1 00:00:00,000 --> 00:00:11,360 Oh, man, I saw it's like in the 60s there. 2 00:00:11,360 --> 00:00:12,600 We got a cold snap, buddy. 3 00:00:12,600 --> 00:00:20,460 It's just, yeah, it was like not last night, the night before it got down to like 45. 4 00:00:20,460 --> 00:00:24,440 And I was like, wow, it's very unseasonably cool right now. 5 00:00:24,440 --> 00:00:25,440 I'm jealous, man. 6 00:00:25,440 --> 00:00:28,360 It's gonna be 109 here on Saturday. 7 00:00:28,360 --> 00:00:31,520 What? 8 00:00:31,520 --> 00:00:33,440 What part of Oregon are you in? 9 00:00:33,440 --> 00:00:34,440 Portland. 10 00:00:34,440 --> 00:00:38,720 It's gonna be 109 in Portland, Oregon? 11 00:00:38,720 --> 00:00:40,320 That's what they're forecasting, yes. 12 00:00:40,320 --> 00:00:41,320 Oh my gosh. 13 00:00:41,320 --> 00:00:48,160 I mean, so to me, you know, based on my limited knowledge, that seems like way out of character. 14 00:00:48,160 --> 00:00:51,440 It is, especially this early in the year. 15 00:00:51,440 --> 00:00:52,600 Oh my gosh. 16 00:00:52,600 --> 00:00:56,320 I just thought because you're closer to the coast, it'd be and kind of north, it'd be 17 00:00:56,320 --> 00:00:58,760 cooler a bit. 18 00:00:58,760 --> 00:00:59,760 Nope. 19 00:00:59,760 --> 00:01:03,280 Oh my gosh. 20 00:01:03,280 --> 00:01:08,320 Okay, we need to mix up the weather from your state and my state. 21 00:01:08,320 --> 00:01:09,320 Yes. 22 00:01:09,320 --> 00:01:16,000 Well, one thing that's interesting is, it'd be interesting to see if these heat snaps 23 00:01:16,000 --> 00:01:18,720 have an effect on harvest, right? 24 00:01:18,720 --> 00:01:27,160 That's notorious for these farmers, right, is there's one bad event and then, you know, 25 00:01:27,160 --> 00:01:28,160 there's a monkey wrenching. 26 00:01:28,160 --> 00:01:29,160 Yeah. 27 00:01:29,160 --> 00:01:30,160 Yeah. 28 00:01:30,160 --> 00:01:31,160 Yeah. 29 00:01:31,160 --> 00:01:37,320 You're gonna have to start doing, you're an economics guy, Keegan, in, what do they call 30 00:01:37,320 --> 00:01:38,320 it? 31 00:01:38,320 --> 00:01:40,160 Futures trading or something like that? 32 00:01:40,160 --> 00:01:50,640 Or like different agricultural type products? 33 00:01:50,640 --> 00:01:56,240 Well it may be soon with cannabis where they're doing futures on yields. 34 00:01:56,240 --> 00:02:01,520 So yields in production and yields in processing. 35 00:02:01,520 --> 00:02:02,520 Never know. 36 00:02:02,520 --> 00:02:03,520 You never know. 37 00:02:03,520 --> 00:02:11,480 So, well, just, I see Heather's joined us again today, so essentially, Charles has done 38 00:02:11,480 --> 00:02:18,960 some diligent work parsing data this past week and I've put together a user interface 39 00:02:18,960 --> 00:02:27,640 and so now there's just a little bit of business logic left and we'll have a rudimentary laboratory 40 00:02:27,640 --> 00:02:30,160 information management system put together. 41 00:02:30,160 --> 00:02:33,360 Well, congratulations guys, that's cool. 42 00:02:33,360 --> 00:02:34,360 Exactly. 43 00:02:34,360 --> 00:02:38,920 So, like, there's still a few pieces to fill in, but yeah, it's coming together well. 44 00:02:38,920 --> 00:02:46,200 So if you want, I could just show you at some point today just what's together and then, 45 00:02:46,200 --> 00:02:52,040 yeah, maybe mostly of a talking day today because I've mostly been in the weeds with 46 00:02:52,040 --> 00:03:02,520 that, but I'm curious, have any of you had any data adventures this past week? 47 00:03:02,520 --> 00:03:09,760 Heather kind of grimaced there for a second. 48 00:03:09,760 --> 00:03:13,560 Observed data. 49 00:03:13,560 --> 00:03:17,560 I'll be an observer today. 50 00:03:17,560 --> 00:03:19,680 Okay. 51 00:03:19,680 --> 00:03:25,160 So I did a little bit of work this week on my graduate project. 52 00:03:25,160 --> 00:03:32,480 Unfortunately, most of it's been writing, so my introduction, everything for my paper, 53 00:03:32,480 --> 00:03:42,840 but I was able in Google Cloud in their AI platform to feed in my first set of data into 54 00:03:42,840 --> 00:03:47,000 the market basket analysis algorithm. 55 00:03:47,000 --> 00:03:48,520 I haven't really looked at the results much. 56 00:03:48,520 --> 00:03:52,480 I was just trying to get the code to work properly. 57 00:03:52,480 --> 00:04:00,400 So probably by next week, I think I will have some basic results to share if we wanted to 58 00:04:00,400 --> 00:04:07,160 as far as some association rules that come out of the retail transactions at some dispensaries. 59 00:04:07,160 --> 00:04:12,360 So I might have something rudimentary to share next week. 60 00:04:12,360 --> 00:04:13,360 Okay. 61 00:04:13,360 --> 00:04:22,560 And so these are just sort of associations between purchases that you may not be really 62 00:04:22,560 --> 00:04:24,320 apparent on first glance? 63 00:04:24,320 --> 00:04:25,320 Yeah. 64 00:04:25,320 --> 00:04:28,880 So there's going to be association rules on two different levels. 65 00:04:28,880 --> 00:04:34,880 The first level will be on association rules between the different product categories. 66 00:04:34,880 --> 00:04:38,780 So there's eight product categories in the Washington data set. 67 00:04:38,780 --> 00:04:44,400 And then the other association rules will be actually down at the product level to see 68 00:04:44,400 --> 00:04:50,080 if any interesting relationships pop up there as far as complimentary products being bought 69 00:04:50,080 --> 00:04:51,320 together. 70 00:04:51,320 --> 00:04:58,320 That one's going to be more challenging because the data set that needs to feed the algorithm 71 00:04:58,320 --> 00:05:07,680 is very, very sparse, but it should be handled in the AI platform in Google Cloud. 72 00:05:07,680 --> 00:05:12,440 But that one's going to be a little harder to tease some intel out of it just because 73 00:05:12,440 --> 00:05:18,320 some of the measures for these association rules become pretty diluted. 74 00:05:18,320 --> 00:05:19,320 But we'll see. 75 00:05:19,320 --> 00:05:22,120 And that's part of this whole process is kind of exploring this data. 76 00:05:22,120 --> 00:05:23,360 So we'll see what pops out. 77 00:05:23,360 --> 00:05:27,520 But to begin with, it's just going to be looking at the categories of the different products 78 00:05:27,520 --> 00:05:30,120 to see how those relate. 79 00:05:30,120 --> 00:05:31,120 So yeah. 80 00:05:31,120 --> 00:05:36,000 So of course, I think the category is going to be promising. 81 00:05:36,000 --> 00:05:38,120 The sample name is going to be all over the board. 82 00:05:38,120 --> 00:05:39,880 So that may be difficult. 83 00:05:39,880 --> 00:05:40,880 Yeah. 84 00:05:40,880 --> 00:05:51,440 Do you have any data points on total price, like the total amount per receipt or number 85 00:05:51,440 --> 00:05:54,640 of items by chance? 86 00:05:54,640 --> 00:06:04,000 I mean, that information's in the raw data, but for the market basket analysis, I'm just 87 00:06:04,000 --> 00:06:09,600 incorporating the product categories at the moment. 88 00:06:09,600 --> 00:06:13,480 That particular modeling, not that I'm aware of that. 89 00:06:13,480 --> 00:06:19,880 You may be able to incorporate pricing in some capacity, but I haven't really, if that's 90 00:06:19,880 --> 00:06:24,120 what you're thinking of, I haven't really considered that angle yet. 91 00:06:24,120 --> 00:06:34,160 Well, what I'm trying to get at is basically, in general, there is maybe low quality and 92 00:06:34,160 --> 00:06:40,360 high quality cannabis. 93 00:06:40,360 --> 00:06:43,680 How you differentiate the two may be just sort of arbitrary. 94 00:06:43,680 --> 00:06:46,400 So it may not be the best. 95 00:06:46,400 --> 00:06:51,340 Well, keep thinking along those lines, because this is just a starting point. 96 00:06:51,340 --> 00:06:57,640 And if it gives us a springboard into looking at some different angles, that would be useful. 97 00:06:57,640 --> 00:07:03,720 One of the things that I had sent some information to you, I think it was last week, and Charles 98 00:07:03,720 --> 00:07:09,000 and Heather, just to kind of fill you in, to do this type of analysis, typically you 99 00:07:09,000 --> 00:07:14,560 do it in retail stores and you're looking for, I gave the analogy before, but if I buy 100 00:07:14,560 --> 00:07:19,600 peanut butter at my grocery store, then I might be also likely to buy bread and jam. 101 00:07:19,600 --> 00:07:23,800 And that's kind of what this algorithm does, is it shows you those relationships. 102 00:07:23,800 --> 00:07:33,720 But I wanted to look at some representative dispensaries to get kind of, treat them like 103 00:07:33,720 --> 00:07:40,960 flagship dispensaries that they might be representative of the industry. 104 00:07:40,960 --> 00:07:45,180 And Keegan helped me out a little bit last week with some good questions and some advice 105 00:07:45,180 --> 00:07:55,120 on looking at dispensaries by median income of their zip codes, because he had noticed 106 00:07:55,120 --> 00:08:00,800 previously that sometimes there might be a relationship between median income and kind 107 00:08:00,800 --> 00:08:05,880 of the product base at a certain dispensary and how much they're selling and things like 108 00:08:05,880 --> 00:08:06,880 that. 109 00:08:06,880 --> 00:08:13,880 So I use that median income as a way of trying to differentiate some of the dispensaries 110 00:08:13,880 --> 00:08:19,760 and that was helpful. 111 00:08:19,760 --> 00:08:26,120 So I'm using that as kind of my basis for picking, in my project I said I'm picking 112 00:08:26,120 --> 00:08:30,920 three dispensaries, but I'll probably actually look at more dispensaries and look at the 113 00:08:30,920 --> 00:08:33,540 association patterns of more than just three. 114 00:08:33,540 --> 00:08:35,120 But that analysis kind of helps. 115 00:08:35,120 --> 00:08:41,180 So thanks for that Keegan, just kind of recommending to go down that path and look at those. 116 00:08:41,180 --> 00:08:45,120 So yeah, so that's kind of where I am right now. 117 00:08:45,120 --> 00:08:50,360 Like I said, I hope to have something to share next week, some rudimentary stuff, but Keegan, 118 00:08:50,360 --> 00:08:57,240 I know that we're meeting Friday here in Detroit. 119 00:08:57,240 --> 00:09:04,040 So but I may have something to show you on Friday as well, but we'll see. 120 00:09:04,040 --> 00:09:07,640 I do need to bring you up a little bit to speed on some of the more of the details of 121 00:09:07,640 --> 00:09:10,120 the project, but I don't want to. 122 00:09:10,120 --> 00:09:11,120 So that's cool. 123 00:09:11,120 --> 00:09:19,280 And yeah, just so everyone knows, we're attending CannaCon in Detroit on Friday, which should 124 00:09:19,280 --> 00:09:20,960 be big and it's exciting. 125 00:09:20,960 --> 00:09:31,640 And so I'll get to learn a lot about the Michigan market, which I don't know much about. 126 00:09:31,640 --> 00:09:39,480 And then basically I just started to think about, okay, so what makes the dispensaries 127 00:09:39,480 --> 00:09:40,480 look different? 128 00:09:40,480 --> 00:09:41,480 Right. 129 00:09:41,480 --> 00:09:44,080 And so I'm sure you've seen them. 130 00:09:44,080 --> 00:09:50,400 So there's, you know, sometimes you have the classier, the nicer looking dispensaries, 131 00:09:50,400 --> 00:09:56,000 then they'll maybe be like in a prime real estate. 132 00:09:56,000 --> 00:10:02,360 Like so for example, in Portland, like in like, I'm sure in downtown Portland, there's 133 00:10:02,360 --> 00:10:04,360 like some pretty nice dispensaries. 134 00:10:04,360 --> 00:10:09,560 No, man, they keep all that stuff out of downtown. 135 00:10:09,560 --> 00:10:10,560 Yes. 136 00:10:10,560 --> 00:10:16,560 And so anything that's anything that can be perceived as seedy, they move out to the neighborhoods. 137 00:10:16,560 --> 00:10:23,200 But yeah, there are, well, there's sort of like the bigger chains now, right? 138 00:10:23,200 --> 00:10:27,240 There are places, you know, that have like four or five, I think there's some places 139 00:10:27,240 --> 00:10:29,760 that have like 10 locations, right? 140 00:10:29,760 --> 00:10:35,800 And they have nicer stores, and then they're like, you know, the smaller, sort of like 141 00:10:35,800 --> 00:10:39,800 mom and pop places where they only have like one location. 142 00:10:39,800 --> 00:10:46,240 And so yeah, they're, the chains are kind of like in nicer neighborhoods and the stores 143 00:10:46,240 --> 00:10:50,840 are really nice and clean looking. 144 00:10:50,840 --> 00:10:58,660 And yeah, I don't know about, yeah, I don't really know that there's a big difference 145 00:10:58,660 --> 00:11:03,200 in pricing. 146 00:11:03,200 --> 00:11:07,200 What about just product, like the products they stock? 147 00:11:07,200 --> 00:11:17,600 Like, they all stock the same products or does it store the chains? 148 00:11:17,600 --> 00:11:18,600 I don't know. 149 00:11:18,600 --> 00:11:21,280 I'm not, you know, like a store of these things. 150 00:11:21,280 --> 00:11:23,400 And that's why we'll have to look at the data, right? 151 00:11:23,400 --> 00:11:25,280 Because it would just be anecdotal evidence. 152 00:11:25,280 --> 00:11:28,840 So we'll have to kind of parse this out. 153 00:11:28,840 --> 00:11:34,520 But I know they're like, they're billboards around town, you know, like one chain, you 154 00:11:34,520 --> 00:11:41,080 know, like they're the exclusive carrier of, you know, Tommy Chong brand, and he's the 155 00:11:41,080 --> 00:11:45,280 exclusive carrier of Snoop Dogg's brand. 156 00:11:45,280 --> 00:11:54,800 It's interesting that you're talking about this, Charles, you remember I-75 here in Detroit. 157 00:11:54,800 --> 00:11:59,760 That's the main north-south highway that leads, that passes right by Detroit and goes north. 158 00:11:59,760 --> 00:12:04,080 Well, I had to drive down I-75 yesterday to get to the airport to pick up my mom and I 159 00:12:04,080 --> 00:12:05,840 had to go through Detroit. 160 00:12:05,840 --> 00:12:10,920 And to your point about these billboards, almost all the billboards on I-75 are all 161 00:12:10,920 --> 00:12:13,160 cannabis billboards now. 162 00:12:13,160 --> 00:12:18,400 And it's been about four or five months since I've been down that highway. 163 00:12:18,400 --> 00:12:23,400 It is amazing how much it's changed, the billboards in that period of time. 164 00:12:23,400 --> 00:12:33,000 It's either cannabis dispensary billboards or lawyer billboards. 165 00:12:33,000 --> 00:12:40,520 So to your point, yeah, about just the advertising and everything, I noticed it as well here. 166 00:12:40,520 --> 00:12:43,360 I think that that could be an interesting study. 167 00:12:43,360 --> 00:12:48,560 I don't know how you'd get the data, but yeah, because people are always trying to find out, 168 00:12:48,560 --> 00:12:50,120 does advertising pay off? 169 00:12:50,120 --> 00:12:54,400 Like what's the return on investment for advertising? 170 00:12:54,400 --> 00:13:02,320 If you could think of a way to measure that, that'd be an incredibly interesting question. 171 00:13:02,320 --> 00:13:04,280 Yeah. 172 00:13:04,280 --> 00:13:14,920 I did have a chart and Keegan, I sent this chart to you via email last week, but I've 173 00:13:14,920 --> 00:13:20,760 added some additional information to it or pointed out some different information within 174 00:13:20,760 --> 00:13:22,840 this chart. 175 00:13:22,840 --> 00:13:28,160 And maybe later on in the conversation, we can talk about it as a group, because I would 176 00:13:28,160 --> 00:13:31,800 just be curious to what people thought about the data that I had in this chart. 177 00:13:31,800 --> 00:13:41,280 It's essentially median income versus the 2020 dispensary annual sales. 178 00:13:41,280 --> 00:13:45,680 And you see some interesting relationships that emerge out of that chart. 179 00:13:45,680 --> 00:13:50,760 But anyway, it's something that if anybody's interested in looking at, maybe we can talk 180 00:13:50,760 --> 00:13:51,760 about it. 181 00:13:51,760 --> 00:13:56,080 You can share your screen if you want, and we can talk about the chart real quick. 182 00:13:56,080 --> 00:13:57,080 Okay. 183 00:13:57,080 --> 00:13:58,080 Okay. 184 00:13:58,080 --> 00:14:06,480 Let me share a window here one second. 185 00:14:06,480 --> 00:14:11,480 Let me know if you can see this. 186 00:14:11,480 --> 00:14:12,480 Yes. 187 00:14:12,480 --> 00:14:13,480 Okay. 188 00:14:13,480 --> 00:14:16,040 All right. 189 00:14:16,040 --> 00:14:22,720 So on the bottom axis here, the X axis, we've got median income by zip code. 190 00:14:22,720 --> 00:14:28,840 So this is an anomaly here, but basically median income and increases and you get something 191 00:14:28,840 --> 00:14:34,400 median income up to over $140,000 up here. 192 00:14:34,400 --> 00:14:40,200 And on the left-hand side, you see annual sales from dispensaries. 193 00:14:40,200 --> 00:14:46,200 So some of the top selling dispensaries are up here around, was it 14 million? 194 00:14:46,200 --> 00:14:47,200 Am I reading that correctly? 195 00:14:47,200 --> 00:14:48,200 Yeah. 196 00:14:48,200 --> 00:14:52,120 $14 million annual 2020 sales. 197 00:14:52,120 --> 00:14:57,120 So this is per your suggestion, Keegan, and thank you again for recommending I look at 198 00:14:57,120 --> 00:14:59,440 this. 199 00:14:59,440 --> 00:15:07,960 So what we see is that the really high selling dispensaries tend to, well, we've got a smattering 200 00:15:07,960 --> 00:15:09,040 of them. 201 00:15:09,040 --> 00:15:14,520 It goes down a little bit here, but the main high selling dispensaries here kind of fall 202 00:15:14,520 --> 00:15:22,760 in the middle of the median income curve or maybe not the middle, but kind of lower section 203 00:15:22,760 --> 00:15:24,760 here. 204 00:15:24,760 --> 00:15:31,360 Well, I get a representation of the higher selling dispensaries across the median incomes 205 00:15:31,360 --> 00:15:33,560 by zip code. 206 00:15:33,560 --> 00:15:41,200 So for my project, I'm going to pick dispensaries that are in this oval so I can get a good representation 207 00:15:41,200 --> 00:15:44,160 across median income. 208 00:15:44,160 --> 00:15:47,680 But what I think is interesting is some of these other areas. 209 00:15:47,680 --> 00:15:53,080 So this big block here on the lower left-hand side, that's where the vast majority of the 210 00:15:53,080 --> 00:15:57,800 operators are, right, the vast majority of the dispensaries operate in this lower median 211 00:15:57,800 --> 00:16:02,120 income area and they have lower sales. 212 00:16:02,120 --> 00:16:03,120 All right. 213 00:16:03,120 --> 00:16:08,480 So this may be the mom and pop quadrant or whatever we call it. 214 00:16:08,480 --> 00:16:15,100 Here we have lower annual sales, but in higher median income areas. 215 00:16:15,100 --> 00:16:20,060 So also mom and pop, but in more wealthier areas. 216 00:16:20,060 --> 00:16:25,440 And then you get the ones that are just more successful regardless of median income and 217 00:16:25,440 --> 00:16:26,760 they just sell more, right? 218 00:16:26,760 --> 00:16:32,080 They kind of bubble up here and you got this kind of transitional area where I've got the 219 00:16:32,080 --> 00:16:39,160 little triangle and these are folks that are just seem to be making their way up the annual 220 00:16:39,160 --> 00:16:43,280 sales range. 221 00:16:43,280 --> 00:16:44,460 And then you get a few outliers. 222 00:16:44,460 --> 00:16:50,940 So on the lower right-hand corner here, you see these guys are again, probably mom and 223 00:16:50,940 --> 00:16:55,620 pop shops, but in high income areas. 224 00:16:55,620 --> 00:17:00,400 What I thought was really interesting, and I don't know if you can see this and probably 225 00:17:00,400 --> 00:17:09,280 I'm obscuring it a little bit, but in median income, you kind of get this division right 226 00:17:09,280 --> 00:17:12,080 going up here. 227 00:17:12,080 --> 00:17:19,820 So you see high income and lower income, and it seems to be like this kind of somewhat 228 00:17:19,820 --> 00:17:22,280 of a gap in between. 229 00:17:22,280 --> 00:17:27,120 I just thought it means kind of an artifact of the data, but I thought that was kind of 230 00:17:27,120 --> 00:17:33,600 interesting that median income, I mean, I'm not saying haves and have nots. 231 00:17:33,600 --> 00:17:37,480 It's not that differentiating, but it's kind of- 232 00:17:37,480 --> 00:17:38,480 Curious. 233 00:17:38,480 --> 00:17:39,480 Yeah, it is curious. 234 00:17:39,480 --> 00:17:44,760 It just seems like that's just the way that the cookie crumbled here. 235 00:17:44,760 --> 00:17:48,760 We see two main groupings of median income. 236 00:17:48,760 --> 00:17:55,240 But yeah, it's just interesting to go through this exercise. 237 00:17:55,240 --> 00:18:00,800 What I would like to see over time would be interesting is how much of these dispensaries 238 00:18:00,800 --> 00:18:04,820 as they mature move upwards. 239 00:18:04,820 --> 00:18:11,200 Maybe they move upwards, or how many of these dots disappear as they're going to be consolidation. 240 00:18:11,200 --> 00:18:16,320 And then the consolidation, I'm sure, would probably push some of these dispensaries to 241 00:18:16,320 --> 00:18:21,120 be more profitable as they get bought by other companies maybe. 242 00:18:21,120 --> 00:18:24,320 I don't know, just kind of thinking out loud. 243 00:18:24,320 --> 00:18:27,720 It's a fun exercise to do this. 244 00:18:27,720 --> 00:18:31,600 Obviously, it brings up more questions than answers. 245 00:18:31,600 --> 00:18:34,080 I think these are helpful groupings. 246 00:18:34,080 --> 00:18:39,800 Like how you identified the big cluster at the bottom. 247 00:18:39,800 --> 00:18:46,480 I think you're right where those are people who are basically, they can get property where 248 00:18:46,480 --> 00:18:48,200 they can find it. 249 00:18:48,200 --> 00:18:53,760 Maybe they don't have the best location. 250 00:18:53,760 --> 00:19:00,640 They may not be paying that much for rent, and they have low sales. 251 00:19:00,640 --> 00:19:04,560 Looks like similar people in those same neighborhoods, like you said, are breaking out. 252 00:19:04,560 --> 00:19:12,160 And so that could even be, those may even be chains in the lower income neighborhoods 253 00:19:12,160 --> 00:19:18,720 where they've just got, maybe they were able to get a slightly better location. 254 00:19:18,720 --> 00:19:21,200 Sure. 255 00:19:21,200 --> 00:19:27,200 This right here, if you, okay, so see my oval? 256 00:19:27,200 --> 00:19:32,320 So the actual dispensaries that are in that oval are the records that are in this kind 257 00:19:32,320 --> 00:19:34,800 of yellow color. 258 00:19:34,800 --> 00:19:46,160 So the top grossing dispensary was this one in Wenatchee, if I'm saying that properly. 259 00:19:46,160 --> 00:19:50,880 Crafts they're called craft cannabis in Wenatchee. 260 00:19:50,880 --> 00:19:56,240 Then you've got Main Street, marijuana in Vancouver. 261 00:19:56,240 --> 00:20:01,480 But you can see these kind of top tier, when I say top tier, they're highest producing 262 00:20:01,480 --> 00:20:03,280 across the median scale. 263 00:20:03,280 --> 00:20:09,760 But here, what I noticed right off the bat is, so look, you've got Main Street, marijuana, 264 00:20:09,760 --> 00:20:13,400 and then you've got Main Street, marijuana East. 265 00:20:13,400 --> 00:20:15,560 So they've been around a long time. 266 00:20:15,560 --> 00:20:21,080 They were one of the first dispensaries in the area, because Vancouver is just across 267 00:20:21,080 --> 00:20:24,360 the Columbia River from Portland. 268 00:20:24,360 --> 00:20:29,560 Okay, so they're well established. 269 00:20:29,560 --> 00:20:31,040 Yeah. 270 00:20:31,040 --> 00:20:35,160 And yeah, that's... 271 00:20:35,160 --> 00:20:41,200 I would almost add a classifier to those as like zero or one, and I would give them a 272 00:20:41,200 --> 00:20:45,680 one for chain. 273 00:20:45,680 --> 00:20:52,960 And so it's going to be tough to identify these, but if you have a clever way, maybe 274 00:20:52,960 --> 00:20:58,720 if they each include their names, because you can kind of identify these as like the 275 00:20:58,720 --> 00:21:03,320 brand is Main Street, marijuana, and they've got two locations. 276 00:21:03,320 --> 00:21:12,880 Yeah, that would be a good differentiator between those. 277 00:21:12,880 --> 00:21:20,840 Yeah, it would just really be cool to monitor this over time, just to see how the market 278 00:21:20,840 --> 00:21:24,080 is starting to mature or develop. 279 00:21:24,080 --> 00:21:27,560 And then the question is, why do they break away? 280 00:21:27,560 --> 00:21:36,800 So I met somebody from Zip's Cannabis, and they're a pretty big, well established dispensary 281 00:21:36,800 --> 00:21:38,560 in Tacoma. 282 00:21:38,560 --> 00:21:43,400 And it could just be... 283 00:21:43,400 --> 00:21:48,920 One of those things is once you start getting your market dominance, then you're able to 284 00:21:48,920 --> 00:21:53,360 grow, and it's hard for other people to get a foothold. 285 00:21:53,360 --> 00:21:56,320 Everybody knows you, you've got a customer base. 286 00:21:56,320 --> 00:21:59,840 So what is it? 287 00:21:59,840 --> 00:22:03,480 Do they just have a really well-placed location? 288 00:22:03,480 --> 00:22:06,600 That's a possibility. 289 00:22:06,600 --> 00:22:07,600 Yeah. 290 00:22:07,600 --> 00:22:08,600 Yeah. 291 00:22:08,600 --> 00:22:15,600 And you were mentioning also about doing some regression in this to kind of tease out some 292 00:22:15,600 --> 00:22:20,240 of those drivers behind this. 293 00:22:20,240 --> 00:22:25,040 That's not part of my graduate project, but it would be fun just to do that, just to kind 294 00:22:25,040 --> 00:22:30,240 of start teasing out some of these variables. 295 00:22:30,240 --> 00:22:35,440 And this kind of work, to know what the drivers behind market growth and development... 296 00:22:35,440 --> 00:22:40,280 I mean, this is stuff that you're probably familiar with, Keegan, but that kind of information 297 00:22:40,280 --> 00:22:49,800 is going to be very useful for market consolidators, these kind of people with big pockets that 298 00:22:49,800 --> 00:22:54,840 are going to go into this world and look to pull the market together. 299 00:22:54,840 --> 00:22:59,320 I'm sure that they would be very interested in that kind of information. 300 00:22:59,320 --> 00:23:00,320 Exactly. 301 00:23:00,320 --> 00:23:02,000 It's just how do you measure it? 302 00:23:02,000 --> 00:23:03,000 And so... 303 00:23:03,000 --> 00:23:04,000 Yeah. 304 00:23:04,000 --> 00:23:08,400 And this is sort of a wild idea, but... 305 00:23:08,400 --> 00:23:13,600 One point I listened to an economic seminar and they were talking... 306 00:23:13,600 --> 00:23:15,880 It was a health one. 307 00:23:15,880 --> 00:23:19,200 They were talking about, oh, it's not good for your health to live through a certain 308 00:23:19,200 --> 00:23:21,920 proximity to a road. 309 00:23:21,920 --> 00:23:28,120 And I think they were somehow measuring the width of the roads, whether that was lanes 310 00:23:28,120 --> 00:23:31,120 or highways or something. 311 00:23:31,120 --> 00:23:39,720 So I wonder if there's any way to sort of give in the location of the dispensary to 312 00:23:39,720 --> 00:23:44,440 know what number of lanes road is that on? 313 00:23:44,440 --> 00:23:46,120 Is it on a two-lane road? 314 00:23:46,120 --> 00:23:49,120 Is that on a four-lane road? 315 00:23:49,120 --> 00:23:55,240 Because basically what I'm trying to parse out is I've got a sneaking suspicion that 316 00:23:55,240 --> 00:24:01,680 some of these are just well-placed and then that gives them the opportunity for growth. 317 00:24:01,680 --> 00:24:05,240 So they're just... 318 00:24:05,240 --> 00:24:12,240 For example, the Green Lady in Olympia, they've got Green Lady East and Green Lady West. 319 00:24:12,240 --> 00:24:17,000 They're both almost outside of city limits because I don't know if you're really allowed 320 00:24:17,000 --> 00:24:19,760 to have something in like downtown Olympia. 321 00:24:19,760 --> 00:24:23,920 And so they basically just have one on each side of Olympia. 322 00:24:23,920 --> 00:24:31,520 So they literally have the Olympia Market cornered. 323 00:24:31,520 --> 00:24:34,800 Yeah, no, that makes a lot of sense. 324 00:24:34,800 --> 00:24:41,480 And I'm sure that lots of retail companies have probably gone down this path many, many 325 00:24:41,480 --> 00:24:42,880 times with this kind of analysis. 326 00:24:42,880 --> 00:24:48,320 I mean, how do you choose the right location for a store? 327 00:24:48,320 --> 00:24:52,840 I'm sure that that's probably well-documented approach to doing this kind of stuff. 328 00:24:52,840 --> 00:25:01,720 It might be just worthwhile to see what already exists in literature to see what we could 329 00:25:01,720 --> 00:25:05,240 snag and use for that kind of analysis. 330 00:25:05,240 --> 00:25:11,320 A lot of this space, I mean, the work that you're doing in the lab world is probably 331 00:25:11,320 --> 00:25:17,360 much more nuanced and unique, but this retail stuff has been done many times over just in 332 00:25:17,360 --> 00:25:19,360 different markets. 333 00:25:19,360 --> 00:25:24,440 Well, it's just a hot area of interest. 334 00:25:24,440 --> 00:25:29,320 So at one point, someone who does a bunch of data analytics told me some like four big 335 00:25:29,320 --> 00:25:33,720 areas and one of the biggest areas is the consumer. 336 00:25:33,720 --> 00:25:36,640 So what's driving the consumer? 337 00:25:36,640 --> 00:25:43,120 A lot of people are just guessing, but like I said, people are starting to uncover it, 338 00:25:43,120 --> 00:25:45,480 but I think it's all interrelated. 339 00:25:45,480 --> 00:25:58,680 So my current job here at General Motors, I'm starting a new position in July 15th and 340 00:25:58,680 --> 00:26:03,200 I'm going over to the customer experience team. 341 00:26:03,200 --> 00:26:09,360 So I'm knowing I'm going to be doing a lot of work in this space as far as customer analytics 342 00:26:09,360 --> 00:26:11,840 and consumer behavior and that type of thing. 343 00:26:11,840 --> 00:26:19,440 So what I'm learning there, I'm sure it could be applicable or what I'll be doing there, 344 00:26:19,440 --> 00:26:22,920 some of it can be applicable to this space as well. 345 00:26:22,920 --> 00:26:25,720 So yeah, should be interesting. 346 00:26:25,720 --> 00:26:34,460 So one thing that I've noticed, I think a lot of these places, they're places that compete 347 00:26:34,460 --> 00:26:38,120 on price, right? 348 00:26:38,120 --> 00:26:41,380 Because when you're driving down, when you're driving to the grocery store, every other 349 00:26:41,380 --> 00:26:50,480 block there's a sign spinner, $3 grams, $1 grams. 350 00:26:50,480 --> 00:26:56,160 They're more sign spinners than billboards. 351 00:26:56,160 --> 00:26:57,560 So there's that. 352 00:26:57,560 --> 00:27:05,680 And then I also, I talked to someone and they were saying a big driver now is like the higher 353 00:27:05,680 --> 00:27:11,200 THC, which is probably not the low price stuff. 354 00:27:11,200 --> 00:27:12,200 I don't know. 355 00:27:12,200 --> 00:27:19,080 Again, this is not something I study a whole lot, but I think that's kind of like there's 356 00:27:19,080 --> 00:27:22,600 two sort of markets. 357 00:27:22,600 --> 00:27:33,480 There's the people who are price sensitive and there are the people that want their thunderbird 358 00:27:33,480 --> 00:27:36,480 of marijuana. 359 00:27:36,480 --> 00:27:43,880 That's a great analogy. 360 00:27:43,880 --> 00:27:46,880 Yeah. 361 00:27:46,880 --> 00:27:56,000 I mean, to that point in this oval here, I think because of the median income distribution, 362 00:27:56,000 --> 00:28:01,040 I think it's going to be interesting to see what kind of association rules we get with, 363 00:28:01,040 --> 00:28:07,680 let's say this point here and this point over here, the difference kind of product associations 364 00:28:07,680 --> 00:28:09,080 that might come out of this, right? 365 00:28:09,080 --> 00:28:15,920 Are they more of the upscale market that you're talking about or is this more of the thunderbird 366 00:28:15,920 --> 00:28:16,920 market over here? 367 00:28:16,920 --> 00:28:22,640 I don't know, but yeah, we'll definitely get a chance to look at it. 368 00:28:22,640 --> 00:28:27,520 Hey, I had a question on your data. 369 00:28:27,520 --> 00:28:35,320 So just from the basics, are unemployment benefits included as a source of income in 370 00:28:35,320 --> 00:28:37,680 this data? 371 00:28:37,680 --> 00:28:38,680 I don't know. 372 00:28:38,680 --> 00:28:40,060 This is a good question. 373 00:28:40,060 --> 00:28:48,160 This median income data came from the government census website and I'm not exactly sure how 374 00:28:48,160 --> 00:28:53,100 they calculated the median income, but that's a good question and I'm glad you asked it 375 00:28:53,100 --> 00:28:58,440 because I should have a sense of how that data was developed. 376 00:28:58,440 --> 00:29:01,440 It's not common sense because I mean, I browse Reddit a lot. 377 00:29:01,440 --> 00:29:06,620 So just speaking to your point about THC, I mean, unemployment and even just Medicaid 378 00:29:06,620 --> 00:29:12,740 benefits are something that can just allow somebody to still use cannabis as a method 379 00:29:12,740 --> 00:29:18,480 of treatment for their pain or whatever, but high percent THC, at least in the state that 380 00:29:18,480 --> 00:29:26,720 I live is not necessarily the most expensively priced of cannabis flowers that is. 381 00:29:26,720 --> 00:29:31,720 But yeah, anyway, so unemployment can make that happen. 382 00:29:31,720 --> 00:29:36,880 I just don't know if 40,000 is even achievable on unemployment benefits in that area, but 383 00:29:36,880 --> 00:29:43,640 it could be almost achievable here because of our special circumstances. 384 00:29:43,640 --> 00:29:48,760 Yeah, that's interesting. 385 00:29:48,760 --> 00:29:51,360 Heather you're in Colorado, right? 386 00:29:51,360 --> 00:29:52,360 I'm in Maryland. 387 00:29:52,360 --> 00:29:54,640 Oh my gosh, I was not even close. 388 00:29:54,640 --> 00:29:57,280 I thought for some reason you're in Colorado, so you're in Maryland. 389 00:29:57,280 --> 00:29:58,280 Okay. 390 00:29:58,280 --> 00:29:59,280 That's okay. 391 00:29:59,280 --> 00:30:00,680 Yeah, I mean, I was so percent percent wise. 392 00:30:00,680 --> 00:30:04,960 I mean, I can get like usually the higher percentage THC I can get from a particular 393 00:30:04,960 --> 00:30:09,600 carrier, and that's usually the cheapest, believe it or not. 394 00:30:09,600 --> 00:30:18,720 But yeah, we have way overpriced stuff for no reason, like 18%, 22% THC, come on, $65 395 00:30:18,720 --> 00:30:19,720 for an eighth. 396 00:30:19,720 --> 00:30:22,960 Yeah, you're going to get the hustling, right? 397 00:30:22,960 --> 00:30:28,280 That's just going to be people trying to maximize their profits. 398 00:30:28,280 --> 00:30:33,140 That's going to happen regardless. 399 00:30:33,140 --> 00:30:38,240 That's interesting to hear about the price points though in Maryland. 400 00:30:38,240 --> 00:30:44,720 I'll look to see if there's any Maryland data because that's just, I mean, it's anecdotal, 401 00:30:44,720 --> 00:30:51,400 but it sounds like quite different than what's going on in Washington or Oregon or California. 402 00:30:51,400 --> 00:30:52,400 So yeah. 403 00:30:52,400 --> 00:30:57,000 So good observation. 404 00:30:57,000 --> 00:31:03,720 That's why it's important to have these types of meetings because especially in this market, 405 00:31:03,720 --> 00:31:04,720 right? 406 00:31:04,720 --> 00:31:09,880 In different states and how it's evolving differently, it really is interesting to get 407 00:31:09,880 --> 00:31:10,880 those perspectives. 408 00:31:10,880 --> 00:31:18,240 Well, it's wild because I mean, I mean, what other good is, well, I'm sure there's others, 409 00:31:18,240 --> 00:31:25,320 but you know, there's like different state markets and there's some overlap, like so 410 00:31:25,320 --> 00:31:31,120 maybe like an equipment producer in Colorado will sell to people all over the country. 411 00:31:31,120 --> 00:31:40,160 Right? There's a lot of, you know, little medium sized businesses all over and the different 412 00:31:40,160 --> 00:31:46,260 regulations obviously have an effect on the market outcome. 413 00:31:46,260 --> 00:31:54,340 So it's, I'm interested to see, okay, how many suppliers, how many producers are there 414 00:31:54,340 --> 00:31:56,340 in Maryland? 415 00:31:56,340 --> 00:32:02,280 Because that could be an interesting data point is, so that's something, that's a reason 416 00:32:02,280 --> 00:32:04,680 that a lot of people were attracted to Oklahoma. 417 00:32:04,680 --> 00:32:11,000 It was the, it's the highest number of cultivators per capita. 418 00:32:11,000 --> 00:32:12,400 Wow. 419 00:32:12,400 --> 00:32:19,340 So all like the equipment producers, like all the equipment manufacturers, we're all 420 00:32:19,340 --> 00:32:27,400 interested in serving the Oklahoma market because you've got some, I think there's 7,000 421 00:32:27,400 --> 00:32:28,400 licensees. 422 00:32:28,400 --> 00:32:33,560 I forget if there's a role of cultivation, but there's thousands of cultivation licenses. 423 00:32:33,560 --> 00:32:41,160 So they all need soil, fertilizer, pots. 424 00:32:41,160 --> 00:32:46,360 So accountants, you know, you name it. 425 00:32:46,360 --> 00:32:50,280 So they, so there's a lot of demand for services. 426 00:32:50,280 --> 00:32:51,280 Yeah. 427 00:32:51,280 --> 00:32:56,600 And that's, I was going to say, that's hard to believe. 428 00:32:56,600 --> 00:33:01,680 I mean, I feel like either Southern Oregon or Northern California must have the most 429 00:33:01,680 --> 00:33:03,160 producers. 430 00:33:03,160 --> 00:33:11,920 Well, what my hypothesis is, is, let's take California, for example, I bet you it's hard 431 00:33:11,920 --> 00:33:19,440 to get a foothold there because you're going to be competing with people who are well established 432 00:33:19,440 --> 00:33:23,520 and may have incredibly large operations. 433 00:33:23,520 --> 00:33:26,820 So and they're probably competitive. 434 00:33:26,820 --> 00:33:36,040 So you know, it's going to be tough to set up shop thing, but other places, you know, 435 00:33:36,040 --> 00:33:38,240 it may be easier to enter. 436 00:33:38,240 --> 00:33:43,760 And then what I'm also interested to see is, okay, like what's the persistent rate? 437 00:33:43,760 --> 00:33:49,880 So you know, just because there's a lot of licenses now in Oklahoma, it may balance out 438 00:33:49,880 --> 00:33:51,640 in a couple of years. 439 00:33:51,640 --> 00:33:55,200 So you know, it may reach an equilibrium. 440 00:33:55,200 --> 00:33:59,360 So we'll see. 441 00:33:59,360 --> 00:34:03,600 Heather in Maryland, what's the status? 442 00:34:03,600 --> 00:34:07,840 Is it just medical marijuana that's legal or is it? 443 00:34:07,840 --> 00:34:08,840 Correct. 444 00:34:08,840 --> 00:34:09,840 Yeah. 445 00:34:09,840 --> 00:34:12,480 And that's completely political as far as I can see. 446 00:34:12,480 --> 00:34:17,280 That is so we have these, you know, we have a number of licenses that can be distributed 447 00:34:17,280 --> 00:34:23,760 and some of the those with the heavier pockets are still doing, you know, trying to enforce 448 00:34:23,760 --> 00:34:25,600 higher prices. 449 00:34:25,600 --> 00:34:26,760 And it's just silly. 450 00:34:26,760 --> 00:34:29,280 But anyway, so yeah, medical only. 451 00:34:29,280 --> 00:34:31,240 I'm a medical card holder. 452 00:34:31,240 --> 00:34:35,640 So people just the generals. 453 00:34:35,640 --> 00:34:41,840 I mean, I don't want to say general, but I would say the majority view, at least on Maryland 454 00:34:41,840 --> 00:34:47,120 Reddit or cannabis, is that they just they want legalization. 455 00:34:47,120 --> 00:34:53,120 So if they don't have college park cops tasing people just because they have cannabis on 456 00:34:53,120 --> 00:34:56,400 the word they vape, you know, that still is happening. 457 00:34:56,400 --> 00:35:01,020 But looking from the business standpoint, they don't want to disturb anything because 458 00:35:01,020 --> 00:35:06,240 their favorite dispensary is doing fine or their their host of strains that they use 459 00:35:06,240 --> 00:35:09,960 is is consistent and they just don't want to rock the boat. 460 00:35:09,960 --> 00:35:12,900 So it's a it's a double edged sword over here. 461 00:35:12,900 --> 00:35:16,840 So I don't think legalization is going to happen, even though we had Virginia. 462 00:35:16,840 --> 00:35:22,480 Can you imagine Virginia legal like they're looking to I thought that that was like on 463 00:35:22,480 --> 00:35:24,280 like happening right now. 464 00:35:24,280 --> 00:35:26,560 I'm like, OK, so we're never going to do that. 465 00:35:26,560 --> 00:35:27,560 Never. 466 00:35:27,560 --> 00:35:34,840 I got a feeling that maybe as people from Maryland might start crossing state lines 467 00:35:34,840 --> 00:35:38,640 to go get better deals, it'll it'll even out eventually. 468 00:35:38,640 --> 00:35:44,080 I don't think they can hold on to that control for as long as they would like. 469 00:35:44,080 --> 00:35:45,080 Yeah. 470 00:35:45,080 --> 00:35:49,680 I mean, I wish you know, but go ahead, please. 471 00:35:49,680 --> 00:35:54,960 It just shows you the market forces, right? 472 00:35:54,960 --> 00:36:01,520 Because, you know, like you said, people just go to Virginia, then, you know, Maryland's 473 00:36:01,520 --> 00:36:06,320 going to realize they're missing out on some tax revenue and then they may change some 474 00:36:06,320 --> 00:36:09,320 things at that point. 475 00:36:09,320 --> 00:36:10,320 Yeah. 476 00:36:10,320 --> 00:36:15,320 Well, incredibly interesting. 477 00:36:15,320 --> 00:36:29,480 Well, should we take 10 minutes or so and just look at the laboratory information management 478 00:36:29,480 --> 00:36:30,480 system so far? 479 00:36:30,480 --> 00:36:31,480 Be great. 480 00:36:31,480 --> 00:36:32,480 I'm excited. 481 00:36:32,480 --> 00:36:33,480 All right. 482 00:36:33,480 --> 00:36:39,520 So just keep in mind that we're still working on a bit of the functionality. 483 00:36:39,520 --> 00:36:44,880 But let me get this spun up real quick and then I'll just give it a quick demo. 484 00:36:44,880 --> 00:36:51,120 Okay. 485 00:36:51,120 --> 00:36:58,680 Thanks. 486 00:37:28,680 --> 00:37:51,080 I'll just go ahead and share my screen and we'll just get this spun up. 487 00:37:51,080 --> 00:38:10,080 So, it's signed into a mock account real quick. 488 00:38:10,080 --> 00:38:30,080 Create an account. 489 00:38:30,080 --> 00:38:47,080 Her graphics are very professional. 490 00:38:47,080 --> 00:39:12,080 First things first, we'll just get this account just set up with an organization real quick. 491 00:39:12,080 --> 00:39:27,080 So, everybody can just use it for free and then if they need integration with the state traceability system, then we can help them out with that. 492 00:39:27,080 --> 00:39:37,080 Really, I'll just sort of run you through the main data points here that the laboratory collects. 493 00:39:37,080 --> 00:39:44,080 So, everything really evolves around your analyses. 494 00:39:44,080 --> 00:40:07,080 So, this is where you're going to, you're basically just going to do, you're essentially going to have your cannabinoid analysis and you'll add analytes. 495 00:40:07,080 --> 00:40:17,080 So, analytes are the compounds that you test in your analysis. 496 00:40:17,080 --> 00:40:24,080 So, for example, you know, you may have, you know, PHC. 497 00:40:24,080 --> 00:40:28,080 You know, there's not going to be a limit to that. 498 00:40:28,080 --> 00:40:37,080 So, the LOD is the lowest order of detection that the lab can actually detect. 499 00:40:37,080 --> 00:40:42,080 So, for example, it may just be, you know, 0.01%. 500 00:40:42,080 --> 00:40:49,080 But it's just going to be the anything that they measure that's lower than 0.01%. 501 00:40:49,080 --> 00:40:52,080 They're going to report as non-detected. 502 00:40:52,080 --> 00:41:01,080 Because the way, you know, science works is, you know, you're always trying to, you know, disprove a negative. 503 00:41:01,080 --> 00:41:08,080 So, you know, you can, you know, you can prove that there's something there, but, you know, you can never disprove a negative. 504 00:41:08,080 --> 00:41:12,080 So, it's always got to be, you know, non-detected. 505 00:41:12,080 --> 00:41:25,080 So, you can't, you know, firmly, you can't, you know, 100, you can't 100% say that there's no THC whatsoever, but you didn't, you didn't detect them. 506 00:41:25,080 --> 00:41:30,080 So, LOQ is the lowest order of quantification. 507 00:41:30,080 --> 00:41:33,080 So, this is what they can actually quantify. 508 00:41:33,080 --> 00:41:39,080 So, this you would expect to be slightly above your LOD. 509 00:41:39,080 --> 00:41:46,080 So, anything above the LOQ, they can report to the client. 510 00:41:46,080 --> 00:41:49,080 So, they can just put that on their certificate. 511 00:41:49,080 --> 00:41:54,080 And that's the official measurement. 512 00:41:54,080 --> 00:42:00,080 If it's between the LOQ and the LOD, it's technically an estimate. 513 00:42:00,080 --> 00:42:07,080 So, they have detected something, but they can't quantify it. 514 00:42:07,080 --> 00:42:10,080 So, different laboratories will approach this differently. 515 00:42:10,080 --> 00:42:13,080 Some will report it as a non-detect. 516 00:42:13,080 --> 00:42:16,080 Others will report it as an estimate. 517 00:42:16,080 --> 00:42:30,080 Here in Oklahoma, I believe they actually have it regulated that they have to put it on their certificate as less than the LOQ. 518 00:42:30,080 --> 00:42:33,080 The analytes. 519 00:42:33,080 --> 00:42:36,080 And then just a couple of formalities. 520 00:42:36,080 --> 00:42:51,080 You know, you'll have areas where you keep things in your contacts to keep track of your clients that you test for. 521 00:42:51,080 --> 00:42:55,080 Then, of course, you need scientific instruments. 522 00:42:55,080 --> 00:43:00,080 And so, these are what are going to be doing the analysis. 523 00:43:00,080 --> 00:43:08,080 And so, what's critical is they'll be spitting out data to a certain place in your network. 524 00:43:08,080 --> 00:43:24,080 So, typically, you'll specify your data path. 525 00:43:24,080 --> 00:43:29,080 So, it'll typically be a path to your instrument's data. 526 00:43:29,080 --> 00:43:39,080 And then this will be collected intermittently to stock up your measurements. 527 00:43:39,080 --> 00:43:43,080 So, you know, there's just a few things here. 528 00:43:43,080 --> 00:43:50,080 For example, people will need to keep the instrument maintained and calibrated. 529 00:43:50,080 --> 00:43:55,080 Instrument is technically a piece of inventory. 530 00:43:55,080 --> 00:44:00,080 But, you know, of course, labs have many pieces of inventory. 531 00:44:00,080 --> 00:44:06,080 So, we need to keep those well managed. 532 00:44:06,080 --> 00:44:08,080 But back to the instruments. 533 00:44:08,080 --> 00:44:20,080 So, you've got your instruments performing your analyses and your instruments generate measurements. 534 00:44:20,080 --> 00:44:26,080 So, your measurements will be for a specific sample. 535 00:44:26,080 --> 00:44:31,080 And they'll be for a specific analyte. 536 00:44:31,080 --> 00:44:36,080 So, this will be your measurement for THC. 537 00:44:36,080 --> 00:44:42,080 And what's going to happen is the instrument will measure your THC. 538 00:44:42,080 --> 00:44:50,080 It may measure it in 600 parts per million. 539 00:44:50,080 --> 00:44:59,080 You've weighed out, you know, let's say half a gram of cannabis. 540 00:44:59,080 --> 00:45:03,080 And you've now diluted it by a certain factor. 541 00:45:03,080 --> 00:45:08,080 The standard method is a dilution factor of 10. 542 00:45:08,080 --> 00:45:14,080 So, you've now done your analyst measurement. 543 00:45:14,080 --> 00:45:20,080 And you've recorded your instrument measurement. 544 00:45:20,080 --> 00:45:33,080 You now apply the analyte's formula to get your final results. 545 00:45:33,080 --> 00:45:45,080 So, your results will be your, you know, I mean, you need to add a label here. 546 00:45:45,080 --> 00:45:54,080 But, you know, your results will be your 20% for your THC. 547 00:45:54,080 --> 00:46:04,080 Or, you know, maybe your 0.3% if you're growing hemp. 548 00:46:04,080 --> 00:46:07,080 You've got your results. 549 00:46:07,080 --> 00:46:11,080 And then these are all for your samples. 550 00:46:11,080 --> 00:46:22,080 And so, basically, the final piece that's needed is to generate a certificate for these results. 551 00:46:22,080 --> 00:46:28,080 So, that's the final piece of business logic that I need to finish. 552 00:46:28,080 --> 00:46:40,080 And so, basically, what you'll do when you, you know, click, you know, create certificate is it will then get the sample data points. 553 00:46:40,080 --> 00:46:46,080 So, that will get, you know, your sample name, your sample ID. 554 00:46:46,080 --> 00:46:58,080 It will get the contact information, so that way you can put your contact's address, city, and information on their certificate. 555 00:46:58,080 --> 00:47:02,080 And then it may even grab some project variables. 556 00:47:02,080 --> 00:47:09,080 So, a project is a group of samples sent in at a specific time by the client. 557 00:47:09,080 --> 00:47:13,080 And so, then this may have a transfer ID. 558 00:47:13,080 --> 00:47:18,080 So, for example, in Oklahoma, all samples need to be recorded. 559 00:47:18,080 --> 00:47:25,080 So, whenever a client sends you a transfer, there will be transfer data. 560 00:47:25,080 --> 00:47:34,080 And so, this will just be the organization that it's coming from, as well as information about the transporter. 561 00:47:34,080 --> 00:47:41,080 So, these are all the data points that the laboratory works with. 562 00:47:41,080 --> 00:47:55,080 And so, I've basically just put together a simple, what they call a CRUD in the software world, which is create, read, update, and delete. 563 00:47:55,080 --> 00:48:07,080 So, now the, you know, lab could, you know, create, read, update, and delete data for all of these data points. 564 00:48:07,080 --> 00:48:11,080 So, it's not a complete lens. 565 00:48:11,080 --> 00:48:17,080 Like I said, there's still a lot of business logic that needs to be written. 566 00:48:17,080 --> 00:48:27,080 For example, the next thing that really needs to be tackled is a COA generation mechanism. 567 00:48:27,080 --> 00:48:38,080 But we're, you know, well on our way to having something functional that labs can use. 568 00:48:38,080 --> 00:48:41,080 And, you know, there's traceability support. 569 00:48:41,080 --> 00:48:55,080 So, that way, if a client's integrated with metric, they can see their packages, their lab tests, their locations, transfers and settings. 570 00:48:55,080 --> 00:49:14,080 I've written the logic for, say, processors and producers and potentially retailers to also use the platform, primarily just to send samples in for testing. 571 00:49:14,080 --> 00:49:28,080 That functionality got put on the back burner because at the moment I'm just trying to finish everything needed for the, you know, for the labs. 572 00:49:28,080 --> 00:49:30,080 That's nice work. 573 00:49:30,080 --> 00:49:32,080 Oh, thanks Paul. 574 00:49:32,080 --> 00:49:39,080 Just kind of putting it together bit by bit. 575 00:49:39,080 --> 00:49:52,080 So, you know, it's that way. It's primarily targeted towards laboratories that are operating in states that are using metric. 576 00:49:52,080 --> 00:49:56,080 Ideally, do you have a question? 577 00:49:56,080 --> 00:50:07,080 Looking at from the perspective of an outsider going through this, and I'm sure you guys have probably already thought about this, but, you know, there's obviously 578 00:50:07,080 --> 00:50:14,080 quite a lot of effort that lab techs will have to do to, you know, make the data entry stuff in here. 579 00:50:14,080 --> 00:50:22,080 Have you thought about kind of like creating menus that are pre-populated templates they can use over and over again? 580 00:50:22,080 --> 00:50:26,080 Excellent, excellent question. And good suggestion. 581 00:50:26,080 --> 00:50:42,080 I have thought about doing essentially what we call worksheets. So having exactly templates or worksheets where, say, you know, a client or say an analyst creates a project. 582 00:50:42,080 --> 00:50:58,080 You know, ideally, like you said, some of these things can be pre-populated and perhaps you could even, you know, print off a form, whether that's you actually printed off or whether it's just a digital form. 583 00:50:58,080 --> 00:51:14,080 So that way, that way, yeah, analysts can use worksheets because a lot of laboratories are already familiar with using worksheets. A lot of analysts are familiar with using worksheets. 584 00:51:14,080 --> 00:51:39,080 So it is a worthwhile mechanism to collect data. And so I think it is worthwhile to include some sort of worksheet creation, some sort of worksheet generation tool that can also, you know, feed data back into the system. 585 00:51:39,080 --> 00:51:44,080 That's really cool. Good work, guys. 586 00:51:44,080 --> 00:51:56,080 And I want to give a shout out to Charles, who's really done the heavy lifting to actually write the routines that parse the instrument data. 587 00:51:56,080 --> 00:52:13,080 So when you're building out a project like this, there's what I like to, I consider it three different aspects. So in the software world, they might call it model view controller, whatever you want to call it. 588 00:52:13,080 --> 00:52:26,080 Basically, I call it there's your user interface. Then you've got your business logic and then you need something to glue them together. And then I call that the API. 589 00:52:26,080 --> 00:52:43,080 So the user interface is coming together. Like I said, there still needs to be some, you know, interesting features here to create certificates. 590 00:52:43,080 --> 00:52:46,080 That shouldn't be too hard to do. 591 00:52:46,080 --> 00:53:03,080 And so the business logic is a lot of it is getting written. So here, analytics is a Python module. 592 00:53:03,080 --> 00:53:08,080 And there's a LIMS module in there. 593 00:53:08,080 --> 00:53:20,080 And there, Charles has begun to write the business logic for collecting results from instruments. 594 00:53:20,080 --> 00:53:38,080 So for example, we're collecting results from Agilent BCs to do procedural solvents and terpenes. And Charles is written routine to import heavy metals. 595 00:53:38,080 --> 00:53:46,080 And so just to show you what we're actually parsing here. 596 00:53:46,080 --> 00:53:58,080 So let's take a look at some of the test data. 597 00:53:58,080 --> 00:54:23,080 This is sort of a typical data file that would be spit. We're not spit. This is a typical data file that would be generated by an Agilent instrument. 598 00:54:23,080 --> 00:54:35,080 So, or maybe for legacy sake, things don't change much. So between the different years and different versions, this is generally what you would expect. 599 00:54:35,080 --> 00:54:41,080 And, you know, that's not a bad thing. So maybe they did the design their data files that way. 600 00:54:41,080 --> 00:55:03,080 So that way, you know, scientists can continue to collect these with the data collection routines that they may have written a long time ago. And in the laboratory, it's important to not break things that are working, especially if they're being used in production to do analysis. 601 00:55:03,080 --> 00:55:18,080 So long story short, data files have not changed much over the years. And so sheet one, you generally just have some of the sample identifiers. 602 00:55:18,080 --> 00:55:29,080 This is where you really get your sample name. And then you have many tabs here that are related to, you know, your actual instrument. 603 00:55:29,080 --> 00:55:38,080 I'm just curious. I mean, how many instruments and their associated data files do you think you'll have to accommodate? 604 00:55:38,080 --> 00:55:59,080 Well, we're trying to do six off of the bat. So that would let me get this right. So that would be residual solvents, herpes, cannabinoids, pesticides, heavy metals, and micro toxins. 605 00:55:59,080 --> 00:56:10,080 So you can screen for micro toxins through the LCMS. The pesticides are also through the LCMS. 606 00:56:10,080 --> 00:56:27,080 So those data collection routines are similar, but they have different compounds. And so I'll show you the similarity here. So this is a data file for residual solvents. 607 00:56:27,080 --> 00:56:36,080 So if you look here on the compound sheet, you'll see the name. And I just put in fictitious amounts. 608 00:56:36,080 --> 00:56:49,080 But these names are your analytes, so propane. 609 00:56:49,080 --> 00:57:06,080 If you look at the propane data, 610 00:57:06,080 --> 00:57:27,080 you'll see that the workbook is quite similar. In fact, it's laid out the exact same, except on the compound sheet, we now have different compounds. 611 00:57:27,080 --> 00:57:41,080 So we now have terpenes. So when we import these in, importing in terpenes and residual solvents, the logic is the same. 612 00:57:41,080 --> 00:57:52,080 We just have to control for the analyte names. So we just have to match the analyte names up appropriately. 613 00:57:52,080 --> 00:58:07,080 The data files aren't quite the same. So as Charles found out, the heavy metals looks quite different than the rest. 614 00:58:07,080 --> 00:58:14,080 And then the pesticides and micro toxins will also look different than the rest. 615 00:58:14,080 --> 00:58:23,080 And we are tentatively going to add a seventh, which would be importing microbials. 616 00:58:23,080 --> 00:58:35,080 So that would be when you screen for microbial contaminants, you do it through QPCR and QPCR can generate a data file. 617 00:58:35,080 --> 00:58:47,080 I've never parsed one of these before, but 618 00:58:47,080 --> 00:59:06,080 centrally, it's just an Excel file. And we're basically going to need to read this in, apply a good bit of logic, especially to the micro, and then you get this data stored. 619 00:59:06,080 --> 00:59:17,080 So to answer your question, we're starting off with a handful and we're sort of going to add more as laboratories demand them. 620 00:59:17,080 --> 00:59:28,080 But we figured we could start with the high demand instruments. And so I would like to add some Shemazdu instruments. 621 00:59:28,080 --> 00:59:34,080 I've met a couple of contacts from Shemazdu at CannaCon, so I'm trying to work that out. 622 00:59:34,080 --> 00:59:50,080 We've got some Agilent examples. So depending on the model of someone's Agilent HPLC or Agilent GC, 623 00:59:50,080 --> 01:00:04,080 they can import their data thanks to Charles's diligent work writing the data collection functions. 624 01:00:04,080 --> 01:00:15,080 Now is really just time for really the glue plus a little bit of automation. 625 01:00:15,080 --> 01:00:24,080 So really, we've got the user interface finished, the business logic. Charles has finished a good chunk. 626 01:00:24,080 --> 01:00:31,080 I still need to write a little bit to create certificates, but that's on the agenda. 627 01:00:31,080 --> 01:00:46,080 And then we'll essentially leverage an API. And so all this API does is facilitates use of the business logic, 628 01:00:46,080 --> 01:00:58,080 either through the user interface or potentially programmatically if somebody wanted to make requests to the Canlytics API. 629 01:00:58,080 --> 01:01:10,080 So I learned a principle, sort of the API design. And so really, I just broke this whole project up into, 630 01:01:10,080 --> 01:01:16,080 OK, what are the data models that people need to keep track of? 631 01:01:16,080 --> 01:01:26,080 And so then each data model is essentially an API endpoint. And I don't think this one's implemented yet. 632 01:01:26,080 --> 01:01:38,080 But basically, it's just going to allow people to get, update or create and delete data. 633 01:01:38,080 --> 01:01:54,080 And so you can now interface with, well, once I finish the API, you'll be able to interface with all of these data models. 634 01:01:54,080 --> 01:02:01,080 Each one will have a handful of, more than a handful of data points. 635 01:02:01,080 --> 01:02:07,080 And then we can also leverage all of the business logic. 636 01:02:07,080 --> 01:02:15,080 And so it's almost a fully functional lens at that point. Well, I don't think it's fully functional at that point. 637 01:02:15,080 --> 01:02:25,080 But then the cherry on top is you can add a little bit of automation. So that way you can, 638 01:02:25,080 --> 01:02:39,080 say you can write a script that will use the Canlytics module, collect data from your instrument and upload the data through the API. 639 01:02:39,080 --> 01:02:51,080 And then you can look at it in your user interface. So it's got all the pieces to be an incredible system. 640 01:02:51,080 --> 01:03:02,080 Just needs a couple finishing touches. And so this coming week, I will be touching up the API. 641 01:03:02,080 --> 01:03:11,080 And hopefully it'll be fully functional by the next time we speak. 642 01:03:11,080 --> 01:03:21,080 So last week, we promised to get the instrument parsing done. That was done. 643 01:03:21,080 --> 01:03:30,080 Thanks, most solely to Charles, because I've been, like I said, I've been just trying to get this user interface up. 644 01:03:30,080 --> 01:03:38,080 I just can't thank Charles enough because now we can read in the data. 645 01:03:38,080 --> 01:03:46,080 And next week we can get this thing generating certificates. 646 01:03:46,080 --> 01:03:53,080 That's pretty cool. So this would be offered as a web service? 647 01:03:53,080 --> 01:04:00,080 Exactly. So I originally was building it out as a desktop app. 648 01:04:00,080 --> 01:04:10,080 And then I realized that really the only desktop functionality you need is to collect data from the instruments. 649 01:04:10,080 --> 01:04:23,080 So eventually I figured, okay, why not just make it into like a web app and then just have a small little either script. 650 01:04:23,080 --> 01:04:31,080 I'm going to start off with a script, but I may like to have just almost a tiny little standalone desktop app. 651 01:04:31,080 --> 01:04:37,080 But all it does is collect results from your scientific instruments. 652 01:04:37,080 --> 01:04:40,080 Sure, that could be a good offering. 653 01:04:40,080 --> 01:04:48,080 So one of the things that hurts me while you're talking through this is if it's a web service. 654 01:04:48,080 --> 01:04:56,080 So the angle of privacy, right? 655 01:04:56,080 --> 01:05:06,080 So different labs that buy your product, they're probably going to want insurances for privacy and their own data and that sort of thing. 656 01:05:06,080 --> 01:05:10,080 So I imagine you've probably done a little bit thinking about that. 657 01:05:10,080 --> 01:05:22,080 But I know at General Motors with the OnStar app where we track vehicle telemetry, the customer has to opt in for that. 658 01:05:22,080 --> 01:05:25,080 And all the data we collect is anonymous, right? 659 01:05:25,080 --> 01:05:29,080 We can't tell who owns the vehicle or any of that kind of stuff. 660 01:05:29,080 --> 01:05:32,080 So we respect the other privacy. 661 01:05:32,080 --> 01:05:38,080 But at the same time, we get to aggregate that data and use it for other things to help customers out. 662 01:05:38,080 --> 01:05:42,080 So, you know, just have to think about for this service, right? 663 01:05:42,080 --> 01:05:49,080 I mean, there's going to be tons of value in aggregated data behind the scenes, but it probably would have to be anonymized in some way. 664 01:05:49,080 --> 01:05:55,080 You hit on an interesting point here is so, you know, obviously labs. 665 01:05:55,080 --> 01:05:57,080 They want to keep everything private. 666 01:05:57,080 --> 01:06:02,080 And so, of course, you know, we respect everybody's data with our privacy policy. 667 01:06:02,080 --> 01:06:19,080 However, if a lab wants to set up their very own analytics implementation so that way they can leverage all of the functionality of analytics and they have oversight over their own database, 668 01:06:19,080 --> 01:06:38,080 then essentially, I know analytics is open source and I've written quite detailed instructions here on how you can get analytics up and running. 669 01:06:38,080 --> 01:06:49,080 Essentially in looking for the you can get analytics up and running in a Docker container. 670 01:06:49,080 --> 01:06:55,080 So anywhere you can run a Docker container, you can run can live. 671 01:06:55,080 --> 01:07:04,080 So I'm running it just in the cloud, just to offer it as a web service to anyone in the world. 672 01:07:04,080 --> 01:07:12,080 And more is, you know, so this is a kind of platform and it's almost an example of yes, this can be done. 673 01:07:12,080 --> 01:07:15,080 We're doing it. You can use ours if you would like. 674 01:07:15,080 --> 01:07:27,080 And if labs have the know how and a lot do because a lot do have technical staff, a lot are building out their own limbs. 675 01:07:27,080 --> 01:07:41,080 So we've provided this so that labs can spin this up and then they can serve it from their they could either serve it from their own cloud instance. 676 01:07:41,080 --> 01:07:45,080 They could serve it from an on site server. 677 01:07:45,080 --> 01:07:48,080 You know, you name it. 678 01:07:48,080 --> 01:08:01,080 It can be done. And so basically we were providing something, you know, something for everybody. So, you know, the labs that want to do it themselves. 679 01:08:01,080 --> 01:08:06,080 We're providing the infrastructure for them to do that. 680 01:08:06,080 --> 01:08:13,080 And in the guide, if they need a little support, we can provide some support. 681 01:08:13,080 --> 01:08:16,080 Very cool. And then exactly. 682 01:08:16,080 --> 01:08:23,080 And then for people who need just the cookie cutter solution, they just need something that works out of the box. 683 01:08:23,080 --> 01:08:33,080 Then we're, you know, hosting our own implementation at console dot analytics dot com. 684 01:08:33,080 --> 01:08:40,080 That way. You don't have to have technical staff to get off the ground. 685 01:08:40,080 --> 01:08:45,080 You can get started in five minutes. You can collect all your data. 686 01:08:45,080 --> 01:08:53,080 Then you can either import your data or say you're ready to go. You're ready to start your own implementation. 687 01:08:53,080 --> 01:09:02,080 You can export all of your data, import it into your new implementation, and then you're off to the races. 688 01:09:02,080 --> 01:09:12,080 So that's great. So you can onboard smaller outfits and as they grow, you can accommodate their growth. 689 01:09:12,080 --> 01:09:19,080 Exactly. And so we're sort of leaving, you know, the door open for some some new additions. 690 01:09:19,080 --> 01:09:29,080 So, for example, I have planned but have not yet implemented in invoicing, you know, an invoicing endpoint. 691 01:09:29,080 --> 01:09:36,080 That way you can keep track of your invoices or your projects and, you know, your samples. 692 01:09:36,080 --> 01:09:42,080 That way you can send invoices to your clients. 693 01:09:42,080 --> 01:09:45,080 I thought that one was not mission critical. 694 01:09:45,080 --> 01:09:53,080 So I pulled that one through the time being, but that one can easily be added in into the future. 695 01:09:53,080 --> 01:10:04,080 And so as analytics starts working with more and more laboratories, hopefully we can find out what each laboratory's unique needs are. 696 01:10:04,080 --> 01:10:19,080 So that way we can start improving the platform, adding more and more features, adding more and more data points and making testing as simple and easy as possible. 697 01:10:19,080 --> 01:10:24,080 Definitely. You can see you guys put a lot of thought into it. It's pretty neat. 698 01:10:24,080 --> 01:10:29,080 Do you have any what they call mean viable product, right? 699 01:10:29,080 --> 01:10:35,080 Do you have anybody that's kind of testing this for you yet or getting feedback on it? 700 01:10:35,080 --> 01:10:49,080 Yes. So we're working with a laboratory here in Tulsa, essentially just doing early stage feedback, just finding out, OK, you know, what data points do you need? 701 01:10:49,080 --> 01:10:54,080 What's your ideal workflow? 702 01:10:54,080 --> 01:11:00,080 So exactly. So we're trying to exactly get the minimal viable product out. 703 01:11:00,080 --> 01:11:06,080 And really the last step. Well, there's two last steps that are needed. 704 01:11:06,080 --> 01:11:12,080 One, Charles has written the logic to. 705 01:11:12,080 --> 01:11:28,080 To parse the instrument data. Now I just need to write a script to automatically upload those measurements so that way, you know, that way people don't have to sit here and tediously type in measurements. 706 01:11:28,080 --> 01:11:35,080 Those are just going to be flowing automatically from the instrument. 707 01:11:35,080 --> 01:11:42,080 And then the other final piece is actually generating the certificate. 708 01:11:42,080 --> 01:11:44,080 So. 709 01:11:44,080 --> 01:11:48,080 These the certificate of analysis. 710 01:11:48,080 --> 01:11:56,080 Is the product that laboratories are selling essentially they are they're in the service industry, right? 711 01:11:56,080 --> 01:12:01,080 They're providing the analysis service. How do you know the analysis was done? 712 01:12:01,080 --> 01:12:07,080 So we've got a certificate of analysis, though. 713 01:12:07,080 --> 01:12:25,080 Ultimately, the certificate is what signifies that everything's been done and the certificate needs all of the data points on there and it needs to be generated in a simple and easy way and delivered to the client. 714 01:12:25,080 --> 01:12:33,080 We've approached this as we just need to collect all of the data points as simply and easily as possible. 715 01:12:33,080 --> 01:12:44,080 So now I'll in the coming week be writing a COA generation routine and I'll put this in the analytics module. 716 01:12:44,080 --> 01:12:53,080 And basically what that's going to do is it's just going to take a template, whether that's a word in Excel. 717 01:12:53,080 --> 01:13:04,080 Probably start with those two. So just in a word or an Excel template and use like Jenga style. So Jenga would just say, oh, you know. 718 01:13:04,080 --> 01:13:10,080 Project ID goes in between like two squiggly lines, two curly brackets. 719 01:13:10,080 --> 01:13:14,080 So we'll do some Jenga style templates. 720 01:13:14,080 --> 01:13:22,080 And then actually generate a certificate. So that would be your PDF saying that the analysis was done. 721 01:13:22,080 --> 01:13:29,080 So that is the final step and to generate to do our, you know, our minimal viable product. 722 01:13:29,080 --> 01:13:41,080 Because so once you can take all of the data points, you can collect your measurements and then you can generate your certificate. 723 01:13:41,080 --> 01:14:03,080 And then you've done the laboratory test from start to finish. You've received your transfer, created your project, created your samples, added your analysis, tested for all the analytes, collected the measurements. 724 01:14:03,080 --> 01:14:21,080 And then you issue your COA and then that will just be essentially a URL where your client, it'll be a private secure URL that only they will know. 725 01:14:21,080 --> 01:14:40,080 And so then they can get their certificate and that's what they're looking for. So that's what the producers and processors need because they need that certificate to be able to wholesale their products to the retailers. 726 01:14:40,080 --> 01:14:59,080 So that's what it's all about is generating the certificate. So we're almost there. And like I said, I would have loved to have this finished today, but Rome wasn't built in a day. 727 01:14:59,080 --> 01:15:06,080 So we're going to have to keep tinkering on this through this next week. 728 01:15:06,080 --> 01:15:11,080 It always takes longer than you think. 729 01:15:11,080 --> 01:15:19,080 Well, congratulations. I don't know who else you've shown this to, but it's really, really cool. 730 01:15:19,080 --> 01:15:34,080 And even from somebody who doesn't really know about the lab world, just looking at your navigation bar on the left hand side and where you just kind of walk through that, it's almost self documenting what it does. 731 01:15:34,080 --> 01:15:41,080 I mean, I don't know what an analyte is, but I can see the process. Right. 732 01:15:41,080 --> 01:15:47,080 So that's that's cool from a user's perspective. Of course, these are going to be lab people. They're going to know some of these things already. 733 01:15:47,080 --> 01:15:52,080 But it's just neat that it's kind of all the self documenting the process. 734 01:15:52,080 --> 01:16:01,080 Exactly. Because I mean, that's the whole philosophy here at CanLytics is we think that cannabis testing should be simple and easy. 735 01:16:01,080 --> 01:16:08,080 It just there's no need for it to be complicated. It's just you're just doing analyses. 736 01:16:08,080 --> 01:16:12,080 You know, you're going to have to take some measurements and record these. 737 01:16:12,080 --> 01:16:16,080 But, you know, it doesn't have to be complicated. 738 01:16:16,080 --> 01:16:24,080 So, yeah, so, you know, so we put in a lot of thought, thought about, OK, what are all the data models you need? 739 01:16:24,080 --> 01:16:31,080 What are the key data points? And we've put together something that we hope is simple for people to use. 740 01:16:31,080 --> 01:16:41,080 But now we need to get people using it or in this laboratory more so that way we can just keep keep improving. 741 01:16:41,080 --> 01:16:53,080 So, so it's come a long way. But hopefully we're providing value to laboratories. 742 01:16:53,080 --> 01:17:00,080 So. But. 743 01:17:00,080 --> 01:17:05,080 But let's go ahead and start wrapping up here, because I see that we've gone quite a bit over. 744 01:17:05,080 --> 01:17:11,080 But thank you for your enthusiasm, because it's exciting. 745 01:17:11,080 --> 01:17:21,080 It's been a long time coming. But I think we're finally close to a solution that laboratories will get an enormous amount of value out. 746 01:17:21,080 --> 01:17:25,080 Yes, it's cool to see something. 747 01:17:25,080 --> 01:17:36,080 You've talked about it briefly before. I know Charles has been working on you with it, but it's nice to see the idea take form and something that you can share. 748 01:17:36,080 --> 01:17:38,080 And I know you guys must be pretty proud of that. 749 01:17:38,080 --> 01:17:42,080 I have an appreciation for the amount of work that it took to pull that together. 750 01:17:42,080 --> 01:17:46,080 So good job on that. Exactly. 751 01:17:46,080 --> 01:17:50,080 And it's just one of those things we just keep keep working on it piece by piece. 752 01:17:50,080 --> 01:17:55,080 And so, you know, we just have a clear vision and we just always know what the next step is. 753 01:17:55,080 --> 01:18:04,080 And so, for example, this past week, it was parsing that data and Charles played an instrumental role. 754 01:18:04,080 --> 01:18:10,080 Pun intended. And so. 755 01:18:10,080 --> 01:18:20,080 It's coming together. So and we're just going to keep at it and keep trying to help people. 756 01:18:20,080 --> 01:18:24,080 But I'm going to have to have to drop off here in a second. 757 01:18:24,080 --> 01:18:35,080 But I started to cut you off there, Keegan, but I will reach out to you probably Thursday ish evening just to firm up things for Friday morning. 758 01:18:35,080 --> 01:18:41,080 Yes. And I have to go back. Go ahead. Oh, no. 759 01:18:41,080 --> 01:18:46,080 I bought the tickets to Canna con for me and my brother-in-law, who will be coming. 760 01:18:46,080 --> 01:18:52,080 You'll meet him as well. And I think I only bought it. 761 01:18:52,080 --> 01:18:55,080 Is there like there's there's like speakers, right? 762 01:18:55,080 --> 01:18:59,080 There's the convention floor and then there's like these break off speaking events as well. 763 01:18:59,080 --> 01:19:07,080 Yes. And so if you can, I would recommend getting the like they're used to call them seminars. 764 01:19:07,080 --> 01:19:12,080 They're they're a bit extra. So they are the more expensive ticket. 765 01:19:12,080 --> 01:19:16,080 But I would recommend that at least for Friday. 766 01:19:16,080 --> 01:19:22,080 So typically like the seminars first thing on Friday are the best. 767 01:19:22,080 --> 01:19:31,080 So OK, I do like I'll be out of state till probably about one thirty. 768 01:19:31,080 --> 01:19:36,080 And then I'll have to leave at that point. But we're going to get there as early as we possibly can. 769 01:19:36,080 --> 01:19:41,080 Oh, yes. And like I said, it's not it's not critical. 770 01:19:41,080 --> 01:19:48,080 And in fact, I think they may record them and I'll have I'll have notes and may record if they let me. 771 01:19:48,080 --> 01:19:58,080 So it's not critical. But I I just like to people usually present their latest research or ideas. 772 01:19:58,080 --> 01:20:02,080 So I just I think it's a good way to keep your ear to the ground. 773 01:20:02,080 --> 01:20:06,080 Yeah, absolutely. Cool. OK, well, I do I do have to drop. 774 01:20:06,080 --> 01:20:09,080 So it's a good talk to everybody. We stayed way late today. 775 01:20:09,080 --> 01:20:13,080 So thank everybody for coming. And I hope you learned a bit. 776 01:20:13,080 --> 01:20:20,080 And next week we all touch base and maybe get back into the data since that's been neglected a little bit. 777 01:20:20,080 --> 01:20:26,080 Right. Well, when you're in Detroit, now get some pizza, really good pizza, the best pizza. 778 01:20:26,080 --> 01:20:33,080 Well, if Keegan can break away at lunchtime, my brother and I, we're going to take him somewhere good to eat. 779 01:20:33,080 --> 01:20:39,080 So maybe I'll end up being a Chicago, I mean, Chicago style slap in the face, Detroit style pizza. 780 01:20:39,080 --> 01:20:47,080 Yeah, I mean, West Coast pizza is horrible. You really haven't had pizza until you've had pizza in Detroit. 781 01:20:47,080 --> 01:20:54,080 Well, I think it'll be worth a worth having fun with some pizza. 782 01:20:54,080 --> 01:20:57,080 So all right. But well, it was awesome. 783 01:20:57,080 --> 01:21:01,080 And then until next week, stay productive, everybody. 784 01:21:01,080 --> 01:21:17,080 OK, good talk to everybody. Take care. Thank you. Bye. Bye.