 Welcome to the September financial markets mortgage subgroup meeting under the hyper ledger umbrella. Before we get started I like to express our appreciation to the financial markets special interest group and the hyper ledger foundation. They're the group that really makes these meetings possible so I always like to say thank you for them. Okay. As always please note that this meeting is being recorded and is under the umbrella of the hyper ledger foundation as I just mentioned, and the please abide by the antitrust policy that I'm sharing on the screen, and the code of conduct, the antitrust policy states that we avoid discussions of companies about pricing products and projects. We don't make negative remarks about other companies or other products, and the code of conduct means that we treat each other with respect, never discriminate communicate constructively, and we fully support hyper ledgers policy of openness equity and inclusion. If you're a new participant to this meeting. Please introduce yourself in the chat if there are any items that you'd like to discuss. Please let us know the more interaction we have the Richard the discussion is so thank you and welcome to everyone. Here's our agenda for today we've already gone through the welcome and housekeeping there's a brief hyper ledger community information portion. James is going to discuss the state of the blockchain in the mortgage global mortgage industry. We have a demonstration and a discussion of the IPDS wallet and a mortgage industry example from James zoning and Casey rock and then we'll go over some future agenda topics. So with that, we always like to start off with this same set of slides because we want to reinforce that we're all on the same blockchain journey. I know people that have been here, have heard me say this multiple times. This group is meant to help everyone on their blockchain journey to demonstrate the feasibility of blockchain technology through mortgage industry use cases we're going to see one of those today, and to define potential implementation path. We're all on the same journey we're just at different points. Okay, some brief hyper ledger information, I just wanted to remind everyone that the hyper ledger global forum for 2022 is in Dublin Ireland from September 12 to the 14. So if you want to get your pint again as head on over to Dublin, there is going to be some, some of it that will be virtual so please look into it I think it's a great opportunity to find out what's going on in hyper ledger. In the next three slides, I always mentioned for those that are new to the group. This first one is just a site map that shows some of the links that provide information on hyper ledger the second one from the bottom there is the financial markets mortgage subgroup wiki so take a look at that one if you're new. Also, if you do want to access some of this information you'll need an LF ID. So this slide shows you how to do that. And the last one is just some free blockchain training I always mentioned that I've taken this and it was very helpful for me so please avail yourself of this information. Well with that, I'd like to turn it over to James Hendrick and he's going to go over the state of blockchain and the global mortgage industry. Take it away James. Marvin thank you very much. Let's go ahead and move on to the first line. So this is the global mortgage blockchain activity that we've been reporting on over the last couple years. 2022 you can see we've done a variety of different articles spanning across pretty much every continent on the planet. You know as we get into this month's updates I want to talk about CV insights and their recent release of the state of the blockchain report, as well as some in the UK with Berkley's Marvin next slide. All right for the state of the blockchain q2 report. So back in January, we published or we posted to the wiki page, the 2021 annual blockchain report from CV insights. It's extremely valuable report they're very detailed 150 200 pages longer so, and it breaks down blockchain activity that is going on across industries via countries. Pretty much every way that you want to slice and dice it. You know some of the big things that came out of the q2 report summarizing the first two quarters of this year. There was 1.4 billion record blockchain funding in Europe these first two quarters. Europe was the only global region with growth in blockchain venture funding and deals. 52% of deal growth and infrastructure and development, one of the only blockchain spaces with real deal growth in q2 of 2022, reaching a new high of 47 deals. In Silicon Valley they represented 28% share of all the US funding with New York following closely 678 million man Los Angeles with 492 million, but not all is dreaming and crypto land the report also talks about what's about crypto markets. There's a 29% drop in global crypto funding blockchain venture funding fell to 6.5 billion and investors scaled back crypto investments, due to macroeconomic pressures and concerns about crypto evaluations. And 44 or 54% decline in make around dollars blockchain fell to 2.6 billion suggesting that investors were more cautious, due to the crypto winter and the recent price volatility. So a lot of great information in that report if you're looking to gather up stats or infos to help support the projects and initiatives that you're looking at, I would definitely take a look at the CD insights report. We've been out of the UK, Barclays is among a group of new investors joining a funding round for copper copper provides custody prime brokering and settlement services to institutional investors, deploying money into crypto assets. We've done investments from big names in the global venture capital sector such as local globe Don capital and MMC ventures and Barclays is expected to invest a relatively modest sum and the millions of dollars as a part of the round. All of this is occurring while a number of major marketing participants including three arrows capital and Celsius which you'll have hear me mentioned later, have filed for bankruptcy in the most recent weeks. Let's move it on to the next side. So in the US sector we've had a lot of different stuff going on. Again, here's our updates from what we've been talking about this year in 2022. Marvin next slide. Two of the key articles that I pulled out for this month. The first one is with JP Morgan. So they release their annual summer reading on this year's collection is available for visitors in the virtual lounge decentralized. Now we've talked previously about the central land I think it was back in around March or so of this year. In fact, we had a whole presentation in March with Mark D'Angelo talking about the meta universe JP Morgan and other companies are estimating the metaverse to represent a one trillion market opportunity. We're going to see more and more banks actually take advantage of it. In fact, Quotonic Bank which is a New York City based digital bank opened up a Quotonic outpost in decentralized land last month. And you can actually see in the picture here in the upper left. That's a picture from decentralized of the Quotonic bank outpost Quotonic claims to be the first US bank to offer its customers of Bitcoin rewards program a feature the bank launched in 2020. It seemed to be the first bank to introduce a tap to pay mobile payment ring to the US market. Product with the officially rolled out in April and actually had to go research and find out what that was and it's literally what it sounds like it's a ring that allows you to walk up to a kiosk and pay much like you do with tapping your phone or or tap in a credit card. The fun about this is to mark the occasion Quotonic Bank hosted a virtual launch party for the space, which they included a DJ they had non fungible token giveaways and things like that so people are really companies are really starting to take advantage of the metaverse. The last article I've got for today is talking about seven different companies that are in the mortgage and DLT industries. So this article was just recently updated again in June of this year and it talks about companies like liquid mortgage figure that we previously talked about throughout this year and our presentations, a couple of the other companies I wanted to highlight that they talk about. There's unshamed capital of Austin, Texas. They lend cash to long term cryptocurrency holders. The crypto owners leverage their Bitcoin or ether for loans anywhere from three to three or three to 60 months with interest rates, interest rates ranging from eight to 14%. The unshamed capital holds the crypto in a blockchain secured vault requiring permissions of both the borrower the company and a third party to avoid any single plane of failure, and they offer loans for personal small business as well as real estate use Well, which is out of Denver, Colorado, they're also a company that use blockchain's flexibility offer cash loans that leverage digital assets. You know, similar their loans can be up to 36 months with an APR as low as 5.99% and they are available for business and almost every state in the US are now expanding New Zealand, Brazil, UK and Switzerland. Blockfly is another one of the companies out in New York, they're a lending platform that uses crypto as collateral borrowers can receive 12 months, 12 month cash loans by leveraging their ether like coin or Bitcoin. And then I thought it was also interesting this as I mentioned this article was updated in June and they do talk about Celsius which as you heard me mention a couple articles ago Celsius now is actually in the process of filing bankruptcy. So Celsius was providing cash loans exceeding 5000, allowing customers use Bitcoin, Ethereum, like coin and ripple tokens as collateral. The company loan started at 5.5% APR and a borrower receives all their crypto back upon making their final payment. It'll be interesting to see now that in the last couple links and Celsius moved into bankruptcy, what's actually going to wind up happening with that company and what we'll see them doing in the future so more to come as we keep an eye on them. Marvin next slide. And just a quick update, this is our wiki page. All the resource articles I mentioned are on the right hand side of the page. In fact, if you scroll down the page, even farther on the right hand side, that's where you're going to find the blockchain information and research section. That's where you'll see that CB insight state of blockchain report. And then you know over on the left hand side we have sub links in our groups that provide information have links to our previous meeting recordings as well as the presentation. So if you missed out on any of those previous presentations I mentioned earlier, do come take a look at the wiki they're all accessible directly from there. And in fact, for everybody's quick reference, I will go ahead in a moment and I will drop the wiki into the chat as well so you can save it as a favorite link. And Marvin that sums up my update for the month back over to you. So James interesting information as always sounds like there are some headwinds that are emanating from the crypto space so always keeps life interesting. Absolutely. Next, I'd like to introduce James Shoning and Casey rock from the I am project. James leads the Linux foundation I am project chairs the IEEE on holiday standards working group is an, and is an advocate for self sovereign identity and personal data stores. Casey rock is a computer scientist with the US Army futures command working group, excuse me, doing cybersecurity R&D, both James and Casey introduced the integrated personal data store in a previous meeting and now they're going to talk about a more good specific application so I'm going to turn it over to them, take it away. James and Casey. Thank you I'm going to present a few slides, and then Casey is going to present a live demonstration of some of our technology. Now let's let's come back to the mortgage industry to the to actually you know what this working group was formed this, you know as I understand this group was formed, because there's this vision of how blockchain and verifiable credentials and some of the other, you know web 3.0 technologies could could bring about changes in the mortgage industry. And, and I share that vision I think Casey does too. And, but, but today we're going to, we're going to share a perhaps a slightly different perspective on the same ecosystem and that's the nice thing about ecosystems. There's a lot of parts to an ecosystem. There's a lot of players and there's a lot of different perspectives. So we're going to introduce a new one, and we're going to propose an actual pilot. Marvin's chart, you know you start with an idea, and at some point you get to where you're ready for pilot. So we're going to be proposing a pilot, and to just give you a quick preview. There's a pilot that's user centric where a user has a an identity wallet that's that also stores all of their personal data, and they, they collect this data. They collect credentials, and eventually, you know, and if they are a motivated borrower, you know if they're looking to take out a mortgage, they collect all the information. And then they are in a position to go out for quotes to post post somewhere on the internet. I am interested in quotes for my mortgage. I'm going to pause for one second to cough for a second. Casey, what could you give a little introduction here. One of the things that we've realized when it comes to these different types of blockchains and verifiable credentials is that you realize that there are standard ways to represent the metadata or the header data of these verifiable credentials. And this is going to be talking about the credential ID that the timestamp when it was created and W3C is created a standard for that. Now the part where we realized is one of the big problems with these verifiable credentials is they are specifically designed for the model that the issuer uses. So if you think about it, when somebody issues a credential, they use their own data model inside the credential to represent what information they want. Now, the holder of that wallet will then share the share the information and receive the credential. Now the thing is that we've realized is if that holder of the verifiable credential wants to go to another organization. The other organization would have to reflect the same data model that the issuer provided. There's the same consistency. So one of the things that we'll be showing today in our demo is how we solve this inconsistency with adding a standard to the data attribute layer of verifiable credential. Jim, was there anything that you wanted to. Yeah, I can. I've stopped coughing so I can take over. Again, could I share some charts here. I'm right now is telling me that I. Okay, so now let me share. All right, can you see my charts that's my last chart so let me go to my. Can you see my charts. Yeah, yeah, we can see. Okay. Okay. All right, so just like the mortgage, just like this community has a vision for the future mortgage. What it's coming from is is that the individual has a multi purpose wallet they have one wallet. Today there's a lot of wallet projects but for every project, you get it, the user gets another wallet. There's no general purpose wallets, but the industry is moving towards that you know this the interoperable standards which we're part of, the vision of person has one, one digital wallet that includes both their, it's an identity wallet for their credentials but it's also a personal data store and stores their data and data is integrated on now explain that. Okay, so what we envision is when a person is ready to apply for a mortgage, they already have most of what they need. They already have a lot of the data, a lot of their identity, you know, maybe they've already proven their income and who they work for and where they live and what property property they might on, they already have most of it. So that's going to make it a lot easier. And then when they need a mortgage, if they have all this data and if there are standards, because the data has to be collected to a standard. And they can, you know, post somewhere, I'm open for bids from to lend me money from my mortgage. And so that's a slightly different paradigm and that's the different perspective, but now we'll go, we'll start to go through what technology, our team has that makes this possible. The verifiable credentials is that that technology is moving along quite well in fact their standards for verifiable credentials that, but they stop when they get to the actual data that's inside a claim. So if you have a credential that says, I have an annual income of X, the existing standards don't standardize on what X is. We, that's the work we're doing and we're, we're not using schema technology, even though that's that state of the art, we're using ontology or standard ontology, and I'll get to that and, and another couple slides. So we're helping to create these standards that will enable people to collect data in a standard format and reuse it for other purposes. And one of the things with data is data is unique to its purpose, it's often called fit for purpose. It could work perfectly well within an application, but some other application gets a copy of that data. Can't tell what it means. You know, there's no semantics, you know. You know the small scale solution to that is, you do a mapping from one application application to another you map the terms in a data model, and that does work, but it just doesn't scale. Because the more and more applications they want to reuse the same piece of data, you have to keep mapping it and it actually scales at an n squared rate. So if there's less than five or three or four, you're okay. But if n gets to be 10, it's too much mapping, you really need a common hub. And that's where that's part of our technology. Now the hub really cannot be a traditional data model. And I'm just going to state that there's no time to prove it. If anybody wants to challenge whether a traditional data model could ever be a hub for any kind of data. I'd be happy to get involved in a discussion on that but I'm just going to state that you can't be a traditional data model, however, however it could be what we call an ontology. And I'll explain that in the following chart. The ontologies don't model data ontology's model reality, and reality actually does scale. Now, of course when you model it, you can kind of get to different perspectives, but at least it starts with something that scale. If we go to quantum levels, then, then maybe reality is not what we think it is. But for the rest of us reality is logically consistent and it does scale, and that's why these ontologies can scale ontologies are developed in a hierarchy. We have what we call the top level ontology and this is something that just in the past year we have achieved a ISO standard for a top level ontology it's called the basic formal ontology. And that's kind of small, it just really gives the framework for more specific ontologies, but but that was a huge leap because we prove we actually can standardize ontologies. And that's something that the semantic web claimed was not passed 20 years ago and then semantic web came out they said, you should never have standard ontologies you should always have stovepipe ontologies and just connect them together. And I think by, I think we've partially proven that you can have a standard ontology and many people can reuse it. And this is the mid level ontology and this is gets a lot bigger about 1600 terms. And these are the terms that are commonly used across domains. If it's specific to a domain. That's the third level down that's where you get a domain ontology but if it's common across domains. And that's what we call the common or a mid level ontology. There is one and only one mid level ontology that anybody has ever tried to develop. And that was developed by my colleagues and I within the US government and over the past 10 years I think we've invested about $7 million and developing a mid level ontology, it's very mature still needs some more work, but it's going through it's going through standardization now we're not doing it through ISO, because we want to invite individuals to help with developing that and ISO it's, you know it's more of an organization based standards than a significant amount of money, just to go to meetings. So we're doing it within IEEE, and the domain ontologies. We're doing within IEEE too. All right, so, so that's some of our technology that's that's backing up and what we call an integrated personal data store. Most data is for the individual is not going to come from the individual, you know it's going to come from other organizations like if, if you want to prove what your annual income is, you don't get to make up that number, you have to get that from your employer, maybe from the IRS. The vendors are going to create most of this data. So that data is going to come out of disparate data sources. So what, with this, these common ontologies, any, any structure data can be mapped to these standard ontologies. And then once you map them, then the instance data can be transformed and then you can this data then fits together it can be queried, it's integrated as we call it. You know, we envision that the individual will have an integrated data store made up of data coming from many different vendors. And even if it's coming from the same vendor could come from five different computer systems from that one vendor, and it still can be integrated into a common data store. Okay, so this is where Casey, are you. I will stop. And I'll pick it up from here. I'll let you provide your live tech demo. All right. So to kind of talk about what we have on this screen. On the left hand side, we have what represents an issuer. They're going to be the ones issuing credentials on the right hand side we have a holder. This is the individual that receives the credentials stores it. Now to kind of put a little bit of background about what we have on this tech demo. We're utilizing the sovereign builder blockchain network with an areas compatible wallet. So the wallet of choice that we went with with transit. They were, they took areas built some frameworks and API is around it made it accessible. So the whole picture that we were presenting and talking about in the slides was that there are standard ways to issue out credentials. So we have three credentials inside of our issuers template. Now, what they do is they'll offer a credential by typing in different types of values for the holder. Now in this case, if you notice we have will come to the credential. The credential has standardized details. So the schema, the credential ID, whether or not it's irrevocable. But when we get to the attribute level, this is where we start talking about the individual's data. There's no standard way of representing this. So this causes the problem where if multiple issuers are sending credentials, each pointing to the same value, it could be street address. But if the credential references that attribute in a different way. It doesn't match. So now when the holder receives these credentials, the data models that the credentials map to don't fit together. So here let me bring up another example. In this example, we have a verifiable credential that was designed to represent, you know, some pieces of a mortgage application. First name, last name, the date of the birth date, address line, city. These attributes in this verifiable credential are unique to the issuer. So some issuer decided that I want to receive these attributes. And I'm going to ask the holder to provide the information. So what happens next, the issuer on the left hand side sends a credential to the holder. So pull up to our digital wallet. So now the holder receives that verifiable credential from the issuer with the proper values inside the credential. So as you notice, the values are going to be very similar across multiple credentials. You know, if my first name in this example is Jane, I may have four different verifiable credentials that reference that same value, but use a different attribute name other than first name. It could be user first name, first name, name, but we're all referencing the same data, the same data point, which is the name Jane. So what we've done in this case is we've created a technology that can accept verifiable credentials. Then using the ontology technology that Jim talked about, we're able to pull out the data from the verifiable credentials and standardize it in a standard format, making it a lot more reusable across different issuers. So in this page right here, we have an example of what this looks like. We've created a standard representation for first name, last name, while keeping the records of the verifiable credentials. So the way this mapping process works is we select a credential. You select an attribute from the credential and then map it to our standard model, which would be redress. Once this data gets generated, it can now then map to our ontology. So the big picture here is we've created a way to make the data inside verifiable credentials reusable across different domains. So let me share my screen to this next part right here. This is what the data looks like underneath the hood inside of an ontology. So as Jim mentioned, we look at ontologies, the data that we have represents reality, not a data model or schema. So when we reference, we talk about standardizing upon values, we're really able to standardize upon any value that an issuer verifiable, verify our needs. So with this example, we're taking the first name example where we've standardized the attributes for first name. Now we'll start off with this red note right here. This is the ontology representation of a person and a visually. Now I think what's important to note is that this is truly the center of the data model because each connection each representation between the individual and the data that they receive has a defined relationship. So in this example we have a relationship between the individual and their full name. So we say that this individual is designated by a full name. The full name has parts this is their last name family name or their first name given name. And we map it back to the values which if you click onto here you'll see that this will contain the value dough and this will contain the value gene. So, from this view, we show that we can represent any type of data that we need to. And more importantly I want to talk about this relationship between the person this red node, and this yellow node, which is the active ownership. So we've represented data that shows how the individual participates in this action which is this red node right here we represent. And the action has an object which is the digital wallet. So, in this representation, we show how the person has physical attributes associated with them they have their name their email address the street address. And we also showed how our data model can then represent a digital wallet and the relationship between a person and a digital wall. And this further will show how the digital wallet has verifiable credentials in it, and these verifiable credentials track right back to the values. These 10 looking boxes are going to be the values of the attributes. So the important part show here with all of these nodes and edges going along in this graph structure is that we can now represent what's really happening in reality, when a person has a certain attribute and verifiable credential maps to it. And bringing us back to this picture right here when we abstract out the data structure. This picture right here leaves us with the technology that makes these data values, more integratable across different type of issuers. So that's all I have I think we'll bring it back to you Jim. James, Casey, I do have a question on the anthologies and the attributes. There was one slide you showed or when you were going into the demo. You had a box that had the attributes first name, last name, and just to me those are. Yeah. Yeah, those are the names of the data field to what extent yeah that's the exact screen I was talking about first name last name to what extent have you guys map those attributes first name last name to the MISMO naming standards, because everything that we do in mortgage or what we hope to do in mortgage is really starting to standardize around MISMO and their naming standards. Yeah, absolutely. I think one thing that we've done is our ontology, the way it maps to different data models like MISMO is practically you find an attribute inside of a data model. The way MISMO represents first name, and it'll be common among any representation of first name so I think that the big important part that I want to make here is that we're not our ontology will not be a you know, a MISMO ontology it won't be a my data ontology it'll be just a picture of reality. I think we could probably say we're probably at a 20% coverage rate with the terms we currently have, but since ontologies are very flexible and you can use a combination of terms to represent, you know, a single term. It allows us to cover a lot more data inside these data models so that's kind of my, my best answer Jim did you want to add anything to that. So our approach is very compatible and complimentary. I mean, if a person has a wallet, you know, one day they're applying for a mortgage, you know, a month later they're off using their same wallet, the same data, doing something totally different in some different industry that has its own data model. So, so the ontology will be able to map between any structured data models, any structured data, as long as there's a model, we can map to it. But also, you know, we are still developing these ontologies so we will. We will factor in the MISMO model, so that we make sure we can do a good mapping to it. So, so, and I think, you know, if I could move ahead to the next to the pilot. You'll see where we. All right, can you see my last slide here pilot proposed pilot. So this is a way in which we can work together here. And so our concept is, we're proposing that we do do a pilot in which a real person this is not a just a tech demo. Now we would, of course, do these things in a tech demo fashion. Before the real person is introduced to it, but, but the pilot would be a real person we wouldn't need to recruit a person who is planning to apply for a mortgage. And probably not somebody who needs to get that mortgage in the next month, probably somebody who's planning for it in the next three to six months. So we're going to recruit somebody like that and then the team that's going to support this, you know, Casey and I would be on that team hopefully we'd recruit some others. We're going to identify what data and credentials are typically needed by mortgage lenders. And now some mortgage lenders might need something very unique, but my guess is that the vast majority of pieces of data and specific data are common amongst mortgage lenders. Now, and then, you know, our ontologies might not cover all this data but we would that we would extend the ontologies and make sure we do cover them. So that we can then have have a draft standard and emerging standard for a person's mortgage application data. We also have to recruit some. Well, okay, so the borrowers. This real person would then gather their real data and credentials, and they would do it privately, even though this is a pilot. They're not going to share their data, unless, you know, it's in their interest to share their data. And then what we would do is we would have a parallel. We have a Jane Doe test bed where we have a fake person, a dummy dummy data so we probably do this in parallel and try to collect some of the data. And that's more shareable, but. But then also a lot of this data would not be in the right format so we would have to, you know, help that borrower transform the data into the common ontology format, and it does get stored in a triple store. So that. Then then the borrower would borrow or would post a request for quotes. Of course we'd have to tip off the lenders they might not be looking for it but we'd go recruit some lenders that would agree to try to give this person a quote for their for their mortgage. And but the lender would define their terms and conditions, especially when it comes to when it comes to sharing their data, you know, and it would be basically, I'm sharing my data for you to give me a quote but you do not you do not keep a copy of my data and you certainly can't share it or sell it or anything like that. And then if the lender agrees to the terms and conditions the borrower would then share the data and credentials. And then the lender would would map this data to their applications now, my guess is a lender has has an application they plug in the information, and it comes back and computes, whether it's a good, good loan or not. There has to be some mapping that would be done there. Now in the pilot that would probably be manual, but if this were to, you know, take off that mapping could be done in advance so it could theoretically quotes could theoretically be given automatically, based on input data or at least a preliminary quote. But that's the type of pilot we think we could, you know, help if we could recruit some participants. And that's our offer. And it's potentially a way that this could help this ecosystem evolve. Because some mortgage borrowers are highly highly motivated to get a mortgage. My interest mortgage was in 1984 and when interest rates were in the upper teens, and my state had some state backed mortgages, which I stood in line overnight to get a state back more I was highly motivated to get a mortgage. If I was told I got to collect a bunch of data credentials. No problem, I would have done that. So, it could contribute to the, you know, this vision of an ecosystem. Jim Casey, I think you guys are looking for a couple things. You want a real person or several real people to go through this process to set up a digital wallet and then to go through the mortgage application process. But you're also looking for lenders or mortgage companies that would be willing to accept those credentials as part of the mortgage application process, or have you guys already identified those types of companies. No, we don't. We don't know the lenders we, we hope you do. Okay. Okay, okay, so then you are proposing a pilot that would where they're going are going to be real people and they're going to be lenders with a loan origination system that would accept the credentials that you guys would identify or you guys would develop. That's what you guys are looking for in your play. And the first implementation that that mortgage lender, you know, is going to get these data and credentials and they're probably going to manually have to replug it into their applications or whatever for the pilot purposes, but theoretically that could all be automated. Because these these ontologies this data can be mapped to any other, you know, unique business application. Okay. Well, there are several lenders and quite a few people that work with lenders that are a part of this group. Casey, if you guys could put your contact information in the chat, and then for anyone on the call, anyone that downloads this recording and it's interested in participating or helping out your pilot, I would encourage them to reach out to you, either as a real person taking getting those quotes, or being one of the lenders that would provide those quotes and help process the mortgage application. Okay, that would be great. Great. Thank you guys for walking us through this information and this pilot. I think a, I think it leverages the type of blockchain technology and use case that we advocate for it in our subgroup and I think this is great technology that is going to be useful and it's just getting a pilot out there I think that's usually the hardest part so if there's anyone that's interested or can help you I encourage them to reach out to you. Appreciate it. Thank you. Okay. Thanks guys. Thank you. Are there any questions from the rest of the team for Jim and Casey. I think they walked us through an excellent presentation and demo and I'm going to go back to sharing my screen. So, any questions for the rest of the team. Yeah, Jim Casey take we appreciate you taking the time to walk through this it's a fantastic idea that you guys got so hopefully we'll be able to get you connected with some potentials. Great. Thanks again guys and I'm going to go to the next slide so going back to future agenda topics. We do have someone lined up for our October 13 meeting this is going to be David Fitzgerald. I'm going to talk about property records and a smart contract project that he did in wise County I think this may have been something that we've covered or briefly mentioned in an earlier meeting but David will be able to walk through us through this project in a lot more detail I think it's another example of a great use case for blockchain so hopefully you guys can join that. In the meeting after that November 10 we're contemplating a blockchain panel discussion, or another demo from another blockchain LOS provider I'm still trying to finalize that, and then December eight so those are future agenda topics. If there's anything that the people on this call are interested in, or would like to discuss, please let us know either in the chat or choose an email, we want to make this as interactive as possible and also support the interest of the entire team. With that, that brings us to the end of our session. I want to open it up to any questions and any feedback from the rest of the team and participants. I can see that Jim and Casey entered their information into the chat so thank you guys. Okay, if there aren't any questions I show 948. Thank you to everyone for joining this call. This call has been recorded and will be available in the next couple days when the hyper ledger team converts it and post it on to the YouTube link. Thank you everyone for joining us and have a great rest of your day.