MARCUS DAHLLOF: Welcome to the startup presentations part of the MIT Japan Conference 2022. My name is Marcus Dahllof. I'm a program director at the MIT Startup Exchange.
Before I get going, I want to spend a few minutes, or a few moments, covering the language instructions. To access the two sound tracks, every attendee should choose either Japanese or English as their primary language. Please click on the globe icon at the bottom of your Zoom window, and then click on interpretation.
You will then have the option to choose either English or Japanese. After each presentation, attendees can submit questions to the speaker. Click on the Q&A icon on your Zoom screen, and then type in your question in either Japanese or English. Mentioned the startup name, please.
So I'll give you a moment just to do that. All right. So let's spend a few minutes covering what the MIT Startup Exchange is and what exactly we do. Overall, our mission is to connect MIT startups to our corporate members.
Today, we have approximately 1,400 active MIT startups. And each year, we make about 600 introductions between them and our corporate members. And these are targeted, highly-vetted, high value introductions. The 1,400 startups come from all parts of MIT from all departments, employing all types of technologies to solve business problems across many, many different industries. The keywords that you see on this page represent some of the key technologies that they employ.
All these startups are B2B, and they have exceptionally strong, technical founding teams. We're adding approximately 150 new startups per year. These are some recent success stories between our corporate members and our startups. These partnerships take many different forms. Sometimes it's a license agreement, sometimes it's a direct investment, or sometimes it's a more straightforward customer agreement.
A few notes about how you might engage with MIT startups. Number one is attend events. Each year, we put on about 20 different events. Obviously, a lot of them are virtual today, but there are many opportunities to come to MIT.
We hope that there will be opportunities to come to MIT in the spring. In the fall, we had a number of events live in-person. You can also request meetings with specific startups through your ILP program director, and then you can post an opportunity.
Meaning, let's say you have a business problem that you're trying to solve. You can post this to our community of 1,400 startups, and that begins a selection process that allows the startup to apply to these opportunities. A few more words about today's format. We will have 11 startups presenting today, each presenting for about five minutes. And that will be followed by about two minutes of questions. All these startups have been selected because of their strong business activity in Japan already or because of their strong attempts to begin operations in Japan.
To post your questions, utilize the Q&A tool. You can post your questions in Japanese or English, and we will answer as many as we can. Please mention the start up name in your question. After all the start up presentations, there will be breakout rooms, one per startup, and that's an opportunity for you to ask questions directly to each startup.
We've already sent you the link in an email, and we will also post that link in the chat at the end of the startup presentations. Should you wish to be connected with a startup, you can either follow up directly with that startup or you can contact them via your ILP program director. Now, with that, let's go to the first startup presenter, Alan Flohr, chief revenue officer of Pathr.
Alan, please go ahead. ALAN FLOHR: Thank you very much. I'll share my screen. And hopefully you're seeing it now. I am the chief revenue officer.
My name is Alan Flohr. At Pathr. S founder is George Shaw. He is our CEO and MIT 2011. And our company Pathr is a spatial intelligence company that takes data feeds from cameras existing on your sites and other sensors to identify the movement of people through space. Our tagline is, "You could learn a lot from a dot."
And as you can see from this chart, if you observe the motion of people and things through your spaces over time, those dots will tell you stories. They'll tell you about what's interesting to them, and what's not interesting to them. They'll tell you about traffic patterns.
Where there may be congestion, where there may be opportunities to improve efficiencies, and opportunities to improve effectiveness of that location, whether it's a selling location, a commercial office building, or a factory or distribution center. Most companies have mountains of data right now that they're not using that has come from cameras, from sensors that were originally installed for loss prevention, but have never been used for any purpose beyond loss prevention. By harnessing the power of spatial intelligence, Pathr utilizes those existing data sources to anonymously track the locations of people and things moving through space in real time. And because we use existing equipment and we're not requiring our customers to install specialized cameras or sensors like many of our competitors do, we are able to deliver a very fast time-to-value, typically less than 30 days, and very strong financial results because there's almost no startup costs.
We're able to deliver a 10x or more annual ROI for all of our customers. So again, existing data sources on the left, cameras, but it could be anything like Bluetooth sensors and card readers, going through our software to drive real-time insights for your business. To bring this to life to you, I'd like to share a few case studies.
The first is a retailer, and this is all about understanding customer and staff interaction. You're going to see one right there. The system is smart enough to understand when people are getting close enough and spend enough time together to count as an interaction. In this case, it's a retail store and they wanted to understand how their staff is interacting with their customers and how they can improve that.
And what was interesting to them, if you look at this store, it happens to a bike shop, a specialty bike shop. And on the right side, you see those blue and purple circles which are the staff interacting with customers and with each other, and that happens to be where all the bikes are stored and where the service desk is so that makes a lot of sense to them. Well, what was surprising to them is if you look to the left side, many, many orange circles which represent customers working with each other, talking to each other about the products that are at that part of the store.
That happens to be where the accessories are stored. The apparel and bike accessories, which have the highest margin in the store. So the insight for this retailer was if I could just train my staff to move over to that side of the store for just a little time during the day, they could drive incremental profitability by getting more of those high margin items sold.
And that is exactly what happened when they implemented this, and that is why they are-- at this moment-- expanding to all the stores in their chain as a result. This next use case may be closer to many of your businesses. It's around factory and distribution center optimization. The idea here, again, is to leverage those existing cameras that are in place predominantly for security reasons, but also other sensors. And here, we want to look at the risks of human and machine collision. In distribution centers and factories, we want to make sure that people are avoiding things like fork trucks and automated guided vehicles, and there's enough space between them that we don't have a risk of accidents.
And we also want to remove congestion points to improve productivity. This one is just in flight now in market, and so we don't have results to share yet, but it's already very promising and s a very proven implementation in terms of the ability to leverage that existing infrastructure to solve new problems and drive new value in your locations. What we're looking for today from this group, first and foremost, we're looking for more customers. We have over a dozen customers so the system is out there. We serve large, multinational companies, and all of our customers right now are very large, multinational companies.
We have a current focus on retail stores, malls, commercial real estate, and manufacturing and distribution. But as you can imagine, this is a pretty flexible technology so if you have a use case that's outside those industries, we'd like to hear about that as well. If you think there may be a fit for your business, we would welcome an opportunity to have a discovery session with you to confirm that is the case. We're also looking for implementation partners. We're a small company. We don't have aspirations to grow into a very large company as it relates to people so we rely on local market partners to give us that in-market presence and expertise.
We have two active partners in Asia now. We're looking for more partners, specifically in Japan and in the greater Asia to help us implement Pathr services. And then lastly, we've started-- we have a great strong base of early, large enterprise customers, and we are ready to expand. And so we're looking to expand again not with our own resources but through partners. So we're looking for selling partners in Japan and beyond.
Both companies that are interested in reselling our services as is, but also companies who are interested in branding our technology as their own and selling it as their own under a white label. Those are the remarks I want to make. Thank you.
I appreciate your time, and would like to open it for any questions. MARCUS DAHLLOF: Thank you, Alan. Let's take a couple of questions here. So first question, as a customer, what kind of equipment would I need to buy or install to use your services at one of my facilities? ALAN FLOHR: The only thing we require is a small server, and the reason we do that is we process the actual video on site.
Most companies don't have the bandwidth to ship video out over the web, nor do they want to for privacy reasons so we install a small server that processes the actual video, and then sends a very small data stream of XYZ coordinates and time stamps to our cloud-based analytics. So that's the only installation is that one piece of hardware. MARCUS DAHLLOF: And the rest is just a video feed from the site already. ALAN FLOHR: Exactly. We plug into the existing systems. MARCUS DAHLLOF: Great, great.
Any additional implementation support for corporates or customers? ALAN FLOHR: No, not really. Once we have the server on site, there's certainly a back and forth about what people want to accomplish in terms of use cases, but we've grown this business in COVID, and everything we do can be done remotely, and is done remotely today. MARCUS DAHLLOF: Great. Thank you. We're going to go to the next speaker at this point.
Keith Pasko, R&D engineer at Meter. KEITH PASKO: Sorry about that. My name is Keith Pasko. I am from Meter Parts. We are in San Francisco and Boston, and we do CT.
What is CT? It is computed tomography where we take a series of X-ray images and stitch them together, and create a full 3D internal imaging of any number of different parts, as you can see here on the left. Why would one want to use CT? Well, there's a number of different benefits of looking inside and seeing everything about something's internals. You can look at fitment, for example, and alignment issues that you s not be able to see from outside. You can detect defects such as cracks or pores, and it also allows you to take measurements that you physically wouldn't be able to otherwise because you can't access those areas and compare them manufactured to as designed, for example.
And CT is actually incredibly useful across the entire range of the manufacturing lifecycle. So in research and development, early staging, we can use CT to inform design and manufacturing decisions with low cost and rapid turnaround. When ramping up machining, we can ensure that the tooling meets all of the design specifications and tolerances. During production, we can play an essential role in quality control of things coming off the production line. And then finally, in post-production, a detailed 3D analysis of the results from these scans can catch possible failure modes over the life cycle of the continued product.
And this is also just a small set of examples. Each individual customer may have their own very unique needs for CT scanning. And actually, at Meter, we pride ourselves on collaborating directly with our customers to help them make the best products possible by using our technologies. So if CT is so useful, why isn't it more widely adopted? The biggest by far is the cost.
Our competitors' machines cost into the millions to operate. Their machines also take up the size of a small room, require very specialized, trained experts to install, operate, as well as analyzing the data coming off. Meter, on the other hand, we offer a fraction of the cost, and a very low footprint, very minimal setup.
Our workflow from start to finish is very, very simple. Not only just installing the machine, but also being able to share and analyze data. It requires no installation of the software, no internal support teams to deal with it. All of our software is cloud-based, shareable, and easily expands. And here is an example of our software at work. You can kind of see we imagine a world where you can interact with CT data similarly to how you would a Google Doc, where you can share, collaborate, make versions all from a maintenance-free, web-based integrated interface.
As an example, we had one company who started using our product. They shared a scan data with engineers from many other offices, and collaboratively, they found a number of voids in their product, shared that information with their manufacturing department, who then changed their temperature and pressure settings in order to eliminate those void errors in the product. And actually, within a week of shipping a machine, we had a tenfold increase in the number of engineers across the world that we were talking with about our technology. Why we're specifically interested in Japan is because we believe that Japan has a very long history of quality and reliability in manufacturing, and we believe that our product can help dovetail with those beliefs and further those traditions and values of quality and reliability.
And in addition, installing one of our machines in, for example, a US-based branch, our cloud-based software allows you to improve communication and collaboration due to the fact that everything is in the cloud and shareable instantly. We are also open to vendor relationships with any suppliers of X-ray sources, cameras, other hardware equipment. As we are hoping to help everyone make the best products that they can, we are also hoping to make the best product we can and continually trying to improve. Thank you very much for your time, and I welcome any questions.
MARCUS DAHLLOF: Thank you, Keith. I think the first question I'll ask is probably the question you hear a lot. And I think the first question I asked when I first heard the pitch of Meter from one of your founders, and that's, what kind of parts can you scan and how large? KEITH PASKO: Right. So there are a number of different parts that we can scan, and it kind of spans everything from a shoe to a spaceship in a certain way. We are limited to a cylinder that's 175 millimeters in diameter by 300 millimeters in height, and there are certain materials that are more difficult than others.
Very, very dense or very, very clear materials can be difficult, but it's metals, plastics, rocks, there's a whole world of different things that can be scanned. MARCUS DAHLLOF: Got it. And for someone that's not currently using CT, how might they benefit from your products, your services? Help us understand additional use cases. KEITH PASKO: Sure. I think the biggest benefit is that many people might actually want to use CT or have an application for CT, but don't have the ability to put up the capital, or the room, or the support that it takes, and so we're I think less about creating brand new CT things that have never been seen before per se, but enabling more people to scan more things at a lower cost, and enable them to sort of not be afraid of looking into everything with CT, which is going to improve their reliability, quality control, design, everything, again, as I mentioned throughout the manufacturing. MARCUS DAHLLOF: Got it.
Got it. Another question from the audience here. Do you develop smaller CT hardware than ever? How do you compare to existing solutions now? KEITH PASKO: Yeah.
So our footprint is much, much smaller. And we've put a lot of work into our engineering in order to get the largest scan volume possible while still keeping a small actual footprint of the machine, which is a lot of optics and engineering. I can't say off the top of my head if there is a smaller CT scanner out there. I don't know of it off the top of my head, other than a potentially microscope CT. MARCUS DAHLLOF: Yeah. All right.
Thank you, Keith. KEITH PASKO: Thank you. MARCUS DAHLLOF: Let's go to the next startup. Pham Quang Cuong, co-founder and CEO of Eureka Robotics.
PHAM QUANG CUONG: OK. Good morning, everyone. My name is Quang. I'm co-founder and CEO of Eureka Robotics. We are a robotics company based in Singapore.
What we address is high accuracy, high agility automation. What is that? Traditional robotics is very repetitive. For example, on assembly lines. Those robots can achieve very high accuracy, but they are not high agility. If you change the core model, then you have to reprogram all the robots on the assembly line. On the other hand, warehouse-picking robots have higher agility.
For example, they can pick objects that are randomly placed or different objects without reprogramming, but the accuracy is about 1 centimeter, which is relatively low. Now, what we do at Eureka Robotics is we provide the technology to address high accuracy, high agility. So high accuracy here means typically less than 1 millimeter, sometimes even less than 50 micron. And high agility here means that the work pieces that we are working with can be in random initial positions. So you can see in the picture, we are, for example, picking lenses that are very, very small, about a few millimeters and placed completely randomly in the basket. So some of the use cases that we have in high accuracy, high agility, as I mentioned, picking lenses that are used for example, with telecom or in cameras to clean them or to do the assembly.
So I'm just going to play the video here. Clearly, this robot has been deployed in several countries in the world, in Singapore, in China, and they have already picked more than 1.25 million times, 1.25 million objects over the last year. We have developed several use cases in the optics manufacturing. And on the right, we are also collaborating with Denso, a Japanese company, to commercialize our technology in the [INAUDIBLE].
What allows us to achieve high accuracy, high agility are typically three things. First of all, it is a unique technology to calibrate the full system from the camera to the robot end point, which allows us to achieve 0.2 millimeter with off-the-shelf cameras. We also have deep-learning computer vision that allows us to detect objects to be manipulated to be picked with high accuracy and high robustness.
And finally, we have a unique force control technology that allows the robot to achieve extremely high accuracy when it comes to assembly or insertion in very tight-- for example, shafting holes. We have strong engagement already with Japan. So as I mentioned, we just commercialized our force control technology together with Denso so our software will be installed on Denso robots.
Actually, the product was launched in December last year. We have also collaboration here with Sigma-Koki. So our computer vision software will be shipped together with Sigma-Koki microscopes to detect automatically scratches on optical lenses. And finally, we are planning to open a Japan office this year. So what we are looking for in terms of partnership with Japanese companies is, first of all, if you are a precision manufacturing company, for example, in automotive, optics, electronics, then we want to help you solve the high accuracy, high agility automation challenges that you have in your production. So we are already working with a number of Japanese companies here in Singapore but with our planned Japan office, we will be able to help you in the factories where you are in Japan.
If you are a robot, or camera manufacturer, or a system integrator, we want to partner with you to combine, for example, our software with your hardware or help-- if you are a system integrator help you address high accuracy, high agility needs of your customers. So if you have any such request or possible partnerships, I'll be very happy to answer the questions. And feel free to contact us at the email and in the chat session. Thank you MARCUS DAHLLOF: Talk to us a little bit about the actual product that a customer would be buying. PHAM QUANG CUONG: Yeah. So right now, at first, we make the full systems, integrating robots we buy off-the-shelf, camera that we buy off-the-shelf, and we put our software, and we deliver the whole system-- turnkey system-- to our customers.
And we keep doing that. However, what we're adding now is we are selling also the controller itself, and we are making it so easy that anybody, any system integrator can take our controller and develop applications. The high accuracy, high agility applications by themselves to serve their customers. And third, we are open to partnership where we work with robot manufacturers, camera manufacturers so that they can integrate our software and sell them together with their products. MARCUS DAHLLOF: Please expand a little bit about what you see as the differentiation of your technology versus what's in the market today.
PHAM QUANG CUONG: So in the market today, there are a lot of companies, for example, who are specialized in warehouse fulfillment, bin picking. So those companies have-- these are vision-guided, so we can say that they are high agility because they can pick different objects, and the objects can be randomly placed. But they cannot achieve super high accuracy, for example, submillimeter accuracy, which is needed when you are not in a warehouse, but if you are on the production show floor. Typically, assembly task or installation of electronics components, all of those tasks require submillimeter accuracy, which existing robotics startups besides Eureka Robotics have difficulties doing. MARCUS DAHLLOF: Got it.
Thank you. Let's go to the next startup presenter. That is going to be Kota Weaver, co-founder and CTO of Skylla Technologies. KOTA WEAVER: Yeah.
So you can hear me I think so OK, great. So thanks for joining me. Again, I'm Kota Weaver, and I'm the CTO and one of the co-founders of Skylla Tech. And our MIT connections are Professor Harry Asada, who teaches robotics there, and also Doctor Sheng Liu, who is a-- has a PhD from MIT in robotics as well. So Skylla's goal is to make robots work more safely and more effectively by better understanding how people move and interact. And this can really be applied to a pretty wide range of different areas, but we're currently focused on the manufacturing setting.
And there's really been this increased demand for automation, especially with the global pandemic. And in addition, we found that a lot of customers really want frequently changing floor layouts because they have different parts that they might want to be manufacturing, s and this often in spaces where there are also people working with the robots. And then again, the submillimeter precision is actually a big need as well. Very often, customers need high precision.
And we found that none of the existing robots actually meet all of these requirements. So we put together a controller, which can be integrated into either new or existing vehicles and robots as sort of the brain of the operation. And so by combining onboard sensors such as cameras and Lidar along with our proprietary algorithms, we can actually allow these robots to map out and navigate a customer's environment. And then once we've reached the goal, then we can perform various manipulation tasks and also carry objects from place to place. So this is all made possible by teaching robots how people move around using machine learning.
And by better understanding how they move, we can move more effectively, and also more safely. We also provide about a 0.3 millimeter endpoint positioning accuracy. So we do have a number of customers in Japan.
This includes DMG MORI, who's the world's largest machine tools company. And DMG MORI uses our jet stream core controller as its navigation platform for the WH-AGV5. So that's been deployed already in a number of factories around the world, including in Japan.
And we're also helping DMG MORI release additional products around this. We also do have robots in JR East train stations. So currently, we're pilot testing some guidance and data collection robots in Takanawa Gateway Station. And so that uses our human-aware navigation technology.
And so if you visit that station, you might find our robots driving around. So as I mentioned, we have that jet stream core robot controller as one of our products, and then we also have a fleet management server. So with these two things, you can manage your robots and also basically synchronize them and make sure that they're doing the right things. And then we also have a new compact mobile manipulator coming. So we found that a lot of our customers feel that the existing mobile manipulators out there too big so we're trying to build something a little bit smaller that can fit in tighter spaces.
So this is a good fit for factories in Japan as well as in other places. So as I mentioned, we have substantial experience working with customers in Japan, and we're actually planning on opening a branch office in Tokyo this year. And typically when we work with customers, they might send an engineer to work with us for anywhere from a couple of months to a year, or we've also worked with them on a less-- basically just by providing frequent site visits to provide some support, and also we want to make sure that we understand our customers' needs.
We try to work very closely with our customers to help them integrate, and that's very, very important to us. So we're currently looking for some strategic partners to develop some new mobile manipulators as well as retrofit to existing platforms, and actually, in general, vehicles. And we're looking at a few different areas.
In particular, I think the indoor and outdoor hybrid use cases are something that we're very interested in as well as clean room applications. And then we're also looking for early stage customers for our new human-aware AGV. So again, I'm Kota Weaver, and my contact information is below.
And thank you very much. MARCUS DAHLLOF: Kota, talk to us a little bit about how you integrate with the existing systems. KOTA WEAVER: Sure. Yes, so we do have experience integrating with basically existing factory infrastructure. So we've integrated with, for example, DMG MORI's MATRIS product line as well as various PLCs and things like that.
So we actually have a pretty-- of course, we don't have complete coverage or anything, but we do actually have experience working with a pretty wide range of PLCs and also other systems to help better integrate our controllers and our robots to existing factories and vehicles. MARCUS DAHLLOF: What about the human safety component? KOTA WEAVER: Yeah, so we've-- Yeah, of course, naturally, we've worked pretty closely with various standards groups and things like that as well as some testing sites. And in addition, yeah-- There are a pretty wide range of different safety standards that we go through to make sure that these things are safe for human use. Naturally, that's a very important part of integrating these into a factory so yeah. MARCUS DAHLLOF: Understood. So let's pause here.
Let's go to the next startup. That's going to be Brian Alessi, VP of marketing at Everactive. BRIAN ALESSI: --you, Marcus. Hello, everybody.
Can you all hear me and see my screen? MARCUS DAHLLOF: Yep. BRIAN ALESSI: All right. Well, let's get into it.
I'm Brian Alessi. I head up marketing at Everactive. So what is Everactive if you're unfamiliar. Everactive is the self-powered IoT platform for hyperscale data acquisition. So what do we mean by that? We mean being able to collect and manage massive amounts of physical world data that we all know is incredibly valuable, but at the same time, has been incredibly challenging to get in a cost-effective and really easy to obtain way.
So how have we done that? Fundamentally, we're able to make that claim because we've solved some really challenging technology problems. So with custom semiconductors that operate at 1,000 times lower power than what you get off the shelf today, we've been able to develop completely battery-free, wireless sensors that are always on, continuously monitoring, continuously transmitting data from very small form factor wireless and battery-free devices. What we believe that that unlocks and is already unlocking for our customers today is the ability to deploy sensors at a scale that's just not possible if you have to think about batteries or wiring devices. What we've done with that as a company is we-- from that custom silicon that was actually developed dating back to the days of our co-founders at MIT, we've developed a real data acquisition platform. And if we move to the right quadrant here, you can see we've proven out the value of that platform by ourselves, Everactive developing two very specific use cases and applications to begin with.
So those are specifically a steam trap monitoring product and a vibration monitoring product for motors, pumps, rotating industrial equipment. You can see some of the logos there that we've been able to win and expand with over the past couple of years. But the very exciting thing-- that is exciting, but the more exciting thing-- to come over the coming year is this bottom piece now. Poised to enable entire industries to innovate and really usher in this ubiquitous computing revolution.
And the way we're going to do that is by opening up the platform to other developers, enterprise developers, large customers to be able to develop battery-free products on top of the Everactive platform. So that's a pretty bold claim, the ubiquitous computing revolution, right? Sort of lofty claim, but the way we think about the world is that each of the computing revolutions over the past few decades fundamentally are about two things. It's about fundamentally new pieces of hardware, being able to access and generate fundamentally new streams of data.
So we see that from the PC Era, just connectivity sort of in its initial phases, going through the mobile era, right? About being able to put those computers in our pockets and have access to just vastly new and high volume data. And then moving into sort of the early stages of the IoT. The Fitbits, the sort of connected, wearable and home devices. Now, to being able to think about connectivity as one device per thing out there in the world. And that is what we view as really getting to the true promise of this IoT that's been talked about for so long.
Fundamentally-- you might have been reading these-- we think there have been some key challenges in getting there. First and foremost, of course, you heard the battery-free theme. We think getting rid of the battery is absolutely critical to achieving that vision.
But there are also some other things tied up in there. Wireless networking, being able to have reliable, long-range, high density wireless networks that don't crumble at tens of devices per gateway, and tens of meters per range from gateway to device. The third bullet here is making all of this very easy to use and easy to integrate, right? For folks who operate manufacturing plants, they shouldn't have to worry about getting a doctorate in wireless communication and semiconductors in order to have a meaningful system that gets them data that they need. And then, of course, being able to demonstrate real, quantifiable savings to customers.
We'd talked about the platform. Here's a bit of a double click on what that actually looks like. The platform from Everactive standpoint consists, of course, of battery-free hardware. Think of that as the data acquisition layer. Then a managed network layer. Think of that as a data services layer.
And then the application layer, which is really where the developers get to have fun and to innovate and really start to bring new value to their customers. And we've packaged the green bits here as a managed network in large part to make it easy for customers who don't want to have to worry about integrating all of these pieces together. Conscious of time, I'll jump through. So Everactive focuses its innovation now on success of generations of its chip technology and its wireless networking technology. So you can see some of the specs here, but the vectors we push on between each of these generations is lowering the power requirements so we can harvest energy from scarcer and scarcer sources.
So the light in my very dim office right now would be enough to continuously power our generation of devices out there today. Improving the wireless range, and then expanding the different sensors that we can interface with all in the name of being able to collect more and more data using scarcer and scarcer energy, and transmit that more reliably back to where it needs to go so that our developer community can build high value applications on top of that. What we're asking for here today, specifically is, of course, always looking for customers of our current two products, but specifically now partnerships for the platform service, companies and organizations who are looking to build for themselves innovative battery lists and solutions to take to market. So with that, I think I probably ran over but-- MARCUS DAHLLOF: We have a little bit of time for questions here.
So maybe as a starting point, tell us what's unique about your technology. What are the things that stand out? BRIAN ALESSI: Yeah. So fundamentally, it's the innovations at the semiconductor level, notably related to power and wireless networking. So we've lowered the power requirements for always-on devices in the-- you saw in the tens of microwatts. So what that enables us to do is generate power from those scarce sources you saw on the last slide.
I think the very important point to hammer home though, so it's batteryless and always on, right? It's easy to make a batteryless system. You could take one of those solar panels from the highway and you can measure a temperature point and transmit data once a month. That's a batteryless system, but it's not super functional and super scalable. So we've cracked the nut of getting rid of the battery and continuous data transmission. MARCUS DAHLLOF: So very briefly, what are the use cases that you solve that others cannot? BRIAN ALESSI: Yeah. So the real sweet spot use cases for us are related to scale.
So if you have a facility where you want to put 10 sensors, you want to monitor 10 pieces of equipment, you can probably tolerate changing out 10 batteries. If you have a refinery or a manufacturing facility where you want to monitor thousands upon thousands of different data points, you cannot tolerate from a logistical or financial standpoint replacing batteries. So that's kind of our-- that's what we solve for, and then it's kind of plug and play from there in terms of well, I want to measure vibration, or temperature, or humidity.
MARCUS DAHLLOF: Got it. We're running out of time. I see a question in the chat here. Perhaps you can answer that directly, Brian. I want to encourage everyone to post your questions in the Q&A tool ideally, or in the chat in English or Japanese, and we'll try to answer them.
And also, just to let you know that if we don't get to your question, you can join us in the breakout room at the very end, and you can ask your questions directly to each startup. We're now going to go to our next startup presenter, and that is Munehiko Sato, co-founder and CTO of mui Lab. MUNEHIKO SATO: OK.
Yeah. OK. Thank you for the introduction. So hi, everyone.
So I'm Munehiko Sato. Can you hear us? MARCUS DAHLLOF: Yes. MUNEHIKO SATO: OK. So I'm CTO and co-founder of mui Labs.
And I used to be a research scientist at MIT Media Lab two years ago. So mui Lab, so we provide IoT platform and interfaces that enable a "calm" digital living. So digital technology has made our lives so easier, yet devices interrupts our lives constantly, and compete for our attentions. And these applications and devices literally cut into our lives and moments, and threatening our most valuable moments with your loved ones and by yourselves.
A challenge is, how can we take control of your calm moments without losing the convenience of digital technology? So we provide mui platform along with our iconic smart home hub [INAUDIBLE]. So it provides a comfortable balance of digital life and analog life. The mui [INAUDIBLE] shows only minimal amount of information and quietly and softly. So the lady is looking out of the window. So she might be thinking about something important. So we believe that technology should not get in the way of people, but rather humbly step aside and provide only essential, minimum amount of assistance to people.
So here's more details about our product. So we provide a smart home hub with elemental features for people's lives like communicating with your family members using text voice and also handwritten messages, and controlling IoT devices like light bulbs, music streaming, and air conditioners. And essential utilities like weather forecasts or night timers-- meditation at night. So here are some example of the apps. It's light dimming timer.
That's [INAUDIBLE] unique interface with a touch interactive wooden circuits. So you can draw a single line, and the length of the line becomes the length of the timer. And it dims the light very slowly.
So it's perfect for winding down and reading a book before you go to sleep. [INAUDIBLE]---- and so you or maybe your [INAUDIBLE] can send a handwritten message to your family member also adding your personal touch. So it's pretty simple.
You can send such messages, and then we have a companion app, and then you can receive the messages, for example, when you are at the office. So our business is not selling this mui bot hardware to consumers directly, but providing this mui IoT platform to our B2B customers. We have a design and technology platform to enable this calm and unique user experience, and it's backed by our patented hardware structure and manufacturing processes, but also practical integrations of IoT hardware, [INAUDIBLE] structure, and API integrations, and UI/UX designs. It's all centered around the concept of providing calming user experience. So there are mainly two forms on how we sell customers.
The first is shown on the left. So we customize and tailor finished products, including hardware, software, and product. So such customers include health providers or real estate agencies and infrastructure providers.
So these clients have s tough consumer customers like home buyers or tenants. So we provide customer relationship and engagement platform connecting to their end users. And second, on the right side. So we license our technology and design stuff, including IP patents, supply chains, and SaaS backend, and UI-UX assets so that our B2B customers can make their products come alive. So we have been working with a variety of collaborators and clients, including high-end furniture brand Ascena or retail store-- [INTERPOSING VOICES] --as I just showed you.
And co-working space or housing makers, [INAUDIBLE] and office complexes. So we've been working with more than 70 clients Japan and worldwide, but I highlight some of the collaborations here. So the first one is Wacom. So it's a reading touch stylus provider. So we create new digital products for home and reading market, [INAUDIBLE] and it is scheduled to release this year. So this JIBUN HAUS is a Japanese health provider.
We built a partnership with them to include mui platform to all of their houses. [JAPANESE] in Japanese to provide better and streamlined smart home experience. And third, we worked with Alexa to build a custom Alexa experience. It's hybrid of voice UI and minimalistic touchscreen directions for some Alexa skills like a timer or sunlight.
And finally, with SAP, we integrated their Qualtrics customer experience platform with mui board and the customers can leave handwritten feedbacks to neighbors. And our system recognized that text, and then transfer that to the SAP database, and they can run the analysis for business intelligence and so on. OK.
To wrap up, so we are looking for partners. First, companies who would like to use mui boards in our cloud and customer engagement apps to their consumer end-users. And second, companies who like to use our tech and design stacks to make their product calm. And last but not least, so we are looking for strategic manufacturing and supply chain partners for Japan and the North America market. Thank a lot.
I'm happy to answer questions if you have. MARCUS DAHLLOF: Thank you, Mune. Maybe tell us a little bit about how one should think about the difference between your platform and other existing IoT platforms. MUNEHIKO SATO: Yeah. So our platform is really focusing on providing a common UI and common user experience. So we have basic-- all the same drivers of the functions and reliability of IoT platform existing out there, but I think it's a complementary technology.
For example, when you are at the home office or maybe at the office so they can use something existing like Alexa, or Google Home, or other IoT platform, but when in bedroom or maybe more time with your children, you want something different. So we think it's complementary IoT platform specialized for family and kind of calm moments. MARCUS DAHLLOF: Got it. Thank you. Let's go to our next startup presenter.
Lifeng Wang, co-founder and CEO of Eion Technologies. LIFENG WANG: s you very much, Marcus. I'm going to share my screen. See it all right? Hello, everybody. My name is Liefeng Wang.
I'm the co-founder and CEO of Eion Technologies Inc. I co-founded the company with Professor Ju Li of the Department of Materials Science and Engineering at MIT and also Professor Sa Li of Material Science of Tongli University. And Professor Ju Li and Professor Sa Li have collaborated over the last six years to develop this technology. Eion produces a high-performance LFP battery cathode material through recycling spent LFP material from used retired EV batteries. And our method can cut the cost of producing new LFP cathode material by 70%, and the process also reduces the CO2 emission by 90%. The global LIB, the lithium-ion battery market size is growing very rapidly.
And this year, it's going to reach $30 billion. And the amount of decommission battery is also increasing exponentially. And this year, just the cathode material-- LFP cathode material itself is going to reach over 150,000 tons. So the battery, once it's retired from the car's storage system, It's collected, and shredded, and grinded.
And the material inside is separated into copper, graphite, aluminum. And the remaining black mass, which contains lithium, is what we recycle and reprocess so it becomes a new LFP material and it can be used again to build the new batteries. And the current existing traditional method of recycling is annealing, which is one kind of direct recycling. There is a hydro process recycling. There's also the pyro process recycling. And all three of them, if you look at the chart, they create a lot of CO2 emission.
It's actually more or the same amount of emission and uses the same amount of energy as generating brand new LFP material. So it's costly, and also not very environmental friendly. And if you look at the environmental impact, you can see the difference between our direct recycling via our technology and the other methods. So we estimate right now there are only less than 10% of batteries being recycled as of today. And worse is the traditional car battery, the lead acid battery, already 99% of a recycling ratio.
So there's a long way to go. We estimate that by using our technology, by 2030, we can reduce the CO2 emission by 400 million tons and reduce the environmental hazard waste by 42 million tons. So the car battery, once it retired, the LFP material, there's only 20% to 30% of it that failed. The other 70% to 80% are still good to be used. But the traditional method of recycling, they regenerated, reprocess 100% of it, and this is the deep recycling.
It's very costly and uses a lot of energy, and generate a lot of emission. So our solution is, we call it targeted repair. We only repair the 20% to 30% of it that's failed instead of processing the good part of it too. And we call the process light recycling. And this is our product, finished product. If you look at the graph, the energy density, the cycle life, and the charging, they are all either at par or superior to the commercial grid LFP available on the market of today.
And currently, we already built our first test production pipeline, and our goal, we are on track to go into limited scale mass production in the second half of this year. And we are starting to plan a much bigger factory so we can get into large scale, mass production in 2023. They're also working on the development of recycling NCM batteries in addition to LFP batteries. The technology we estimate will mature in the second half of this year. And we plan to build one or more factories in Japan because Japan is a large market for EV and batteries, and to do the recycling drop there. And we would like to partner with Japanese battery recycling companies so we can buy the spent black mass material from them.
And we would like to partner with the Japanese battery manufacturers so we can sell them our newly generated cathode material so that they can build more new batteries. That's it. Thank you very much, and happy to answer any questions. MARCUS DAHLLOF: Great. Thank you, Lifeng. Maybe start by summarizing some of the key benefits of your technology compared to what exists on the market today.
LIFENG WANG: I think it cuts the cost dramatically. 70% of our cost of processing is 30% of others using today. And that's due to we use much less energy and then we generated much less emission. So I think we provide a much cheaper material to the new batteries by recycling the used one. MARCUS DAHLLOF: Got it. Where are you looking to do your production line? LIFENG WANG: The first production line we're building in China, which is the largest market for LFP batteries.
And so it's going to remain the largest battery market manufacturing base probably in the long future. So that's why we are building the first factory. And then we once that's up and running, we finished the pipeline, and then we figure out all the other small issues, now, we're going to expand.
We're going to build our second one in the US, and we would like to build one in Japan. And we have investors from Korea that they are talking to us about building one there. And then, of course, in Europe. We will actually-- we think this is a breakthrough technology, and we would like it to benefit the entire world wherever there's a need for battery recycling. MARCUS DAHLLOF: Got it. Thank you.
Let's go to our next startup presenter. Jifei Ou, founder and CEO of OPT Industries. JIFEI OU: Hello, everyone. And I hope everyone can hear me. Well, I'm going to share my screen as well.
OK. So hi, everyone. My name is Jifei Ou. I'm founder and CEO of OPT Industries.
So-- MARCUS DAHLLOF: Can you put it in the-- OK, thank you. JIFEI OU: Is it full screen? Awesome. Yeah. So OPT Industries, what we do. We are a material and manufacturing company so we build additive manufacturing hardware, software, and a polymer system to help us to developing new types of materials faster. And also making the material manufacturing more scalable, and also uncover new materials that were not possible before.
So when we're talking about materials, of course, depending on what kind of background you are coming from, you might mean something a little bit different. For us, the materials mostly specifically focusing on soft goods such as firm textile fabric. Most of the things that we'll be using are from day-to-day basis.
And then if we're really looking into how nowadays we manufacturing those kind of like a soft goods material, so it's actually quite complicated. So this is a very simple example of a testing swab. And if we do anatomy on this swab, we can really see that each component and parts went through a quite long supply chain, and really from a beginning, a very small scale of the fiber all the way to a larger scale to fabric in the end to cut and sew to make the finished goods. And then the problem with this kind of current conventional pipeline is it really makes any kind of a new material property discovery or a development relatively long, but also in order to basically make any new inventions, there's quite a high capital requirement to purchase in all of those expensive equipment. So what we're really thinking about, the future of the material manufacturing should be digital. What we mean by that is instead of a materials property is solely relying on the chemical and also mechanical processes, that you should be design and assimilated digitally and algorithmically.
Instead of looking at different materials needs to be produced by different processes and different machines, you should be able to produce it in a unified, single process. One process, multiple different material structures that can be produced. And because of that, third point is that really allows the scale of the manufacturing become more dynamic. So basically, if we wanted to have a larger production or small production, we don't need to basically invest in a large, very expensive manufacturing equipment. So this is basically what we're building in terms of the technology. So there are a lot of things that we can talk about on this new type of additive manufacturing system.
But three main takeaways from today is that so first of all, this is the world's first row-to-row-based and additive manufacturing system. As you can see here, that we can produce in a very, very precise at the scale of micron material structures. But instead of a conventional additive manufacturing system that is a batch to batch process, this is a truly row-to-row process, which means that we can produce the materials in unlimited length that not only allow us to tap into applications requires a larger format, but also really automate the process really easily. Secondly is that as you can-- sorry. This one is not looping so I'm going to basically put it back in there.
But really, besides this foam structure you can show here-- so if you see my video, that there are different material structures such as this kind of micro pillar faux fur structures. There's kind of like a spacer fabric or even something much longer like a weave and ribbons that we can create. So again, this is to the second point is that one manufacturing machine platform allows you to manufacture different material structures on the same platform.
And third point is that we have a fully end-to-end digital from design to manufacturing process integrated. What I mean by that. If you're thinking about really design and modeling a sweater that you are wearing from the scale of each individual fiber, you'll find it's impossible to do because there's no digital modeling kind of software will really allow you to do that.
So what we do is that we really have our own data structure file format to allow us to really modeling material from very, very small scale and to the large scale, really allow us to really-- a designer, engineer to design the material functions in a very deterministic way. So what this platform can do, this is one of the application example that we can see here. So essentially, this is a medical swab where you show study it has a much faster wicking property because of the capillary forces that we can tune.
But not only that, and also we can sort of design the pore size in the sense that it's also big enough that when you put it into the reagent, it can release your samples very effectively. Now, of course, this such a product was very timely in the peak of the pandemic because this is much-needed for a diagnostic, but really the idea behind it is that because we can start really designing such a fine scale, we are not really designing for shape, right? You are designing the material properties in a very deterministic way. So yeah, so in the past year, we've been very lucky, have been some engagement with some Japanese business partners, mostly in the packaging, and cosmetic, and also diagnostic side. And then the way we engage with our customer is that instead of selling them equipment or software, we are actually their prototyping and manufacturing partner.
So they came to us with a material challenges or requirement in their current process, and we basically work together to see how our platform-- how the software and polymer platform can provide a solution for them. So really as I said, right? This kind of absorbent, the applicator medical swab is just really the tip of the iceberg. A few things that we can do. Beyond that, things like packaging insulation, acoustic and material filtration, mechanical adhesions such as like a Velcro, different three dimensional Velcro, or cushioning materials. Those are things that we've been already working on.
And by the way, those are real photos coming from our machines so not renderings. So yeah. So very excited to-- looking forward to having the opportunity working with the Japanese partners. One of the really main things that are making us really excited about this opportunity is that we know that Japan is really known by s precision machining and process, and then we're really looking for how our micron scale precision, additive manufacturing process would be able to benefit really in the material innovation side. Thank you very much.
MARCUS DAHLLOF: Great. Thank you, Jifei. A few questions, perhaps. What is the typical input material? JIFEI OU: So we are mostly right now focusing on photopolymers. So it's a thermal set polymer.
Right now, we actually have quite a focus on how we can increase the plant-based raw material into our formulation and composition so this is one of the things we're looking at from the sustainability perspective. And not if we're talking about additive manufacturing in plastic, most people are concerned about the sustainability. And this is basically one thing we've been working on, an increase in the bio content in our input material. MARCUS DAHLLOF: Got it. Maybe tell us a little bit about the use cases.
What are the potential applications? JIFEI OU: Yeah. So as you can see here, that what we see here is that we're basically developing different type of a material has a certain functionalities , right? Like absorbent, cushioning, or even thermal insulation. So a few applications in the medical and the diagnostic, obviously, is looking into how we can develop a new type of applicator swaps for better-- a high sensitive diagnostics. In the cosmetics, we are looking at developing new type of a cosmetic applicators such as mascara brushes or foundation pads, and all of those that can better release fluid or particle, a cosmetic product on human faces. And we're also looking into automotive where we can basically-- Really if you're thinking about an automotive or other type of cushioning material, if you need a different breathability or bouncing cushioning, you need to basically have a cut and sew process.
We're looking at and now you can design all of those structures and producing one go how we can really reduce the assembly cut and sew assembly process in the manufacturing line. Yeah. MARCUS DAHLLOF: We have a couple of questions written in Japanese. I'd like to get a translation for those, and we can try to answer them. So let's try for the one in the Q&A tool first. AUDIENCE: [SPEAKING JAPANESE] MARCUS DAHLLOF: Jifei, can you hear that? JIFEI OU: Yeah I think I heard a bit.
That was in Japanese. [LAUGHTER] MARCUS DAHLLOF: Yeah. So the translation is, talk to us about the speed of manufacturing. How quickly can you produce? JIFEI OU: Yeah. So it depends on kind of a microstructure we're creating. So right now, we can basically create-- in terms of a speed-wise-- from 180 to 600 millimeter per hour.
And this is basic in terms of the speed of-- as you can see here, things going out. And in terms of the width-wise per machine, we can basically produce about 20 centimeter wide of the material. So basically, that gives you a sense in terms of the throughput. Yeah. MARCUS DAHLLOF: Got it.
And one final question posted in the chat here, Do you mean that OPT has developed new 3D printer hardware and software? JIFEI OU: Yes. We have several patent on our hardware, which is this kind of like a principle and mechanism of a roll-to-roll printing. And software-wise, it's really about how to design those microstructures and make it scalable. MARCUS DAHLLOF: Got it.
Let's move to th
2022-12-09