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Revolutionizing Prefabrication with Autodesk Platform Services and AI

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说明

Discover the power of Autodesk Platform Services and artificial intelligence (AI) to revolutionize prefabrication processes. In this session, we'll explore core industry challenges, including time-consuming tasks, repetitive actions, errors, lack of standardization, and the sharing of information about increasingly complex models with people in the shop. Witness firsthand how Autodesk Platform Services—armed with reusable assemblies, customizable rules, and a machine-learning modeling assistant—propels you toward a new era of efficiency and consistency. Furthermore, experience captivating web-based viewers that foster dynamic collaboration, as you learn how to unleash fabrication models with enhanced visualization and precision. See how these elements form a seamless and transformative process for optimizing fabrication workflows. This talk will offer a fresh perspective on driving innovation in the architecture, engineering, and construction (AEC) fabrication industry, leading to improved project outcomes.

主要学习内容

  • Learn how to implement AI to automate repetitive and time-consuming tasks in prefabrication modeling workflows.
  • Learn how to create and utilize reusable fabrication assemblies and custom rule sets to enhance workflow efficiency and standardization.
  • Learn how to integrate Autodesk Platform Services to export model information and collaborate effectively.
  • Learn how to enhance visualization capabilities through a custom web-based viewer for better project understanding.

讲师

  • Gonzalo Villanueva 的头像
    Gonzalo Villanueva
    Hello, I'm Gonzalo Villanueva, a civil engineer deeply passionate about technology's transformative potential in industries. My career in civil engineering seamlessly blends with a master's degree in AI and data science. I specialize in Python, TypeScript, and C#, using them to streamline processes within Building Information Modeling (BIM). My goal is to enhance construction project efficiency through task automation and workflow optimization. My career kicked off in the field of modeling, eventually leading me to the challenging role of manually crafting spool sheets and various related assignments. Recognizing AI's potential to optimize these processes, I transitioned into an innovator's role within the Architecture, Engineering, and Construction Industry, seamlessly merging engineering and AI expertise. Beyond my technical skills, I actively contribute to non-profit associations, organizing over 30 events. I'm dedicated to pushing boundaries and seeking new challenges. Join me on a journey of innovation and efficiency in the AEC Industry. My commitment to advancing technology and delivering impactful solutions drives my career.
  • Gustavo Gusmão
    Meet Gustavo Gusmão. Starting my journey as a Civil Engineer, I quickly found my passion in BIM. Initially, I delved into modeling and, over time, progressed to leading BIM teams. Facing challenges head-on as a BIM coordinator, I embraced them with innovative solutions. For the past two years, I have been at the forefront as a BIM Solution Architect. My enthusiasm for automation nudged me from modeling to development. Simple Excel VBAs in my early days paved the way for advanced Revit Add-ins and tailored web applications for the BIM community. Lately, I have been exploring the world of Fabrication for BIM, seeing it as the next big step. Merging my profound BIM insights with programming, I'm exploring new ways to integrate fabrication with BIM.
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      Transcript

      GONZALO VILLUANUEVA: Hello, welcome to our industry talk. Today, we will explore ways to enhance our workflow using Autodesk Platform Services and artificial intelligence. My name is Gonzalo. I am a civil engineer with a masters in data mining. I'm passionate about in engineering and programming. I have several years of experience in both fields. I always look to solve problems in a creative way and consider myself an innovator in the industry. Joining me for this discussion, let me introduce my colleague, Gustavo.

      GUSTAVO GUSMAO: Hi. Hello, everyone. I'm Gustavo Gusmao. I started as a guy deeply involved in civil engineering, like planning and cost estimation for general contractors, and then a few twists and turns later, I found myself in the BIM universe, first as a modeler and then as a coordinator, and finally, as a solution architect.

      Well, I've always been really into automation. I mean, why spend a few minutes doing a task if I can spend hours trying to build a script to do it for me right? Well, kidding aside, this is not how it works. So through the years, I've been refining my sense of what is the best solution for this. Is that really necessary? Who are the stakeholders? And as a BIM solution architect, this is an integral part of my job. And well, this presentation is a good sample of this job, and we are really excited to present it to you guys today.

      GONZALO VILLUANUEVA: Right, thank you, Gus. Now, we can start with our first topic, which I'm sure we have all experienced, and that is the feeling of deja vu when we face something repetitive. This, just as you can see, is an image from the movie Modern Times.

      Repetitive tasks are everywhere, but we don't have to let this stop us to continue working to turn these tasks into the spark that motivates innovation because-- and this way, be able to unlock the automatization of our task. To be clear, let's look at a practical example. In our case, we want to see how to improve the process of creating and manufacturing assemblies.

      The current workflow is quite simple. We start with the modeling, then we add all the necessary pieces in the final adjustment. Next, we create assemblies and finally, we send all the information to the shop for the manufacturing process. And then the entire cycle starts over again this is not only monotonous but also a bottleneck. Thus, we must discuss how to prevent this problem and how to create modern solutions.

      So let me repeat the question, how can we do this? Well, one might think that Autodesk would have a tool that does exactly what we need. But the reality is that, no, there is no tool that perfectly matches our requirements. However, the company gives us many options to build our own solution.

      For example, it gives us the API of software like Revit and Inventor and all the tools available in Autodesk Platform Service. And finally, apps like Flow, Fusion, and Forma to automate some processes. So with all that said, we can finally start thinking about our approach to the solution that we call the question.

      To begin with this question, we have to answer what we want to do. We want to save time to say goodbye to unnecessary back and forth. We want to innovate to break the monotony of creating assemblies. We want to focus on the user through a web platform. And finally, we want to integrate AI to assist us.

      With all that in mind, we arrived to our solution that we call, this, our MEP modeling assistant our first attempt to approach solving the problem I mentioned earlier. Now, I will let Gustavo take over and provide more details about it.

      GUSTAVO GUSMAO: OK, now that you guys were formally presented to Blaze, let's get a bit more familiar with it. I want to start with the foundation, a blueprint of what we will be detailing in a while, as you can see here, we begin in Revit. It. Is where we take details from the Revit assemblies and create what we call catalogs. Think of a catalog like a toolbox, where tools are grouped by the users.

      So in our solution, the assemblies are grouped by something in common, its use, manufacture or price range for example. Well, we also need to apply this catalog somewhere, right? So this is our target model. This is more of a backstage process size. This is just a preparation for what is coming.

      And well, here is where the fun part is, the web page. And our goal is simple, make the everyday tasks of building and arranging assemblies pieces smoother and friendlier on a web page. And finally, once we are done playing with the web, we bring everything back to Revit.

      So this was just an overview of the web the App, but you might be wondering at this point, why jump to the web when we started in Revit and ended it in Revit too? Well, Revit it's an awesome software. If you try to use other apps or back in the time when we need to create every field line by line in AutoCAD, you know how handy Revit is for us.

      Well, Revit has its learning curve, and sometimes, you might have the best designer or coordinator with years of experience, and still, he will need to dedicate some time on learning Revit. So this was the spark that engaged us in pushing it to the web. Anyone can get a laptop or a tablet and navigate to a website with no complex wheels or filters or anything like that. So no confusing stuff. Just pick and place, choose an assembly from the catalog, click, and add it, easy.

      But if the web is so cool, why would we go back to Revit? Well, that's because Revit lets you dive deep, from creating simple drawings to highly detailed pose with quantities and et cetera. So after having fun on the web, we bring everything back to Revit for the final touches.

      So for the GNOME developers, folks watching this, I apologize in advance, but this might not be the funnest part of this presentation. So I'm about to give you a brief idea of the architecture of this app, but no worries. This is just an overview. If you are interested in more details, take a look at the handout or feel free to contact us later.

      So the app's architecture starts with our primary stakeholders, the users. The user is going to have contact with two components in this schema. First, the add-in, the Revit add-in, which as I mentioned before, is responsible for exporting the catalog and also the target models that will receive the assemblies. This add-in is-- was created with C#, and it communicated with our back end, which uses .NET Core 6.

      Maybe web API or a back end sounds like weird tech words, but in short, It's usually the core of web applications, which connect different components or database. So for example, when you are at home watching your Netflix, all those fancy interface is your front-end component, but all information shown there as the movie name, description, or even the movie itself, comes from a back end running somewhere in the world.

      Well, also communicating with the back end are web APIs. It is our web page or interface. This is where users will be interacting with the model and catalog. This is our front end, which uses React and TypeScript to be implemented. Well, if I say that the previous part could be boring for the nondevelopers, I'm afraid to say that this part will become a bit worse, but no worries. We are getting to the fun part soon.

      Picture our app like a big theater. What you have seen so far, the web page and the add-in, are the front stage, where the actors perform, but there is also a backstage, where all the magic preparations happens. These components, for us, are the database, where we store the catalog information and project data, and given the data type we are working in here, we decided to move with a nonSQL solution called MongoDB.

      We also have this mysterious component I don't want to spoil the surprise here. So let's follow up on this in a while. And on the other hand, we also use APS, a.k.a. Autodesk Platform Services, formerly known as Forge, and given all the backstage components, I want to get into more detail on this APS.

      So APS is a collection of tools from Autodesk that us build our app faster and better besides starting our app not from the ground but with a solid base, some of the products we are going to be using here are the authentication API so we can use Autodesk Construction Cloud credentials to authenticate our users in the app we also use data management which gives us information and some control over the files hosted on Autodesk Construction Cloud so we can list project folders, publish models, gather metadata, and et cetera.

      Well, we think that the start of our APS-- the APS start on our application is definitely the Viewer, a WebGL-based component that wraps all the 3D interaction for us. This is compatible with several file types, and it really shines when working with Autodesk formats like the Revit for example, making the integration even easier.

      So the other components listed here are not currently in use on our app, but they are also available on APS, and they are on our roadmap for the future features. But for example, like Autodesk Construction Cloud would help us manipulate projects creation or editing permissions. Data Exchange could make it easier to extract information from Revit models and remove the first step of exporting data from Revit that I just mentioned.

      All the data extraction could be done automatically. There is also the design automation where Autodesk handles Revit instances on their servers to perform actions in Revit files also be aware that these features might get updates since Autodesk is releasing new features on APS. All the time all right enough with the behind-the-scenes stuff. Let's dive into what you guys have been probably waiting for, the app itself.

      Let's take a look at the grand entrance to add-ins. There are three buttons available on our Revit. So once you have your model open and accessible with the symbols created, you can export them as a catalog. It could be a real project or a reference model you created for this purpose. Then, you click on Export catalog and all the corresponding views are exported as a new catalog. OK, so now you have your catalogs and you want to add them to the new models.

      So you open the target model in Revit and click Export Project. This will restore all the data we need to calculate the assemblies on the web. Well, finally, we saw all the gathering information. Let's take a look into the website. Well, this is our web page.

      Not much to see here, but I would like to bring your attention to the use of the APS authentication. I explained it before, like, when you have to sign up for a new app and it feels like it takes forever, like filling out details, confirm verification, codes sent by emails and all that jazz. Well, we thought, why not make life a little easier? So when you hop onto our app, you can simply use Autodesk login and it's all about making things simple.

      OK, so here's where users can navigate on different catalogs, and within each catalog are all the assemblies waiting to be explored. Something you note-- you'll be-- note is how you can organize these catalogs. Maybe you want to group them by manufacturers or by clients or even sorting them based on price ranges. The choice is yours.

      Now, you'll notice we are leveraging in the Autodesk viewer. That means that you can view these assemblies and also interact with them, rotate them, select specific elements, or even pull out details that were embedded in Rev, like comments or descriptions for example. Well talking about the products the target projects this is where all the action happens. It's called the Viewer Ribbon. Here, users can scroll through ACC Revit models, choose one, and start to add assemblies. And how does that work exactly?

      First, the user will spot and select an assembly from the knit list on the left. Got one? Great, then next, you need to choose the perfect spot in the viewer. Just make sure it is a logical spot like a pipe or a fitting size. We cannot just place it anywhere, and the app is aware of that.

      Now, if you have picked a valid location, you see that it turns into an action on the right list, the green one, and the assembly will be inserted in the viewer. Think of each action like a note to yourself or a bookmark. These actions are our way of saving information and allowing users to collaborate in real time. Everything is tracked in our database. Awesome, finish with all the placements.

      Great, you can wrap it here if you just want a visual model in the web, but if you are looking to make it all official in a Revit file, this is where Revit add-ins come back into play again. So this is the add-in again, so open the same ACC model Revit that you are tweaking on the web. Now, all you got to do is hit Import assembles and everything you did on the web will populate here. How? Remember those actions we talked about? Each one of them will represent an assembly and will be populated here in the model.

      OK, well, now that we have taken you on a quick tour of our app, I'm sure a couple of questions might come up. So why an assembly catalog? Think about it. Sometimes in a project, [INAUDIBLE] and assembly requirements might differ. That's normal, but there are occasions wherein certain assemblies are essentially identical. They might just be situated in different spots or have different pattern, like needing to be near to a restroom for example.

      So why recreate the wheel every single time? Why not have a catalog, a ready repository that can be tapped into for any project, any client? But why choose a web-based approach? We did touch upon this earlier and the goal is to simplify the process. Often, the decision maker for the assemblies might not be the Revit specialist. It might be a field expert with years of hands-on experience. Expecting them to learn Revit is not efficient

      Moreover, the beauty of the solution lies on its integration. Beyond just being a web application, we are bridging multiple services. We are pulling elements from Revit, utilizing APS for multiple functionalities, and employing our back end to run essential checks, ensuring that the assembly placement makes sense. The power of our application isn't just in an individual feature, but it's how seamless they are interwoven. And well, this is all the screens for our application from the main page to Revit and back to Revit again.

      All right, we covered quite a bit, and this is great, right? This is a great workflow. However, some of you might feel that something is missing you're probably skimming the session description, or we call it the title and thought. Hey, there's something else promised, here. Gonzalo, can you help us to figure out what might be missing?

      GONZALO VILLUANUEVA: Yes, it's that thing you mentioned earlier, the mysterious component in our back end. It's true we haven't detailed it yet, but where is the AI part of this? That is an excellent question. I think it's time to reveal the role of AI in all of this. But before, that, we have to start talking about what is AI? At this point, I think all of what AI means.

      And as harness the formative power of Autodesk tools, we also entrust AI with the task of handling repetitive tasks and pattern identification within our application. But this brought another question to the table that is necessary to ask ourselves, are we being replaced? The short answer is no. We are not being replaced, at least for now.

      AI could be a powerful assistant it complements and elevates our capabilities and takes charge of all the repetitive tasks. Imagine having an assistant that has observed and learned from every decision you have made in the past regarding when and how to use certain assemblies.

      This assistant powered by machine learning model can automatically suggest which assembly to use based on its accumulated knowledge. But how can it actually assist me? Well, I'm going to explain a few examples such as intelligent assembly suggestions and the optimization of choices. For more, Gustavo is going to explain other features for the future.

      Let's start with the intelligent assembly suggestion. Here, We have the same UI that we mentioned before, but if you pay attention to the bottom right corner, you can see how our robot is popping up in. In this case, we don't have any action in our action list, but with a few clicks, Blaze will start to change.

      In this case, it identifies a few similar locations where it can place our currently selected assembly. If we click Apply, then it will automatically place our assembly for us, and then we can continue working on other parts of our building.

      Next, we have the optimization of choice features. In this case, we place an assembly in a spot that is not allowed. So our system will inform us that this assembly is misplaced, and it will ask us if we want to fix it. With just a click, Blaze, our assistant, will fix the problem and add-in the element where it is fixed.

      But how does it work? Well, we have a few paths to choose here. The first one is called supervised learning. Think of this one as a guided learning, where we prepare all the data to be learned, in this case, our Revit models. The second one is the unsupervised learning. It's the one we choose for our training because it doesn't need labeled data. We can live just with our model so that I find all the hidden patterns and structure in our model.

      The last one is reinforcement learning. That involves a trial-and-error training approach that improves decision making by receiving awards. But it doesn't stop there. The idea for the future is to continue learning and keep feeding our AI with different of information. In this case, we only use the Revit model from our company, but we could also add all the input by the users that use our application. And then, the AI can become more accurate in our assistance.

      Well, you may ask if that is all that the AI offers. The reality is no. We have just seen a little of what AI can do. We have to explore what enhanced AI means and what can be expected beyond that limit. We had to get all the ideas that we had to the reality. So I'm going to summarize what Gustavo is going to explain a little more about our future roadmap.

      We want to anticipate the next step of the project. We want to automate real time reviews. We want to make more integration with industry-standard tools like Inventor, and we also want to create an assembly marketplace, where all manufacturers can sell and share their products.

      GUSTAVO GUSMAO: Well, as Gonzalo pointed out, while we are excited about what we have achieved so far, our aspirations reach even higher, and well, we are not working on these features right now, but we believe that there are a place for it in the future. One of the exciting features we are contemplating is the anticipated project next steps.

      So during a project life cycle, specific patterns often emerge in how a person works. For instance, if you are consistently placing assembly in some logic across different projects, our application could recognize this behavior. The next time you initiate a similar project our application could proactively suggest, hey, I noticed that we have done this before. Would you like me to apply in this project?

      And this is not only for saving time, but it could also help preconfigure a model even before you delve deep into it. In a sense, it's about making the application smarter, more intuitive, and more tailored to each user's unique workflow.

      But while we have developed an application that streamlines and even automates many processes for the user, let's be realistic here. We recognize that there's always going to be an element of manual intervention. Every project is distinct and unique requirements arise constantly. This individual nature of each project means that there is always going to be some hands-on tasks involved.

      So what's our approach to this ever-evolving landscape? We have been considering on integrating artificial intelligence to spot discrepancies or errors in the model. Let's take an example, you might be using an assembly that's typically not recommended for certain projects or perhaps it's positioned too close to a electrical panel for example.

      These nuances might be second nature to someone with years of project expertise but could be a challenge for someone who just started with the application. This is precisely where AI can play an important role. Providing guidance insights or perhaps even preventing costly errors down the road.

      Well, right now, our application is deeply integrated with the Autodesk ecosystem, but we are conscious that many projects might involve other trades or organizations that use a different set of tools software or databases. So how do we bridge that gap? Our vision extends to integrating our app with other industry standard platforms we are looking at industry standards, such as IFC for example. We are gearing up for a future where our app becomes the hub that seamlessly connects to a variety of tools and platforms.

      Well, now, this is the feature that, for this speaker over here, is the more excited to be implemented in the future, and well, let's draw a parallel. When you are shopping for a computer, you have the option to handpick components like the CPU, the screen, keyboard one by one, or you can buy that all together.

      So why can we replicate this concept here? Rather than individually sourcing parts, coordinating fabrication, and finally dispatching to the field, why not offer a one-stop solution? So let's imagine that contractors can simply browse a manufacturer's catalog, place an order, and receive a ready-to-install assembly right on their project site. In an age where prefabrication is growing fast, this is not just a far-fetched dream but an opportunity, and we want to explore that.

      So we are getting to the end of this presentation, and after presenting our current features implementations and what's coming next, one thing becomes clear for us because the construction industry is rapidly evolving and we believe that prefabrication stands on the forefront of this revolution. So we are not just observing this transformation, we are trying to actively embrace it. Modern constructions demand modern solutions, and that's precisely what we are committed. Right, Gonzalo?

      GONZALO VILLUANUEVA: You are right, Gustavo. As you mentioned earlier, the industry is moving fast and we need all the tools and allies that can we handle. In this case, AI will become the best assistant that you can get, and we have to consider AI as an assistant and ally, not an enemy. We are talking about smart homes and cities, but we are still manually adding assemblies in a process.

      We have to change that mentality and start embracing the change. With that in mind, we wanted to spend the last few minutes of this presentation talking about the future. We have a lot of ideas and dreams about it, but we have one thing sure, that our tools and AI will help us to build better solutions, better cities, and better buildings. Thank you so much for your time, and see you all the next time.

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      我们通过 New Relic 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. New Relic 隐私政策
      Salesforce Live Agent
      我们通过 Salesforce Live Agent 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Salesforce Live Agent 隐私政策
      Wistia
      我们通过 Wistia 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Wistia 隐私政策
      Tealium
      我们通过 Tealium 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Tealium 隐私政策
      Upsellit
      我们通过 Upsellit 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Upsellit 隐私政策
      CJ Affiliates
      我们通过 CJ Affiliates 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. CJ Affiliates 隐私政策
      Commission Factory
      我们通过 Commission Factory 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Commission Factory 隐私政策
      Google Analytics (Strictly Necessary)
      我们通过 Google Analytics (Strictly Necessary) 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Google Analytics (Strictly Necessary) 隐私政策
      Typepad Stats
      我们通过 Typepad Stats 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Typepad Stats 隐私政策
      Geo Targetly
      我们使用 Geo Targetly 将网站访问者引导至最合适的网页并/或根据他们的位置提供量身定制的内容。 Geo Targetly 使用网站访问者的 IP 地址确定访问者设备的大致位置。 这有助于确保访问者以其(最有可能的)本地语言浏览内容。Geo Targetly 隐私政策
      SpeedCurve
      我们使用 SpeedCurve 来监控和衡量您的网站体验的性能,具体因素为网页加载时间以及后续元素(如图像、脚本和文本)的响应能力。SpeedCurve 隐私政策
      Qualified
      Qualified is the Autodesk Live Chat agent platform. This platform provides services to allow our customers to communicate in real-time with Autodesk support. We may collect unique ID for specific browser sessions during a chat. Qualified Privacy Policy

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      改善您的体验 – 使我们能够为您展示与您相关的内容

      Google Optimize
      我们通过 Google Optimize 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Google Optimize 隐私政策
      ClickTale
      我们通过 ClickTale 更好地了解您可能会在站点的哪些方面遇到困难。我们通过会话记录来帮助了解您与站点的交互方式,包括页面上的各种元素。将隐藏可能会识别个人身份的信息,而不会收集此信息。. ClickTale 隐私政策
      OneSignal
      我们通过 OneSignal 在 OneSignal 提供支持的站点上投放数字广告。根据 OneSignal 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 OneSignal 收集的与您相关的数据相整合。我们利用发送给 OneSignal 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. OneSignal 隐私政策
      Optimizely
      我们通过 Optimizely 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Optimizely 隐私政策
      Amplitude
      我们通过 Amplitude 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Amplitude 隐私政策
      Snowplow
      我们通过 Snowplow 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Snowplow 隐私政策
      UserVoice
      我们通过 UserVoice 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. UserVoice 隐私政策
      Clearbit
      Clearbit 允许实时数据扩充,为客户提供个性化且相关的体验。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。Clearbit 隐私政策
      YouTube
      YouTube 是一个视频共享平台,允许用户在我们的网站上查看和共享嵌入视频。YouTube 提供关于视频性能的观看指标。 YouTube 隐私政策

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      定制您的广告 – 允许我们为您提供针对性的广告

      Adobe Analytics
      我们通过 Adobe Analytics 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Adobe Analytics 隐私政策
      Google Analytics (Web Analytics)
      我们通过 Google Analytics (Web Analytics) 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Google Analytics (Web Analytics) 隐私政策
      AdWords
      我们通过 AdWords 在 AdWords 提供支持的站点上投放数字广告。根据 AdWords 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AdWords 收集的与您相关的数据相整合。我们利用发送给 AdWords 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AdWords 隐私政策
      Marketo
      我们通过 Marketo 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。我们可能会将此数据与从其他信息源收集的数据相整合,以根据高级分析处理方法向您提供改进的销售体验或客户服务体验以及更相关的内容。. Marketo 隐私政策
      Doubleclick
      我们通过 Doubleclick 在 Doubleclick 提供支持的站点上投放数字广告。根据 Doubleclick 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Doubleclick 收集的与您相关的数据相整合。我们利用发送给 Doubleclick 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Doubleclick 隐私政策
      HubSpot
      我们通过 HubSpot 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。. HubSpot 隐私政策
      Twitter
      我们通过 Twitter 在 Twitter 提供支持的站点上投放数字广告。根据 Twitter 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Twitter 收集的与您相关的数据相整合。我们利用发送给 Twitter 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Twitter 隐私政策
      Facebook
      我们通过 Facebook 在 Facebook 提供支持的站点上投放数字广告。根据 Facebook 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Facebook 收集的与您相关的数据相整合。我们利用发送给 Facebook 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Facebook 隐私政策
      LinkedIn
      我们通过 LinkedIn 在 LinkedIn 提供支持的站点上投放数字广告。根据 LinkedIn 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 LinkedIn 收集的与您相关的数据相整合。我们利用发送给 LinkedIn 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. LinkedIn 隐私政策
      Yahoo! Japan
      我们通过 Yahoo! Japan 在 Yahoo! Japan 提供支持的站点上投放数字广告。根据 Yahoo! Japan 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Yahoo! Japan 收集的与您相关的数据相整合。我们利用发送给 Yahoo! Japan 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Yahoo! Japan 隐私政策
      Naver
      我们通过 Naver 在 Naver 提供支持的站点上投放数字广告。根据 Naver 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Naver 收集的与您相关的数据相整合。我们利用发送给 Naver 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Naver 隐私政策
      Quantcast
      我们通过 Quantcast 在 Quantcast 提供支持的站点上投放数字广告。根据 Quantcast 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Quantcast 收集的与您相关的数据相整合。我们利用发送给 Quantcast 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Quantcast 隐私政策
      Call Tracking
      我们通过 Call Tracking 为推广活动提供专属的电话号码。从而,使您可以更快地联系我们的支持人员并帮助我们更精确地评估我们的表现。我们可能会通过提供的电话号码收集与您在站点中的活动相关的数据。. Call Tracking 隐私政策
      Wunderkind
      我们通过 Wunderkind 在 Wunderkind 提供支持的站点上投放数字广告。根据 Wunderkind 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Wunderkind 收集的与您相关的数据相整合。我们利用发送给 Wunderkind 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Wunderkind 隐私政策
      ADC Media
      我们通过 ADC Media 在 ADC Media 提供支持的站点上投放数字广告。根据 ADC Media 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 ADC Media 收集的与您相关的数据相整合。我们利用发送给 ADC Media 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. ADC Media 隐私政策
      AgrantSEM
      我们通过 AgrantSEM 在 AgrantSEM 提供支持的站点上投放数字广告。根据 AgrantSEM 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AgrantSEM 收集的与您相关的数据相整合。我们利用发送给 AgrantSEM 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AgrantSEM 隐私政策
      Bidtellect
      我们通过 Bidtellect 在 Bidtellect 提供支持的站点上投放数字广告。根据 Bidtellect 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bidtellect 收集的与您相关的数据相整合。我们利用发送给 Bidtellect 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bidtellect 隐私政策
      Bing
      我们通过 Bing 在 Bing 提供支持的站点上投放数字广告。根据 Bing 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bing 收集的与您相关的数据相整合。我们利用发送给 Bing 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bing 隐私政策
      G2Crowd
      我们通过 G2Crowd 在 G2Crowd 提供支持的站点上投放数字广告。根据 G2Crowd 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 G2Crowd 收集的与您相关的数据相整合。我们利用发送给 G2Crowd 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. G2Crowd 隐私政策
      NMPI Display
      我们通过 NMPI Display 在 NMPI Display 提供支持的站点上投放数字广告。根据 NMPI Display 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 NMPI Display 收集的与您相关的数据相整合。我们利用发送给 NMPI Display 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. NMPI Display 隐私政策
      VK
      我们通过 VK 在 VK 提供支持的站点上投放数字广告。根据 VK 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 VK 收集的与您相关的数据相整合。我们利用发送给 VK 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. VK 隐私政策
      Adobe Target
      我们通过 Adobe Target 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Adobe Target 隐私政策
      Google Analytics (Advertising)
      我们通过 Google Analytics (Advertising) 在 Google Analytics (Advertising) 提供支持的站点上投放数字广告。根据 Google Analytics (Advertising) 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Google Analytics (Advertising) 收集的与您相关的数据相整合。我们利用发送给 Google Analytics (Advertising) 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Google Analytics (Advertising) 隐私政策
      Trendkite
      我们通过 Trendkite 在 Trendkite 提供支持的站点上投放数字广告。根据 Trendkite 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Trendkite 收集的与您相关的数据相整合。我们利用发送给 Trendkite 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Trendkite 隐私政策
      Hotjar
      我们通过 Hotjar 在 Hotjar 提供支持的站点上投放数字广告。根据 Hotjar 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Hotjar 收集的与您相关的数据相整合。我们利用发送给 Hotjar 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Hotjar 隐私政策
      6 Sense
      我们通过 6 Sense 在 6 Sense 提供支持的站点上投放数字广告。根据 6 Sense 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 6 Sense 收集的与您相关的数据相整合。我们利用发送给 6 Sense 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. 6 Sense 隐私政策
      Terminus
      我们通过 Terminus 在 Terminus 提供支持的站点上投放数字广告。根据 Terminus 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Terminus 收集的与您相关的数据相整合。我们利用发送给 Terminus 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Terminus 隐私政策
      StackAdapt
      我们通过 StackAdapt 在 StackAdapt 提供支持的站点上投放数字广告。根据 StackAdapt 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 StackAdapt 收集的与您相关的数据相整合。我们利用发送给 StackAdapt 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. StackAdapt 隐私政策
      The Trade Desk
      我们通过 The Trade Desk 在 The Trade Desk 提供支持的站点上投放数字广告。根据 The Trade Desk 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 The Trade Desk 收集的与您相关的数据相整合。我们利用发送给 The Trade Desk 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. The Trade Desk 隐私政策
      RollWorks
      We use RollWorks to deploy digital advertising on sites supported by RollWorks. Ads are based on both RollWorks data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that RollWorks has collected from you. We use the data that we provide to RollWorks to better customize your digital advertising experience and present you with more relevant ads. RollWorks Privacy Policy

      是否确定要简化联机体验?

      我们希望您能够从我们这里获得良好体验。对于上一屏幕中的类别,如果选择“是”,我们将收集并使用您的数据以自定义您的体验并为您构建更好的应用程序。您可以访问我们的“隐私声明”,根据需要更改您的设置。

      个性化您的体验,选择由您来做。

      我们重视隐私权。我们收集的数据可以帮助我们了解您对我们产品的使用情况、您可能感兴趣的信息以及我们可以在哪些方面做出改善以使您与 Autodesk 的沟通更为顺畅。

      我们是否可以收集并使用您的数据,从而为您打造个性化的体验?

      通过管理您在此站点的隐私设置来了解个性化体验的好处,或访问我们的隐私声明详细了解您的可用选项。