MetriSight Ep.52 - Generative AI the UJET Way

December 18, 2023 00:36:21
MetriSight Ep.52 - Generative AI the UJET Way
Metrigy MetriSight
MetriSight Ep.52 - Generative AI the UJET Way

Dec 18 2023 | 00:36:21

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UJET CEO Anand Janefalkar shares his unique perspective on generative AI, AI innovation, and the AI-empowered future of CX.
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Episode Transcript

[00:00:22] Speaker A: Well, hello, everybody, and welcome to our next episode of Metrosight. Today I am so happy to be joined by Anand Jana Falkar, who is the CEO and founder of Ujet. So welcome, Anand. I want to just get a brief introduction. When you think about AI these days, and we hear about it every day, we read about it every day. It's embedded into customer engagement in such a huge way. It's such a huge area of discussion, it's a big area of spending, it's a big area of focus among CX leaders, especially going into 2024. We've had a big year in 2023, so I'll just say, whenever I have an opportunity to share the wisdom of a successful executive in this space, I'm always very welcome to do that. So I'm really excited to have you here. Anand, I know you have a lot of great insight for us, so let's just jump right in and I'm going to ask you a few questions around the space. But before I get started, always like to ask any company founder, what led you to start the company? Was there like a single trigger or an aha moment where you said, look, I've got to do this, I've got to start a company in this space. Maybe give us just a little bit of background so everyone knows how you got here. [00:01:40] Speaker B: Yeah, it's never that straightforward, and thanks for the kind words, but this was truly an area which was near and dear to my heart. As you can imagine, with the last name like Johnny Falkar, it is not easy to pronounce that on a customer support call, let alone get a follow up email which would have the right spelling, no matter how many times you enunciate or spell the last name. So kind of done that to myself, but kind of looking beyond that, right? I mean, I'm not the only one that has a difficult last name and spelling mistakes happen pretty often, even with very easy names. It's more about how humans communicate. So my background is actually from the mobile industry. So Nokia Research Center, Motorola Mobility, where I spent a lot of my time and attribute a lot of my professional upbringing of scale, security, and just understanding how the transition of the macro behavior moved from just push button phones to smartphones and then furthermore to jawbone and others. But taking all of that into context, if you can appreciate the fact that I was in the core of development when the movement happened from the behavioral change where people started communicating visually and contextually through smartphones, smart devices, and thinking about that for a second, how two humans communicate today which is pretty much through that. The only time when they don't do that is when they talk to businesses, which is super ironic because businesses are trying to communicate in a very personable manner, create this relationship and this repeat business, but you're basically making them communicate with technology or legacy stacks that were pretty much derived from a stack in the late 1990s or the early two thousand s at best. So I felt that having that background where I can truly, deeply understand from a very core perspective of user experience, scale, security, privacy and telecommunications, and how that can move towards a smart way of communicating where not just the two end devices that are smart, but everything in between, the routing engine and everything, the WebRTC, the Opus codec, can also align with the capabilities of these two end devices. So felt that I had a pretty interesting background that could enable this and spoke with a couple of people that had sector expertise. And the feedback that I got is, if you don't do this, give it to us, we'll do it. I'm like, all right, maybe I should do it then. So that was eight years ago and here we are today. [00:04:21] Speaker A: All right, well, thank you. Thanks for that context. All right, so speaking of context, I guess when we looked at our recent survey, or it was a study, it was interviews and survey that we did a global CX survey, about 27% of the companies said that they were already using generative AI for CX purposes, which honestly kind of surprised me. I wasn't expecting it to be that high. That was in June of this past year. And you figure that was pretty quickly. Now, in interviews that I did with people, we certainly saw that they would say things like, well, we're just kind of kicking the tires, we're giving it a try. So it wasn't like real extensive use of AI, of generative AI, but they're using it. Another 47% said they'd be using it by the end of the year. So even accounting for those who maybe anticipated adopting it and didn't actually do it, let's say by the end of the year, that's a strong desire for using generative AI specifically for CX. I wanted to see if that aligns with what you're seeing from your customers or potential customers. Are you seeing this crazy interest in generative AI and what kind of use cases are you seeing? Maybe talk a little bit about that. [00:05:33] Speaker B: I am on the other side of the spectrum that it's only 27% and 47%. So I think people should be dabbling with it to understand how the magnitude of change that it can bring and putting a little bit of a business thinking hat on it. If you really think about it. We've been waiting in this sector for people to adopt cloud, people to adopt just the modern, even case management systems and all that. And there have been some macro trends like the movement towards cloud and movement towards WebRTC and multichannel and omnichannel. But really, I think this catalyst of AI engine AI is going to be the strongest push of them all. So maybe by the next time we chat or exchange emails, that percentage would have moved closer to 100%. On both aspects. It 100% aligns with what we are seeing. In addition to designing a platform from the ground up, where routing engine itself understands that we are in a WebRTC environment with fallbacks for different types of transport layers, what we have in nature is that code base wise, it perfectly aligns with what AI or gen AI is. So it is not a hand in or a handoff. It basically AI and intelligent automation begin from when the query starts. One thing that we feel that Genai will bring to light is that questions, queries and search will all become synonymous. Let's say your garage door is not working. Where do you go to find that out? I mean, you might go to your favorite search engine, or you might go to YouTube and think about how much pattern recognition those platforms already have on how people search for solutions. So you're no longer just picking up the phone and looking at the back of your garage door opener after getting a ladder and then calling that, right. Those days are gone. So what Jenny has already figured out is that there's a lot of disinformation out there. We understand how people look for solutions, and we should incorporate that in an extremely responsible and ring fence manner that will suit enterprises to enable or facilitate faster answers and also actually enable agents. I've never met an agent who wants to be a very tier one concierge agent and read manuals or scripts that are already available. Right. We really need to empathize with agents first, that perhaps they're looking at and learning a technology that was designed 20 years ago and trying to provide service from 2023. But the other aspect of that is that they're kind of held back. Their senses are curtailed when you don't give them a visual, contextual and omnichannel manner. Fluid omnichannel importantly to do that. So where I'm going with this is AI and Gen AI together. This is not going to be successful without understanding the user behavior and usage patterns. And if people are not starting to think about it from all aspects on end to end. Where does a query start? What is the metadata that can be beneficial to understand whether this is a firmware fault, a fault that something is impeding the garage door because you just moved a big chair over there, something that is, where is the power setting of that garage door or multitude of things? You're not going to be able to design or choreograph what the next steps of that would be sequentially towards the solution. So it's very important to understand. [00:09:29] Speaker A: Yeah. And the benefits of it go so much deeper than I think a lot of companies are even imagining these days. It's not just the customer experience, it's the agent experience. It's keeping those attrition rates lower. You get cost savings from that. Because why? Well, because like you said, agents aren't bored just doing the same old thing. They're working on more complex issues and they have AI to kind of help them to do things like upsell and Crosssell. I mean, oh, you can just kind of go on and on with those types of benefits that are. [00:09:58] Speaker B: And continuing on that thread. Right. I mean, we spoke about how Gen AI can be helped for enabling faster things. I think that is going to be the most biggest unknown thing that people don't realize today is like how you can take that information, completely obfuscate and anonymize that for privacy and security, and then make it a facilitator to design these flows. The second part of that is, well, the connection points are also important. I should mention there that you have to be very understanding and appreciative of where this originated. If it originated from a website, if it originated from your smartphone app, originated from your tablet or your smart tv app, it's important to keep that experience consistent and not break that experience and then just show up a phone number to start communicating. [00:10:48] Speaker A: Right. [00:10:48] Speaker B: But if you go into what you just mentioned is like, yeah, how do we enable the agents? Right. First of all, provide them all the contextual information, any metadata that can be important. They are very digitally acquainted. You're not going to find agents that are just like, I just want to read a script. And it's very demoralizing when agents have to do that, entering this modern world where they're supporting a lot of these products and services that people love. Give them that context. And then it is everything from translation. Like we have live translation. We just recently did that with Japanese to English and English to Japanese. And that helps with coverage in terms of support over the number of hours, like a lot of times people would want support at seven to 08:00 p.m. And you just have a certain team that is online then and you enable that. So that's the first part of it. Second part of it is summarization and do a summarization which is very insightful. You highlight the keywords and then kind of give guidance towards that and bring in insights live right to them. Furthermore, taking that and also providing them that might be things from your knowledge base. You as a company and a team have already put in a lot of work on the knowledge base, but it is in a third or fourth different tab. And when the agents are on the front line or the wealth management professional or healthcare advisor, anyone, they don't have time to kind of firefight between those three tabs. So bringing that to the forefront and then follow up. Follow up is extremely important. You take that, carry that over to disposition, auto summarize, give them chance to edit that and then set up what would be a nice follow up, just understanding what this customer's personality was when you communicated with them. So utilize agents for what they said, which is experience, empathy and intuition, and then later on AI to it. [00:12:40] Speaker A: Excellent. No, I totally agree. So when you look at the company, I mean, obviously you have touted many times over the past several years now. Actually, your AI centric contact center is a differentiator. And I would also add to that, I mean, certainly Ujet's partnership with Google is a differentiator. So if you had to answer the question of what makes your platform any different from any other ccas provider that also offers AI functionality, how would you respond to that? I mean, everyone nowadays says, oh, we offer AI, that's our differentiator. What would you say is your true differentiator then? [00:13:19] Speaker B: That it is stylistically and code base five is exactly the same. So we never talk about hand ins and handoffs. A lot of times I'll hear providers or partners in the space that say like, hey, yeah, we've heard about this, but this is the hand in that happens, and then you're kind of breaking the experience. So there's a few things that we are very kind of judicious about. The first thing is really assigning importance to the origin point or the connection point. We believe that the connection points are the new channels, right? Voice chat, video screen sharing, texting, these were all, if you really look at it, were based on device limitations of the past. And those stacks were developed. Understanding those device limitations and the ready busy lines and all of that stuff. Now the agents are communicating with the computer, customers are 99% of the time communicating with the smart device. Those things don't apply. They already have the capability of fluid omnichannel. So why is the in between pipe not smart enough? So that's what we enable, right? So we understand whether this is a query that's originating in a certain app with our iOS Android SDKs. It's originating from a website, or even if someone's just calling the number at the back of their credit card or a box, you can layer on sms and rich messaging services and Apple business messaging, so on and so forth, depending on which is the appropriate way of doing that. Then we already spoke about providing metadata and context and visual and contextual things as default, and then allowing agents these smart actions where they can press a button and request verification the same way that they verify with Touch ID, Face ID as you log into the app or unlock your device, kind of layer that on. Rather than asking what's your favorite band in high school, this is what your challenge question was when you filled out this, that account might be five years old and your favorite brand in high school might be different or whatever be the case, right? I mean, the challenge question is going to go away immediately. You've saved about 30 to 45 seconds and the satisfaction index has gone. Then you kind of take that further and understand that AI actually works. That speech is catching up, but AI actually works better on images and text. So having a platform which not just uses the connection points, but the fluid omnichannel is in an effort to enable what AI can do best in that moment of time is also what differentiates us. So you can send those photos, videos, screenshots, and that immediately not only becomes a part of the summary of your conversation, but it also enables, if you're using the AI products like Doc AI or vertex AI, or a few of the others that Google has or others, you can immediately take the benefit of that. The rudimentary ones like OCR and this and that can always be taken into context and templatized, but using very specialized task virtual agents, extremely specialized, to take your booking number and offer changes, taking your payment and offer a payment plan, taking just your appointment and giving you changes for that. Those are the ones that also truly differentiate us. And we obviously spoke about the follow up and how we feel that that is truly a part of CX and not an afterthought. That oh yeah, the call is done. Let them call back if they need follow up support just truly have use dispositions in the modern way and scoring and all of that quality management to ensure that hey, this really is a model call, or hey, this really needed barging in and let's use all of those patterns to make future things more successful. We covered a lot of that over there, but hopefully I was coherent enough to convey that. [00:17:09] Speaker A: So how does generative then play into all this? And it's kind of interesting because a lot of people talk about generative as if it's an application, but it's really an underlying technology that enables applications, I would say. How do you see that all coming into play with what you just talked about with your differentiator? [00:17:26] Speaker B: Yeah, I'll give a quick couple of examples. Because a lot of times, as you're saying, people are just experimenting with this and if you ask them the question of okay, how much ROi have you gotten? Or you're confident of getting that, 27% might be lesser than 1%. So what we feel is that customer experience is actually one of the most sure footed landing spots for Gen AI in terms of ROI. And the reason for that is because you can truly measure it. You may not be able to measure, I mean, we do believe that copilot and others will eventually be very successful, but you may not be able to measure it. And they might be very specific to different companies, to different teams. Customer experience, you pretty much know which factors are kind of draining that good experience. So using gen AI for immediate conversion of your knowledge base to kind of articles that will pop up big one like really high, reduces Aht, reduces agent flustering, reduces them to put the customer on hold and look for stuff. Instant ROI, gain instant measurability in handle times as well as CSAT. The second thing is to enable some of these flows to just choreograph them right. If people are starting their journeys on search sites as well as YouTube and others, taking that information and understanding those to enable that, rather than taking tons and tons of recordings going through, let's say a specific queue or a specific recording bank of recordings and then trying to make sense of it, highlighting the keywords, this and that, you could do that. And there are platforms that offer it, especially with Google Dialog flow. If you think about it, you can have over 3500 intents and then you could have over 2500 phrases per intent to really accurately nail what you're trying to say. And you could do that. But using Gen AI rather than doing that in three to four weeks, you could do that in three to four days. And I think that is where you can see the enablement happening faster. So I think these are just two examples. I already spoke about task virtual agent, which we call task VA, extremely specific specialized tasks that can help that. And you can measure that in handle time, customer satisfaction, as well as how you can really start moving some of what your traditional IVR settings that historically have had terrible metrics over to this and compare, do a b testing. And that's why I feel that customer experience would be kind of the most sure way to go about it. [00:20:25] Speaker A: So how do customers then cut through the noise on generative AI? I've talked to several it and CX leaders of late in some research that I'm doing. They're like, you know, we're at the point now where there's so many different things we can do with AI that we're just going to say, hey, here's our problem, or here's our opportunity, and we're going to let our technology partner figure out what we should be doing because we don't really know. And I guess that's my question. If you're a CX leader or somebody who's buying your product, what do they need to understand about it in order to realize its full potential? What do they need to know and how much do they just tell you, hey, here's what we're trying to do, and you solve it for us. [00:21:03] Speaker B: Yeah, I mean, first of all, I think that CX leaders should be very happy right now because how many times have you gone and asked for budget and say that we want to do this, we want to do that, and your finance team or anyone, and again, not putting the blame on finance, but like anyone, like, hey, these other things are more important in the company right now. We probably don't have the budget or we don't want to make a change before a big launch or there are other things to do. You should be happy as a CX professional that everyone from your CEO, your CFO, your CEO, CISO, chief digital office, everyone is aligned that, look, we don't want to miss the boat on this. It's not like before. It's like, hey, we don't see anyone else changing it. We don't need to be the trailblazers on it. It's surprising how many times I've heard that, Robin, where they're like, we're going to be good, our metrics are not bad and we're going to be fine. I'm just not getting the support. So I think that is one aspect to know because a lot of times business decisions drive technology adoptions and not the other way around for sure, which is the strange truth of the world. The second thing is what that also makes the conversation easier is that you're not going to be hearing from your finance leaders like, look, you had asked to make this change two, three years ago, and we're in a five year contract and let's just amortize it and then figure it out after five years. That, again is one of the big, big blockers. So that has gone away. So as a CX leader today, even though you have all of this noise, you have all of this tailwind where people are super supportive and really understand that, hey, we need to do this now to be successful. So taking that tailwind, what would be is to just really understand, right? I mean, do you want to do piecemeal things and are you really looking for a specific thing? Piecemeal is not intended to be a bad word here. Are you looking at modular things where you want to make that the highlight of, okay, this is what it can enable. We adopted this, I've done it with this amount of budget, and this is how it made a change in the metrics and the business outcome. I want to do this on a lot of other things. So this is the power of AI. So you kind of have to pick where you could just illustrate how this can be successful. Second thing is, if you do have the support for going for an end to end platform, I think just grab it and say like, look, I'm going to take this shot. I found this, again, modular or logistically separable team that I can do it in either this country or either in this group or either in the technical support as opposed to just the tier one support. And I'm going to show you these factors and then we can really adopt that end to end platform everywhere. Those probably would be the ways. I know that sometimes the focus can be just on agent training and that will yield good stuff. But I would say that taking that and truly thinking of either the same, expanding that same, or to look at a different end to end platform because certain things weren't met, should be kind of the approach CX leaders should be taking. Because the time is now and what you'll see is a lot of the decisions that you'll make as a CX leader in the next year or so will truly start defining whether customers associate you with a great CX brand or just, hey, we love the product, but we love the product despite the CX. [00:24:39] Speaker A: So what would you consider are the barriers to entry right now? I mean, we see, certainly consumers are concerned about things like the truth, trusting the data and all that kind of stuff. But to a lesser extent, we'll see some companies concerned about cost. Not as great, though, as other new technologies. I think there's a general understanding that you're going to see an ROI when you use AI. I really do. But there's definitely some concern about malicious use of the technology and the trusting of the data and the data privacy and all that. What are you seeing as far as barriers to entry right now? [00:25:16] Speaker B: Yeah, very good question. So it's very different than consumer AI. So as CX leader, you really have to be very prudent about which platforms you're picking. Having a lot of the compliances are very table sticks. You have to have a lot of those compliances, but you also have to understand that the Aips that you're adopting needs to be very much catered towards. What you're trying to solve does not have effects of hallucination, can be ring fenced very well. You're having right ways in your AI studio to either highlight certain things as things that you just absolutely cannot cross the boundary on. And these tools are available. These tools are available. It is more about like, are you picking the right company and partner that will give you all of these knobs to ensure that you, your customers, and your information is protected? So if you're in this experimentation stage, even if it's experimentation, please do not use something that is not enterprise grade. There's a lot of stuff out there. There's a lot of noise. Please get your infosec team involved. And it could be a very slippery slope if you're just trying something and then kind of gone and provided the keys to the kingdom, find out that certain things are now a part of their database or their indexing or their content management. So, yeah, having extremely ring fence instances, write controls, and just being responsible as a part of the architecture is also important. And again, like shameless plug in here. But we see all of that with our partners that we use. [00:27:11] Speaker A: Well, you raise a good point about bringing an infosec in early, and a lot of companies don't do that because they don't want to hear the no's. They just want to get in there and start using it, and they could be really putting themselves at risk, for sure. I know one of the it leaders I just spoke to was saying that some of his concerns about using generative AI are, is my data going to be used in some broader database. I don't want that. I want to have my own. And that's a legitimate concern. And with some companies, maybe that would happen. So it's something to definitely be looking at or some approaches, some architectures, companies. [00:27:47] Speaker B: That have very different offerings based on that. So even companies, if you want to experiment something, don't go for the easiest one where you can just plug in a credit card and then try thickness out, and then you find out that it had that in their fine print, that this fine print to improve their intent management or their data part of their database. I know that in the past you can do a quick and dirty trial and POC and then bring your infosec. This is not the time to do that. [00:28:15] Speaker A: Yeah, for sure. All right, so a few more questions and we'll wrap. You know, obviously, as we've been talking about, changed the pace of innovation, definitely in CX and other places of the company, too. But with how fast everything is moving, how do you guys keep up and how do you help your customers keep up? Because I know as I talk to companies, especially those who are on prem still, they're like, we can't keep up anymore. We simply have to move to the cloud now because we can't keep up with the pace of innovation and the pace of technology change and also keep the lights on and also be innovative ourselves. So it's like they kind of need to go to the cloud to not have to worry about that anymore. So how are you guys keeping up and how do you make sure your. [00:28:59] Speaker B: Customers are, yeah, we don't know what on Prem is. So we don't know what that is. We don't know if we were even incentivized to build something on Prem, we wouldn't know how to do that. So we also don't know what a traditional SBC is, all that. So I think that is why when I said that stylistically and code base wise, we are in band with AI and we really don't have to do any hand in or handoff stuff that the other providers have to do because their code mean you're talking about people that are on Prem, even the ones that have moved to cloud sometimes have just done lift and shift of the same code base that is made compatible to the cloud or one version of the cloud and it again, into a very small just domain where they're like, oh, no, we have to rearchitect this plugin, or we don't have microservices, or we don't know whether we need to open. So for us, we've always done like two, three years ago we were doing about 150 to 200 releases per year. Now it's over 500. We operate in true sprints, we have three week sprints, then staging, then QA, and then release. [00:30:16] Speaker A: How do you keep your customers up with all that? You're coming out with all this innovation. [00:30:21] Speaker B: It should be completely transparent. Most of our times our customers don't know that there is a release happening. Okay. The only time that we need to have a customer involvement is if we need any action from them. Obviously we notify them that this release is happening. These are the release notes, these are the things that you can expect to change, especially if there's a UI change, if there's a new smart action being launched, if there's a new payment option being launched. All of this is in the release notes. But we don't have maintenance windows. These are all live. This is how modern technology is. There are no spring releases, fall releases or anything like that. If you do that, you're behind and then you kind of shoot yourselves in the foot when you're trying to sequence everything to hit that. What continuous development truly allows you to do is not only have tons and tons of releases, and again, having your code base very much similar in inband to AI is a huge piece here. And we actually do feel we have an unfair advantage on that aspect. And having developed everything from scratch, we know exactly which pieces we need to tweak in case some of them are going to benefit from plugging in additional components of AI. But yeah, technology. One of the things that I learned in my professional upbringing was a jawbone, which was technology should disappear in the background, and I've always used that as a guiding principle. So the technology should truly disappear in the background because no one really cares. You need to have your stuff online all the time, uptime all the time, no one's going to give you benefit of the doubt. Oh, it was a maintenance window and that's why we had that you can advertise your 100% uptime as much as possible. And when you put it in the fine print, like, oh, there's about 4 hours of maintenance windows every month, that's not 100% uptime. That is what people need to kind of read through the fine print and just pick the right horse. I mean, really, this is the race to the future, and that race is going to culminate very quickly in the next twelve to 15 months. [00:32:36] Speaker A: So I want to go a little bit longer than that. Last question here. If we want to look down five years. I don't know if you want to go as far as 1010 years is very hard to predict. Five years is even hard to predict these days. But let's say that's about as accurate as we can get, right? How does CX look in the future, just in general? I mean, if you want to comment on where's uche in that time frame, are you still partnering with Google? Are you part of Google? I don't know what you want to say about that, but maybe we can talk a little bit about what does CX look like in the future from your perspective as you've been looking at this and studying it and living and breathing it for so many years now. [00:33:14] Speaker B: What I truly feel is that a few years from now, let's say, I'm hoping in the next 15 months, or at least two to three years, gen Ais, the lines of Gen AI, CX, queries, looking things up, they're completely blurred. There's no search. You'll stop calling it search, really. We'll start calling it like solutioning or whatever. So essentially, one of the things I truly feel is going to happen is that Genai and CX and everything just become very synonymous. People will be just. Hopefully they'll say, let's just use it. But again, that's too ambitious. I think there will be a verb that would be coined that will replace all of this vernacular. The second thing would be, I think the virtual agents and the human agents will be completely undistinguishable in or on. You can pick that, but there will be no difference between them. Like, you wouldn't be able to tell. [00:34:20] Speaker A: That would be really interesting. Yeah. [00:34:22] Speaker B: The specialized services and the tasks, all of them will have parity. And my prediction is that humans will be used what we are best for, which is experience, empathy and intuition. And no matter how well trained the AI would be, I think that would be something that will be hard to reach to those levels. So a lot of the mechanical stuff, a lot of specialized tasks would just become parity. And actually the permissioning people will start feeling more comfortable in three years and there'll be good control around it. And again, I'm going to reiterate my first one where I think that CX would be the number one landing spot for ROI on Gen AI copilot. I'm close, but again, maybe my views are a little biased, but I believe that will happen. [00:35:16] Speaker A: Agree with you there on the ROI. Yeah, we do a lot of research on that and we're definitely seeing some very compelling numbers just on all types of AI. But early numbers on gen are pretty high, too. [00:35:28] Speaker B: Yes. So hopefully that answered your question. [00:35:33] Speaker A: Absolutely does. I know you have an engineering background, and it comes through with how you're running the company, how you have the products developed, how you can speak from such an engineering standpoint, which I think is also very important because it says a lot about the products and the technologies that you have at the company. With that, Anand, I really thank you so much for taking out the time today and for talking with us. I know your insights will be very valued by those who are watching and listening. So, with that, I want to thank everybody for attending today and have a great day. [00:36:09] Speaker B: Thank you so much. Thanks for the audience. Take care.

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