, and you have even going to the cloud coming back
there are lots of ingredients. We can tell from there if this has
the potential to be patented or no. And honestly, we are
commercialisation driven, and since we are commercialisation
driven, we need to make sure that anything that will be disclosed
later on when we have a patent granted or patent application, you
know this type of algorithm, it is very hard to police. That is
when we miss the value. So as I said, the process is we meet, we
gather lots of information, and then we decide what is the best
scenario, based on the future activities, if this will be
commercialised or not.
Tamara: That makes sense. OK, turning to Matt
next. In terms of protecting AI inventions, what would you say are
the elements that can be protected?
Matt: Well, potentially everything depending on
which strategy you approach. So, obviously, the raw data or the
training data or the extracted data. It might be in the form of a
copyright work, so it could be your using images or academic
articles, and they have copyright, and there the real issue is
whether you are subject to text and data mining exemptions, which
allow people to mine your copyright works without a licence, and
that is definitely an issue which is going before the Courts in the
US and the UK at the moment. And it is also an area where various
Governments are looking whether they need to change the law in
those respects. If it is not a copyright work, for data you are
very much looking at trade secrets, and we can talk about in more
detail later if you wish. Then for code, if it is human written it
is protected by copyright. So if you have a human who has written
the learning algorithm, you have a framework by which the machine
is going to learn, and that will be copyright. But copyright is a
limited protection, so it protects the expression how you have
written the code, with choice of phrasing as well, but its code,
not the underlying principle so, it does not create any monopoly to
the methods or the ideas. And indeed in the EU and the UK, you are
now allowed to prevent lawful uses of your software from reverse
engineering, a term to that effect would be void. So where you are
looking is to the extent you are not protecting, you want to
copyright, you either need to fall under patent law protection. And
obviously, patent law excludes computer programmes as such but does
allow some software essentially to be patented where it improves
the way an AI works or where it has some sort of real-world
application outside of the AI. Then you have the stuff generated by
the machine itself, either the model or the algorithm it uses to
make its predictions or the outputs themselves. Generally speaking,
in most jurisdictions, stuff generated by an AI isn’t
protectable as a form of intellectual property. There are some
exceptions for the copyright. The UK is an exception there, for
example, and also designs if they are outwards.
So, we are falling back there generally where IP does not reach
on trade secrets and practical measures and contractual measures to
keep your proprietary techniques and innovations to yourself.
Tamara: Are you seeing sort of clients in for
trade secret protection more than patents when it comes to AI? Only
because I know with AI, there is a lot of rapid advancements in the
area, and there are ways to sort of invent around, and you need to
be able to detect if somebody is infringing on your patent for it
to be worth patenting.
Matt: It is hard to measure, obviously. The
only public data is on applications. There is no public data on
trade secret options, albeit that enforcement is clearly rising,
particularly in the US, which suggests people are taking trade
secrets more seriously. But, what I would say anecdotally is that
clients are increasingly asking us to help with their procedures
and the documentation and the like for trade secrets, particularly
where their assets relate to AI.
Tamara: That makes sense.
Tamara: So, we will touch briefly on patents
and patenting AI inventions. So I think the key message here is,
yes, you can patent AI-related inventions. Usually, you have to
frame the invention in terms of a problem and a solution and
provided you have to look at what is the AI being used for. Is it a
technical purpose or a non-technical purpose? If it is technical,
is it an obvious use or not? So, it has the same criteria, I would
say, as patenting any technology. It has to be something that is
new. Something that is not obvious or inventive and something that
is technical. When it comes to patenting issues around AI, what are
the main issues that we are seeing? I am going to turn to you,
Vivian, for this.
Vivian: Yes, so I would say that the first
consideration is that, not all the inventions are eligible for
patent protection. So, for instance, I think around almost all of
the jurisdictions in the world, mathematical methods such as
algorithms per se are not considered inventions. But if the patent
application relates to application of, for example, a machine
learning method to solve a specific problem in a manner that goes
beyond that underlying mathematical method, then the application
could be considered as invention. So, an example would be the use
of a machinery method in controlling the navigation of an
autonomous vehicle. And so, I would say that it is very important
to highlight the important technical advantages and practical
details when drafting patent and this can increase the likelihood
of success during the patent examination and bearing in mind that
an AI tool can also be embodied in a physical form. So, actually, I
have looked at some examples set by the EPO examination guidelines
and some EP case laws. So for example, a method of providing
medical diagnosis by an automated system processing physiological
measurements is considered a patentable subject matter and using
the method to identify irregular heartbeats or to predict the
binding affinity of one ligand molecule on the one target protein
is also considered a patentable subject matter. So, I think
particular care should be taken when drafting patents application
directed to AI algorithm as the patent application will be
published and become part of the prior art so if an inappropriately
drafted patent application is rejected, others can use the AI
algorithm described in the patent application.
Tamara: I think, when it comes to AI as well,
we in the UAE usually look to what is happening with the EPO and in
the US as well. We don’t have any specific guidelines in the
region when it comes to dealing with AI-related inventions. But we
do know that here in the patent offices, they do look favourably
towards applications that have successfully been prosecuted in one
of the other big patent offices and it usually helps speed up
examination regionally as well.
When it comes to inventor ship and ownership, Matt, what is the
main issue when it comes to AI invention? I think we all know about
DABUS the case.
Matt: Yeah and the case has just been to the
Supreme Court on that very point. So, DABUS we wait to see in a
couple of months from the Supreme Court to see if our jurisdictions
will allow invention by AI. But certainly, all the substantive
hearings elsewhere have to date failed. But, I think the big
challenge is the exceptions which we talked about. So things like
mathematical methods and the like. There is also lots of publicly
searchable prior art in this field in AI you know very well archive
and other sites like that contain a huge amount of prior art.
You have also mentioned the issues with enforcement. So a lot of
the patents granted are quite narrow. And so, workarounds are easy.
It is hard to detect infringement because AI, as legend has it is
the black box, you cannot tell exactly what is going on inside it
often. And it is often deployed in the cloud outside the
jurisdiction. So, you have to take a lot of care about how it is
drafted to make sure you can make a claim within a single
jurisdiction. And then the other thing to consider is sufficiency.
So is the skilled person able to work the invention, and often
valuable AI is based on simple proprietary data. So, you may need
to frame your invention in terms of publicly available data to show
that the invention can be worked by others. But again, I would say
because of all of these issues, do also consider trade secrets.
Tamara: Yeah, that is a topic that we are going
to come on to very shortly. Before I do, I just wanted to quickly,
sort of, discuss the patent filing strategies that you can take
advantage of through dealing with an AI-related application because
there are developments that are happening so quickly in this space
instead of you might want to speed up examination and prosecution,
and so I know that some of the patent offices have that as an
option. I was going to turn to you, Vivian. Are there any
particular benefits for applicants to file through the Singaporean
patent office?
Vivian: Definitely. So, the intellectual
property office of Singapore IPOS, it has a range of programmes
that allow AI innovators to accelerate patent applications. So
locally, we have the Singapore IP fast track programme, which
allows patent applications in all fields of technology, including
AI that are first filed in Singapore to be granted as fast as six
months. So, the applicants can also use their Singapore patent
application to expedite the prosecution in more than 30 other
jurisdictions through IPOS network of work-sharing agreements. So,
for example, AI inventions are eligible for something called ASPEC
acceleration for industry 4.0 infrastructure and manufacturing
initiative. so under this initiative, the patent officers in eight
other Asian countries they are able to rely on the Singapore search
and examination report, and they are committed to respond within
six months, whether those applications could be accelerated there
or not. And in addition, IPOS also established the bilateral and
global patent prosecution highways. Actually, it covers more than
20 other jurisdictions, including key markets such as US, EP,
China, Japan, Korea. So yeah, there are lots of accelerating routes
in Singapore.
Tamara: It is nice to see the patent offices
trying to keep up with the trend as well, and offer applicants, I
guess, ways to help them stay on top and make sure that they are
not delayed in the processes, as well. Because it is so important
in this area. I am going to turn to data now because I think this
is really important and probably not discussed enough. When it
comes to the training data, we are sticking with machine learning
here when it comes to AI. Typically, when you are a patenting, you
do not necessarily have to disclose the training data itself, but
you kind of need to describe the type of data and how it would be
used. So, I think it is really important that before you go ahead
and patent have a good understanding of where you are getting your
data from; what do you plan to do with your data; are you OK with,
do you prefer to keep it sort of secret; or are you OK this
becoming published? And another thing to think about is the model
generating data that is valuable itself, so data the IP that is
being generated as well.
When it comes to protecting data assets, Matt, I am going to
turn to you. What are the different tools that you can use to do
that?
Matt: So, I just say as a preface, I don’t
know that the answer on sufficiency is settled and whether you do
need to disclose your training data. I think guidance so far from
patent offices has been somewhat bland that the normal rules of
sufficiency will apply. I’ve certainly had patent professionals
suggesting that they might need to literally upload all of the data
or supply it in a way that I think the new data and other have been
used in the past patent applications. So, that is to be seen, and
it could lead to swathes of invalidity if the court decides
sufficiency is tougher than we thought. But in terms of protecting
your data per se, as I said earlier, it may be copyright, and you
may be able to do it that way subject to text and data mining
exceptions. But certainly, information per se is excluded from IP
on policy grounds, and that is in all international treaties. So,
you really will need to protect it as a secret. And that means a
mix of practical measures. So keeping firewalls and access logs and
the like, contractual measures so NDAs and the like with
collaborators and employees. And then, if necessary, seeking
remedies under trade secrets law, which are well harmonised under
trips, and we all largely have a common definition these days. So
it is a commercial secret that is valuable because it is a secret.
But all-important that you have taken reasonable steps to keep it
secret. So the all-important step, I think the company needs, as
well as keeping it actually secret and avoiding a problem, to show
you have taken reasonable steps. So, I think a lot of that comes
down to making sure you have logs of what policies apply to your
core trade secrets, who is responsible for it, who clears
publications to make sure it is not leaked accidentally and to
collate that evidence of the measures under each policy that
applies and we can quickly get an interim injunction in the case of
some sort of breach.
Tamara: I think it is key that you sort of show
that you are taking reasonable steps as well, just based on the
recent litigation that I have heard about, as well. A lot of times
the Courts are looking to what are the extra protective measures
that you are putting in place.
Matt: It has to be appropriate to the nature of
the secret as well. So there is no point in having a firewall if it
can be reverse-engineered from a physical object, for example. A
rather cheeky defence that was attempted in the States was because
the defendant had been able to steal the secret, they cannot have
taken reasonable measures that that one was not enough to strike
out the case.
Tamara: I’m going to turn to you, Sean. Do
you have any mechanisms in place, at the moment, for capturing
data?
Sean: No, it is funny we have had again, we go
back and innovate our processes in order to deal with the
innovations that are taking place because, you know, it takes me
back to the days before the AIA, and when I used to give lecturers
to new faculty or students about keeping good lab notes and
everything, now we thought we were past that. But now, because of
data, we are having to, again, make sure that we have got
controlled access. All of those good practices are coming back in
the university setting in case we have to use a trade secret route
in order to protect the data. I mean, primarily, our approach is to
look at data as part of the licence that comes into a
collaboration, whether it is part of the background IP but also as
the confidential information within the agreement to make sure that
we have tightened up our agreement so that when we are giving a
licence to our background IP routes specifically giving licence to
our data because increasingly because universities around the world
have historically collected a lot of data that can be repurposed
for commercial applications nowadays. And we are finding that there
is a market for certain of the data that we have collected as a
result of the basic research. So, our normal approach is through
licence and contractual controls. We are limiting access by not
necessarily publishing all of the data when possible as part of the
academic pursuit but to the extent that the information has to be
published as part of the academic publication, as opposed to a
patent application, then we are kind of out the door after that.
So, often there is more data that we can monetize, and there is
more data to provide access to, but increasingly data is a larger
proportion of the discussions when it used to be just included in
the definition of intellectual property incorrectly.
Tamara: Do you see that sort of through your
licencing transactions, that data is a core part of the transaction
when you are licencing your technology?
Sean: Absolutely, especially if we are
licencing a model, an AI model for any particular purpose, the
training data is an expectation that the training data is going to
be included with the delivery of the model and we are having to
educate. So, you can have access to the model, but the price point
changes if you want to have access to the training data while you
are building your own repository of utilisation data. So, it is
causing some I would suggest unique conversations.
Tamara: That’s interesting, and Fawaz are
you seeing the same thing as well when it comes to data value?
Fawaz: Like, for us, when it has come to the
genome data when it comes to the images, like medical images, this
one is mostly confidential. We let the research centre to disclose
it to us, so we keep it on our records and our database. But
definitely, we do not, we keep them confidential, we don’t
share them with anyone else. So, when it comes to academic, so I
don’t like remember we had any commercial activities by sharing
our data with what we do we do research collaboration with
different institutions either regionally or internationally. What
we do, for example, we share with them our data, they send us the
model in return. So we give them the data they give us the model or
sometimes we get data from different universities worldwide, and we
give them the model, this type of things. And sometimes,
researchers or faculties, they need to publish their own data to
become one of the recognition to what they are doing. So depends on
the data, so the data if it is just only normal gathering data that
have, like food images or, for example, yes we let them just take
it and publish it. At the same time, we have Qatar National
Library, which all the locals and residents of Qatar, they have
access to Qatar National Library. So all the data we have, which
is, I mean, I am talking about the data, not confidential ones. So
we share it with the QNL, and we put it in the database so anyone
can access through the Qatar National Research Library to this
data. This is, basically, what we do. This is our activity, and
honestly, it is not too much. But the genome project, it is a huge
data that has been for a while for the last nine or ten years. We
gather lots of data, but still, it is kept confidential, so
far.
Tamara: That is a huge project. I remember that
from my time when I was in Qatar as well.
Fawaz: It has been utilised a lot, lots of
great research outcomes out of it and we are collaborating with
some of the hospitals in Saudi and some of the hospitals in Europe
so it is coming along.
Tamara: And I think it is really interesting as
well when you have sort of open data that different parties can use
to build solutions off of. I think that helps accelerate innovation
as well. So, that is a great strategy too. Vivian, I know you have
worked with a university before, and I know that there are certain
measures that they can take or put in place when it comes to
protecting and managing trade secrets. Are there any examples of a
thing that they have done?
Vivian: I would say that non-disclosure
agreements are essential in the early stage of disclosure
especially if you would like to see third-party collaborations. So,
always make sure that these documents are in place. Then it also
includes, very importantly, to train employees, so that they
understand their obligations to safeguard the confidential
information. Because in a lot of US trade secret lawsuits, the
defendants always they will claim that they are not aware that
those information are actually confidential information, and they
have obligation. So, I think it is very important to train
employees early on and try to limit access to the smallest number
of people possible who need to see that information. And other
measures could possibly include having a good cyber security
policy, whether it is a university or other company. And it is also
important to have a robust on-boarding and off-boarding procedures
when an employee joins an organisation or leaves. Sometimes I see
that when they exit that company, they need to sign a memorandum
saying that they are aware that certain information that they had
access to during their employment are actually trade secrets, and
they are aware that they have a special obligation to keep it
secret. Yeah.
Tamara: I think a lot of entities are sort of
coming on board with creating their own confidential information
policies or policies around trade secrets. But I think that is
still an area that is picking up slowly and probably correlates to
the rise in trade secret litigation and the AI boom as well when
you are dealing with innovations that you wouldn’t necessarily
patent and you might just like to keep as trade secrets…
Matt: Can I add actually that one thing we have
in common with reverse-engineering of models. So Sean was saying
they would licence the model, but you wouldn’t give them the
training data. For some models, they are quite vulnerable for
reverse engineering. You’ve got data, test it with data, get
the output, and you essentially reverse engineer the model or even
elements of training data. So you need to think about practical
measures like APIs, putting it in the cloud, and limiting what
people can put into it.
Tamara: That is an important point. Thank
you.
Sean: On that, just as a suggestion, as people
might want to think about it, as we have taken a subscription
approach, adopting some approach from other forms of copyright
media exchange and saying that subscriptions are a way you can have
access to the data, as long as you maintain your subscription you
can utilise the data with the model.
Tamara: That is a good way of sort of
controlling the data as well.
Fawaz: And can be used through API not to share
the data, but you can access the data through the APIs and this
work, as well.
Tamara: It’s a good point. When it comes to
commercialising AI-related inventions, I am going to turn to you
Fawaz, because I know you deal a lot with that. What are the main
challenges that are coming up for you?
Fawaz: OK, I mean, we have been very active for
the last few years, and right now, I think we have up to nine
start-ups that are coming out. I look at the commercialisation, it
is end-end from the early capture until you go to the market. So
sometimes we had issues, I mean, commercialisation bottleneck, like
in the middle when we wanted to de-risk the technology sometimes
you need partner to take it, for example, to work with them closely
until we have the right product or the right solution to go to the
market and sometimes this one doesn’t work. Sometimes we find
the right one, but you know the agreement, the terms that we
discuss, we did not come to an agreement, so we had sometimes to
stop. But, one of the scenarios, for example, we have a very
well-developed solution, the solution goes to the market, and then
you have the issues when it comes to the team and how do they
manage and how do they have more customers, and some of them they
fail. I look at this one as a, we have been experiencing this one,
so any related type of start-up that is related to AI. Building a
solution, it can be, you know, it is possible, but I think that we
need to be careful just when we got to the market for solutions
that is related to AI it is very challenging. You have, I mean, the
founder or the CEO of the start-up you need to be more engaged, he
needs to aggressively getting into the market having getting more
customers because, for example, we have this technology that we
developed in 2019. It was the state of the art, but we had an issue
with the founder. He had been going back and forth. He did not have
the right strategy to grasp the market share, and then I can see
many technologies doing exactly what we do, and right now, they are
getting more share than what we do. So when it comes to the
commercialisation, there are lots of factors that you need to be
careful of. So you need to look at how to further develop the
technologies. Sometimes, when we increase TRL 4 or 5 to find more
funding to de-risk the technology. When we succeed in this portion,
to TRL 9 and we take the solution to the market, then we have stuff
that, it’s like any business. So what I see, the solution, I
look at the solution, just only it is 30 to 40% of the whole
end-to-end. But definitely, 60% is in the market and how to lead
the product and how to get more customers. This is basically, this
is the way, more than the technology itself.
Tamara: Yeah, so the solution is just basically
one part of it but there are so many other steps and things that
have to happen before you can successfully take it to market.
Fawaz: The solution is not, it is not the big
part of it. I would say like 40% the solution it is worth but
definitely, the business side this is very crucial.
Tamara: Yeah the investment, the resource that
it takes to take it forward. I agree I think the solution is only
like the first part and it can years to take something to market
but in this area you have to be quick which means you need to have
resources at your disposal. Sean, is there anything you want to add
on that?
Sean: No, it is just fast and getting to that
first customer revenue and validating the assumptions. I mean it
kind of puts the lean start-up model on its head because you need
to get out there as quickly as possible. But sometimes that means
you will be finding out what your product is while you are speaking
to customers with what you have. So, no, I agree completely with
Fawaz. You have got to be fast, and it is not about the tech until
it comes to raising money, and often that is one of the fall-backs,
and somebody will say OK what’s your IP provision and they say
well we have got a trade secret and that creates a pause in the
conversation around raising money which is I think just a matter of
educating the investors and making sure that they understand that
there are different ways of protecting the opportunity.
Tamara: I think when it comes to
commercialisation there is a lot more than we could cover, but this
webinar is actually coming to a close in two minutes, and I want to
give some time quickly to a couple of questions that we have
received.
So the first question is, for the invention disclosure form,
would you find your minimum requirement on the invention details to
be sufficient favourably past the IDF evaluation stage? So
basically, when it comes to sufficiency of the invention disclosure
form, what is the minimum that you require from inventors to move
forward? I mean, if I were to answer that I would say it is very
tricky to do a proper evaluation or analysis on an invention
without fully understanding what it is that you are dealing with.
But I am going to turn to you, Sean, as well, is there a particular
process that you follow for this?
Sean: I mean, we use the disclosure as a
starting point, and then we have a dialogue, and it is through
those dialogues what we tease out is, do you know exactly everybody
who was involved in the development of the solution that you have?
For those people that were involved, have you confirmed what tools
that they used, and have you articulated within the disclosure what
available resources that might have been used? And then we drilled
down to find out what the truly novel components are. And I think
we err on the side of caution or generosity in so far as accepting
what people come through with, but they have to understand that I
mean the questions that we ask are going to be a cakewalk compared
to what is going to be asked of them if they try to raise money
against the software.
Tamara: And actually, the next question is
somewhat related. So it is basically how much development work do
the investors have to do before you consider something ready enough
for you to take forward?
Sean: I mean, I guess it depends on where it
starts. I mean, if they come to us with a concept and they have not
done any of the coding, then we have got a lot of work to do. If
they come to us with a mere commercial-grade piece of software, but
they just need to work on the UI, then I mean, I guess, we are
almost at the market. It depends on the starting point, it depends
on where they want to get to. Often their resources are limited,
and what we can do is help by quickly bringing money into the
equation so that they can overcome all those challenges.
Tamara: I mean, I think the more development
work there is, the more it comes back to what Fawaz was saying
about de-risking, right? So, like you know it works, you’ve
tested it out, and you can get more, I guess, investment, more
resources assigned or allocated to that particular invention
because it is further along.
So it is actually past 5 o’clock now, so I am going to have
to say goodbye and end this webinar. But I wanted to thank you all
for participating in this. I think this was an excellent
discussion, and I think we might need to do a follow-up session at
some point as well. And to the audience, thank you for joining us
today. If you have any follow-up questions, please feel free to
email those to us directly, and I can make sure it gets to the
panellists and yes, I just want to end there and say hope everyone
has a good afternoon or evening and thanks again.
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