Thanks very much, John. empowering our employees is indeed at the heart of our approach and Claims. As emphasized by John and as Tricia states often, along with our culture, our people are our greatest competitive advantage. We focus on enhancing, not replacing their roles through technology. Similarly, we want to enable our customers with user-friendly digital options so they can interact with us when, where and how they choose. We'll share some of those concepts and how our Progressive DNA around digital experiences, segmentation and data translate to claims. And we'll dive into a specific use case around photo estimating, highlighting the Progressive made and our commitment to achieve the optimal balance of accuracy and efficiency. John showed how we stack up from a total loss LAE perspective. Here is an alternative view of efficiency. An indexed comparison of auto policy growth and the number of staff aligned to auto claims handling. We continue to grow and hire. But our scale and strategic investments have allowed us to grow staff at a slower rate than policies, shown in the gray shaded area indicating the growing gap. And while we acknowledge that frequency declines in this period are a tailwind, we're very pleased with the efficiency improvements we've been able to achieve. Market conditions may have us make specific calendar year staffing decisions to ensure we're positioned for the future. But over the long term, we believe we can continue to become more efficient and selective with the human capital required to service our customers while maintaining the competitive advantage we believe our culture provides. In prior calls, you've heard us talk about our large and growing media spend and how critical it is for us to manage it efficiently. Efficiency is also top of mind with our claims technology spend. The vertical axis shows the indexed gap between auto policies claims staff, the same gray area I highlighted in the previous slide. In general, we want to be higher on the vertical axis, indicating more efficiency in our staffing. The horizontal axis is our claims IT spend, a subcomponent of LAE. In this case, both indexed to 2007. You'll note that while we made some progress growing that policy of the staff gap from 2008 to 2016, when our technology spend was flat to down slightly, it grew notably faster with some strategic investment decisions after that point. The policy to staff gap grew by 50% from the index values of 1 to nearly 1.5 in the nine-year period from 2007 to 2016. But we've been able to grow it by 130% from 1.5 to 3.4 in the seven years since 2017. Our investments in technology to improve the claims process have shown through and clear efficiency gain. And not unlike our media spend, we're willing to invest here as long as we can do so efficiently. You've heard us talk before about our digital capabilities and acquisition and policy servicing, and the obvious value we believe advanced product segmentation provides for our company results. That Progressive DNA and experience applies just as well to Claims. In fact, when it comes to first notice of loss or the moment of truth when a customer contacts us to report a claim, we think about it similarly to our acquisition funnels. That said, it is a bit more nuanced as it's a funnel that then feeds our claims handling funnel from the point of assignment to resolution. And that path can be nonlinear depending on the circumstances of the claim or what facts might emerge a initial assignment. Nonetheless, there are similar trade-offs at first notice of loss as in our acquisition funnels. Asking a lot of questions might aid initial triage and segmentation, but it might also frustrate customers, drive digital abandonment and increase call times and expense. What's very important, we gather as much accurate information as we can, as efficiently as we can, but limited to only what we truly need. As John alluded to previously, it's straightforward to one, it's much more complicated to do both at the same time. To be clear, segmentation in this context is about the facts of the claim and not customer segments. We are not talking about Robinsons or Sams here, but not every claim or accident is the same, knowing the number of vehicles and parties involved, where their vehicles are likely total losses and whether there are injuries and how severe are just a few of the many important facts that allow us to segment income and claims and make assignment decisions to get the claim in the right hands faster. Getting it wrong can negatively impact cycle time, which can increase expenses such as rental and storage fees, if we must spend time retriage and claims and/or vehicles. It also resulted in a suboptimal customer experience and even hurt retention. We believe there remains additional upside on the digital side of our first notice of loss funnel and have been investing accordingly. Industry data from Bain suggests that digital adoption in the auto claim submission process averages about 25%. While we are meaningfully above that industry data point, we think there remains additional opportunity given the long-term trends we've seen in acquisition and policy servicing. Many customers prefer interacting digitally, and we'll continue investing to ensure we have those options available. That said, an accident is certainly a very different kind of life event than purchasing or modifying a policy, and we will always be available for human-to-human contact with customers that prefer or need that channel. One of the reasons we continue to invest in digital experiences for our customers and our employees is it further accelerates a virtuous data cycle we've referenced a variety of context over the years. As we work to resolve claims, our customers and employees generate a variety of interactions with each other and other involved parties. Historically, across the industry, these have been predominantly analog in their nature with lots of phone calls, manual claim and relatively unstructured data. At Progressive, we've invested significantly in monitoring digital experiences. Those enable us to turn many of those unstructured processes and associated data into more structured information. Additionally, the very nature of digital interactions creates even more data that's not exclusive to a fine set of events or outcomes. They also provide experiential insights to how our customers and employees are using those systems. This data helps us identify areas for additional claims process and technology investment connecting back to John's description of how claims contributes to competitive pricing. Our operational efficiency improved helped lower LAE, which enables lower rates, powering additional business growth and scale, generating even more data and enabling additional targeted investments. One example of these cycles in action has been photo estimating. Photo estimating is a digital experience whose adoption has grown significantly. It can be a convenient one for customers and claimants when they're uncertain as to the repair cost relative to any deductible or they're not sure when or where they'll get the vehicle repaired. From the comfort of their own home or workplace, they can submit photos via guided digital experience and get a repair estimate back quickly, often within a day, if not faster. If you recall the conceptual curves John discussed, photo estimating is a good example of a shift in the curve. And as we'll walk through even when we do a -- a shift, we continue to optimize so we can operate at the bottom of the curve. The chart on the left shows annual photo estimates of 2016, over which time we've enjoyed an 82% growth rate. While we're not showing actual numbers on the prime axis, know that in total, this represents millions of photo-based estimates written over the last eight years. And with it, of course, tens of millions of customer-submitted photos. Secondary vertical axis is an indexed view of our Estimate Quality Index plotted on the orange line. These objective assessments of the work that John mentioned earlier are a core benchmark for quality and accuracy, and an invaluable source of ground truth. Despite the growing volume, there's been no material degradation in accuracy. So while we're pleased with the growth of photo estimating as a viable and efficient vehicle inspection channel, we are consistently measuring accuracy alongside the growth in volume to ensure the experience we're offering and outcomes we're generating aren't compromising the claims guiding principles John described earlier. The chart on the right indexes daily estimates completed compared to traditional in-person estimate completion. In total, we've seen we can complete 2.5x the number of estimates each day, making it our most efficient vehicle inspection channel. Once we achieve viable scale, refined our processes, organizational and data structures, we began to apply additional technology so that we could potentially drive further down the cost curve. In this case, we're using machine vision models alongside a variety of other technologies to help us automate parts of the photo estimating process. These photos illustrate the process without disclosing actual customer images. At a high level, we're using deep learning models or neural networks, alongside more traditional machine learning peaks to look at customer-submitted photos like the example on the left, identify the correct parts through segmentation masks and then identify the location and type of damage by part. Generating an accurate estimate requires several discrete predictions. For example, we need to know not just the location of the damage by specific part, but also the type of damage. Is it a dent, scratch, et cetera. From there, we need to decide whether we should repair or replace the part. And in the case of repair, how many labor hours do we believe it will take. These models use transformer architectures you may have heard about, quite literally the second tea and ChatGPT, but they are trained very differently than some of the models making headlines more recently. These are supervised models that we train on our photos and our own curated data to ensure accuracy consistent with progressive estimating standards. Such models all benefit from very large, clean and diverse data sets. We're fortunate to have all 3 in-house without feeling a need to look externally. And again, that ground truth data from objective assessments and our own people's disciplined quality assurance processes and willingness to do the hard work to get it right give us the benefit of a very clean, trustworthy and we believe, differentiating historical data set. It's my experience that it's really the combination of technology and Progressive's talented people that make the brands. On this solution, our data science team works daily with our physical damage process team that combined brings over 400 years of experience. Most of them have worked at every level of our Claims organization. They have estimates themselves, worked in and alongside shops and have a deep understanding of all facets of vehicle repair. It's that difficult to replicate expertise alongside more advanced technology that really helps us deliver competitive advantage. As one quick example of why the details and subject matter expertise matters, let's consider a bumper. It's not enough to be able to identify a bumper and the existence of damage on it, you need to be able to distinguish between such things as upper and lower bumper covers depending on the year, make and model. And in this case, the presence of bumper sensors. At left is a rendering of an example with some of the things our solution does behind the scenes in relative real time. In this case, cropping the bumper from the original image and then making a prediction as to whether it sees the smaller outlines that indicate the presence of sensors in the bumper of a particular car. These can be very hard for even a human eye to detect from a photo. But we must get such things right to ensure we get the accurate part of the estimate in the first place, and account for the incremental labor required as shown in the estimate ample on the right. Of course, while machine vision is a very interesting technology, we are not in the business of winning data science competitions. We are in the business of insurance, so it's important we prove the value. The orange line here represents the index value of traditional in-person inspections completed per day, the same value as before. You can see the light blue line as we introduced and then scaled photo estimating get into that original 2.5x increase in productivity that I mentioned previously. We began this journey in 2019, well before the current hype cycle, as an R&D effort with a small group of our photo estimating representatives. We continue to iterate and refine our models and approach until such time that we were convinced one of the efficiency gains and two, that we were able to equal or better accuracy results. Now the darker blue line shows our estimates per day when enhanced by machine vision. In 2022, we began expanding these capabilities throughout our photo estimating organization and fully rolled them out in 2023, having achieved an incremental 2x increase in productivity. To put that in some context, without these advances, we would have needed a 200 additional staff in 2024. While it has been a multiyear journey, the combination of data science and other automation techniques have now doubled the productivity of what, as I mentioned, was already our most efficient means of vehicle inspection and estimating. 100% of our photo estimates are now initially drafted by these solutions and then validated and/or corrected by our estimating professionals. To be clear, this is not full automation or straight through processing that you may see referenced across the industry. We have people involved. We do think full automation is possible at some point, but we won't do that until we're sure we can do it consistent with our guiding principles and, of course, applicable regulations. For now, we're quite happy to continue empowering our people to be even more and more productive. And we continue to see gains with nothing suggesting to us, we've even hit us feeling yet. I'll cover just two examples on how we're continuing to further refine our solution that we think will generate incremental gains in accuracy and efficiency beyond even what we've already achieved. Beyond our people's efficiency, we also work to make our model training more efficient. At our scale, we must keep up with a very diverse set of vehicles and an OEM market that is always moving. One of the ways we do that is with a semi-supervised training approach called pseudo-labeling. This is the practice of training a model on a small set of label data to label the rest of the data. Traditional machine vision use cases rely on a very large set of manually labeled photos, as I mentioned. But as we've scaled our solution, we sought to improve the speed at which we can deliver value across a broader and ever-evolving list of car parts. For context, and while it certainly varies by manufacturer, there are an average around 125 distinct external parts on most common vehicle types. Our process starts with our original segmentation model capable of identifying major parts such as doors and quarter panels. From this base model trained on subject matter experts labels, pseudo labels are generated on a much larger data set to create an initial base model that's even better at those major parts. This improved base model is then fine-tuned with even more detailed part labels from a diverse set of vehicles. This allows us to get more details about very specific parts in the vehicle, like portions of the grill and emblems that can all vary in shape, location or even existence depending on the specific vehicle type. This new part segmentation model is a general template model that works reasonably well on all vehicles. But to ensure the most accurate outcome, we customize the model further for specific vehicle makes and models. With only around 100 detailed label examples, we can apply these vehicle-specific pseudo labels to hundreds of thousands more examples of that vehicle using a bootstrap technique to randomly sample additional internal data. For computational efficiency, we then fine-tune the model with a technique called low-rank adaptation or LORA. LORA allows us to efficiently adapt large models to specific tasks enhancing precision without needing as much training data or training time. The point of all this is shown in the images on the right. While you might look at the top right image and suggests the damage is probably on the bumper or maybe the right front vendor, the accurate view is the damage extends across three parts: the bumper, front fender, and molding, the details of which we achieved by much more fine-grained part segmentation capability. Our approach ensures both broad applicability and precise customization across vehicle reducing the need for extensive label data sets. It has enabled us to achieve a 10x speed improvement in training while providing more accurate identification of all relevant external parts. It's important to recognize that such techniques are not possible without very trustworthy and accurate labels and a large data set upon which to extend them. Our subject matter experts and tens of millions of available photos give us both. This is obviously a very fast-moving space. We continue to keep our eyes on. This animation illustrates a newer approach called 3D Gaussian Splatting. It allows us to transform 2-dimensional images into dynamic three-dimensional models. Doing this well requires a lot of photos. But back to those trade-offs of accuracy, efficiency and customer experience, we can't reasonably ask customers to take hundreds of photos. But we do ask them to take a video in addition to around eight to 12 photos on average. A one-minute video at 30 frames per second of the car yields us around 1,800 frames, and it's that data we are increasingly leveraging as part of our solution. Some research scientists have suggested that what large language models are to text, Gaussian Splatting is to graphics. The latter is what's interesting to us. Being able to reconstruct the vehicle as if we were in person to understand the full extent of damage. 3D Gaussian Splatting builds on previous work in the field of reconstruction such as neuroradiance field, but it's much more computationally efficient and as a solution that meets our operational needs. Remember, we must be efficient and accurate. While we won't walk through the math here, there are 4 primary steps to this technique. First, we use the underlying data, again, thousands of frames to understand the position of the Cara and construct a three-dimensional coordinate grid to understand the relative position of every pixel in the frame. In parallel with understanding camera position, algorithmic techniques such as motion create a point cloud or mesh of the vehicle. This essentially gives us the center point or mean upon which to locate the gaussian splats. On those points, we gaussian functions onto each pixel to simulate the physical characteristics of the vehicle surface. It can capture intricate details, providing a more comprehensive reconstruction of the vehicle surface and geometry. Those 3-dimensional splits or blobs help us render the point cloud, representing every point as a gaussian or normal distribution in three-dimensional space. From there, we optimize using traditional graphic rasterization techniques and adjusting the density of the gaussians until we get results representative of the original training data. What's ultimately produced is a fully interactive 3D model of the vehicle. This is important because it allows you to view the vehicle and damaged regions from novel angles and views that may not have been fully captured in the original data and also provide for more accurate scale and distance measurement enabled by full understanding the relative camera angles. Such approaches can enable more accurate labor hour predictions where the service and depth are very relevant to how long it will take to repair a part. Longer term, we think it could enable virtual inspections of additional damage types. Vehicle conditioning in the case of total loss and perhaps enable LEAP decisions by anticipating damage to interior parts that aren't visible from external pictures alone because we have a more detailed understanding damage depth. Lastly, I do want to reiterate that at Progressive, this isn't about cool science or gaussian math for its own sake. It's about our excellence core view striving for continuous improvement and better business outcomes, for our customers, employees and investors. And as John mentioned previously, we are willing to dig deep and work hard to achieve those outcomes consistent with our core values. Before wrapping up, I wanted to share our general approach when we consider build and buy decisions. First, we think those are complementary and not competitive sides of the same coin. For example, our vision solution for photo estimating is powered by things we have built, but also relies on very tight integrations with our EX platform partner to turn those predictions into the correct part numbers and actual estimates. When building or buying, we prefer to keep things decoupled. Rather than monolithic solutions, good for one purpose, but perhaps difficult to change or modify for other purposes, we prefer to build or buy systems that are open or can be decoupled so that some of the pieces and parts can be reassembled and is something entirely new. This gives us the potential to earn outsized returns on the initial technology investment and provides flexibility when new technology solutions inevitably arise. We can use those decouple pieces to drive near-term value but it also allows us to be agile in adopting new solutions like pseudo labeling, gaussian splatting and whatever may be next. We can also start to apply parts of the original solution to build something entirely new that we may not have even considered at the time. On the right is an example of that potential emerging. When we began a firm commitment to our claims Machine Vision journey with photo estimating back in 2019, we were focused on photos that were submitted after first notice of loss from customers that had selected photo estimating as their vehicle inspection option. But as we've iterated on that experience or funnel, we're now receiving hundreds of thousands of photos per year at first notice of loss, and that is an accelerating rate as we optimize. This is before a customer may have decided to opt in for photo estimating. We can and do use those for photo estimating, but we can also apply our existing machine vision capabilities in that initial triage and segmentation decision and not just photo estimating. It could allow us to make even more fine grand triage decisions based on a much more accurate damage estimate. We can also use those photos to enable more sophisticated review processes of estimates that weren't written through the photo estimating channel. We hope a few things came through today as we conclude. First, I'll remind again that the 2024 company-wide results John shared and our efforts in Claims to help enable competitive pricing starts with our people and culture. They and it are what makes such things possible. Secondly, we remain tirelessly committed and energized to chase that ever-elusive perfect cost curve balance while simultaneously investing strategically to shift it further downward, and we see further opportunities ahead to continue doing just that. And as shared in the photo estimating example, we continue to exploit advanced technology by putting it in the hands of our people because we know they will generate outcomes fully aligned with our Progressive core values and claims guiding principles and keep those virtuous cycles turning. On behalf of John and I and 65,000-plus Progressive teammates, thank you for your attention.