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Expert Use of the AEC Data Model API: A Technical Deep Dive into Enhancing Your Data Potential and Efficiency in AEC Projects

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说明

AEC Data Model software is a next-generation cloud solution that delivers granular data access to the right users at the right time via AEC Data Model GraphQL APIs. AEC Data Model is extensible, flexible, connected, and shareable—all without the need of writing custom application-specific plug-ins to achieve a variety of cloud-based automations and workflows. Join us for this session to learn about AEC Data Model and its capabilities.

主要学习内容

  • Learn about the AEC Data Model API and what you can do with the granular AEC data made available by this API.
  • Familiarize yourself with the latest features and the workflows you can achieve with them.
  • Gain an overview of our API developer docs and step-by-step tutorials, including how to use our interactive data explorer.

讲师

  • Zhong Wu 的头像
    Zhong Wu
    Principal developer advocate in Autodesk with more than 7 years programing experience on CAD related application, and about 10 years experiences on the API support/consultant/evangelism for AEC industry products, including ACC, BIM360, Revit, and also contribute in APS support and evangelism.
  • João Martins
    I joined the Developer Advocates team in 2020, working mostly with .NET applications. I'm a Civil Engineer graduate with experience in software development. Since I joined the DAS team, I had the pleasure of working with customers interested in many different applications. It's always a great experience when we get together with customers and they share their workflows with us so we can think about ways to improve their process while taking the most from our platform.
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Transcript

ZHONG WU: Hello, everyone. Welcome to AU 2024. Today, we are going to have a technical deep dive into our AEC Data Model APIs. We will showcase how you can enhance your data potential and efficiency in AEC projects.

First, quick introduction on the presenters for this class. I'm Zhong Wu, a developer advocate from Autodesk developer advocacy and support team, focused on the APS service in AEC industry, including ACC, BIM360 APIs, and AEC Data Model, and also Revit Design Automation, extra. Today with me we have Mike and Joao. So Mike and Joao, can you do a quick introduction?

JOAO MARTINS: Yes. Thank you, Zhong. My name is Joao Martins. I'm also from developer advocacy and support team. Work mostly with also AEC related APIs and mostly with .NET. I can help with support, events, and presentations like this. Glad to help. To you, Mike.

MIKE ENGEL: Yep. And I'm Mike Engel, a product manager here with our Autodesk teams working in support of AEC industry and the AEC software products we deliver, particularly today focused on our AEC Data Model.

ZHONG WU: Yeah. Thank you. Yeah. And this is the safe harbor statement. Please read the statement. One thing you need to be aware is that please don't make your business decision based on the presentation. Talk to us before making any decisions. Yeah. So in today's session, Mike will provide a quick recap of the Autodesk data strategy and what is a AEC Data Model.

Then Joao and I will move on to introduce the details and the key principles of AEC Data Model. And we will walk you through in details in different capabilities of AEC Data Model APIs and introduce our core code samples. After that, yeah, Mike will take over to showcase a couple of core successful stories created by our customers using AEC Data Model APIs.

So yeah. Before handing to Mike, I want to bring one reminder here. This class assumes you have a basic knowledge of web programming. Seems without a deep dive into AEC Data Model APIs, it's not very complex, but you may feel a little challenged if you don't have any programming skills. So now let's kick off the session by Mike for data strategy and Data Model introduction.

MIKE ENGEL: Thanks, Zhong. Yep. First thing we want to do is just do a quick recap of Autodesk journey to granular data to just set some context of what you'll be seeing in the presentation. Over the years, we've seen three key trends emerging and accelerating. You, our customers, need more automation, whether this is to increase productivity, improve efficiency or just faster time to market and your solutions.

There is a need for increased collaboration, whether that is within organizations or across disciplines. And we see a continued trend towards industry convergence across AEC manufacturing and media and entertainment industries. And at the foundation of all of these trends, we see the foundation of data.

To respond to these challenges in our data strategy as we look towards the next three years, we're focused on three key areas. Data granularity, data interoperability, and data accessibility. And this presentation with the AEC Data Model will be mostly focused on data granularity. If we look at it today, files are the smallest unit of collaboration. But file based collaboration is very painful. Files move between different products, organizations.

There's a patchwork of standards, which results in challenges like translation and the fact that multiple disciplines cannot work on the same design simultaneously. It also leads to mistakes as design teams manage these different files and their versions. And lastly, it makes the exchange of data across organizational boundaries difficult and often putting IP at risk.

It's clear we need better design to make data management by putting together a standardized way of describing data, allowing for real time or close to real time access to this data and ensuring access controls so that you have the right data available to the right people at the right time. Imagine a world where data and workflows come together across the entire product lifecycle. At Autodesk, our vision with the industry data models to democratize access to design and make data normally trapped within files so that your customers can deliver better and cheaper products to market faster.

We wish to unlock a valuable design and make granular data that is generated by the Autodesk flagship products and provide access to this granular information via APIs. We look to delight you by building a net new experiences through industry clouds on granular data. Lastly, but not least, we hope to enable value differentiating outcomes that will fuel the digital transformation and disrupt the industry. Now let's look a little more specifically at the AEC Data Model.

Our specific vision for the AEC Data Model and the AEC industry is to provide a vision where we provide open, extensible data platform which connects the AEC and O teams in the tools of their choice. Providing value through secure, compliant support of this data, scalability across the diversity of it, connected experiences, easily accessible, mutable, and extendable along with granularized and regionalized.

And today, as we'll see in the API, this first ability with the AEC Data Model is generally available. In June of 2024, we released this to general availability to all Autodesk subscribers of Autodesk Docs. Currently, it is compatible with Revit 2024 and above in Autodesk Docs, and you can enable it for your account today by accessing it in the account admin and toggling on AEC Data Model activation. Now, we'll hand it back off to Zhong to give you more details on this API.

ZHONG WU: Thanks, Mike. Was a great introduction. Now Joao and I will go deep dive into our AEC Data Model APIs, including the concept and also the user scenarios. So I believe you guys here are familiar with the BIM data. So BIM and AEC does not fit nicely into an Excel spreadsheet. It's complex.

Sometimes it's a folder system which looks like a tree structure. Other times, inside of the design model, it's more like a graph structure. REST API works great when the data is a simple, flat list, but for data like a folder system or BIM data model, REST API is not efficient. You have to recursively call the REST APIs to get the result. Joao, is there a better solution here?

JOAO MARTINS: Yes, Zhong. That's what GraphQL is, an interface language designed by Facebook that makes it fast to work with complex data structures, like trees and graphs. AEC Data Model stores data in a graph structure, which makes it a perfect match for GraphQL. With a stronger interface like GraphQL, we can be faster about how we get the AEC data. Just one fast request instead of hundreds of requests from different REST endpoints.

So to recap, basically, it is easier to read. You have one endpoint, not hundreds. You have granular access to the graph data. You can choose the shape of your response to fit your workflow. Under the hood, when you send a request, a resolver is calling the APS endpoints on your behalf. You just call the API once and let the resolver do the heavy lifting. Faster for you and faster for Autodesk.

ZHONG WU: Cool, Joao. Yeah, so GraphQL seems working perfectly with AEC data. Let's look at the core AEC Data Model constructs. First of all, the top level category within the API schema is element group. In some cases, you may also consider it simply as a model or a design. It contains elements and its version based. So each elemental group includes multiple elements. Elements are the building blocks of AEC data.

For example, a window instance, a wall instance and a wall type and a window type, they are all elements. So it provides a good flexibility to adapt to different requirements. So it is very easy to be extended in future. Different elements may have dependency or relationship. For example, a wall instance belongs to a wall type. This relationship is represented by reference property in AEC Data Model.

Properties are very important data within an element. They are well defined data describing the element, such as Revit parameters, including area, volume. Last but not least, each property also has property definition, which provides detailed metadata about the property, including unit and types and extra. With this concept, Joao, maybe you can explain them in more detail with an example.

JOAO MARTINS: Yes, Zhong. As an example, let's consider a restart from one project, including all of its disciplines available through the AEC Data Model. At the entry level of our hierarchy, we have the element groups that you explained. In this example, let's assume a group of elements in the architectural design. The next level represents each one of the individual elements available. In this example, we can think about one specific window.

Now, in a more granular level, we have access to these window's properties. We can think about one, for example, the head height. And lastly, each property will have its own definition. So in this case, we can think about the units being used by this property. In this graph context, each element also has the reference property which connects these elements with each other according to the hierarchy of the model. In the case of a window, we could, for example, obtain its reference level, its type, materials, and any element associated.

ZHONG WU: Yeah. Thank you. Thank you, Joao. So after you understand the constructs of AEC Data Model, let's take a look at the details of AEC Data Model queries to understand its capabilities. Sometimes, for example, we would like to get all the elements among the ACC, the whole hub, or the whole project. For example, to get all the windows instance within the hub. Joao, I know that we can use the simple query, elements by hub or elements by project. But can you do a live demo to show how easy it is?

JOAO MARTINS: Yes, sure. Let me change here from presentation to a live demonstration. In this case, first, let's focus on retrieving all the elements from the hub. I already ran this query here to retrieve all my hubs. And from one of my hubs IDs-- in this case, I'm picking this one-- I can simply use the query you shared in the presentation. The elements by hub.

So in this case right here, to use this query, I need to specify one hub ID. I just need to pass the hub ID, and also, I'm going to add a filter. And this filter specifically, precisely, it is going to return only the elements from the windows category. To address that, I can use this syntax, specifying the property.name.category equals to windows.

And after that, in my response, I have the freedom, the flexibility to specify precisely which data is going to be returned. In this case, I'm only interested in the name and ID. As soon as I send this request, remember, we are going to see the resolver. The resolver will take place and it will fetch the data according to what we specify in the query and return that in a list for us. Here you can see all of the elements from a hub. OK, Zhong?

ZHONG WU: Yeah. Cool. So how about you get the elements from the project?

JOAO MARTINS: That's also possible. And to achieve that, we can simply take advantage of another query. Instead of elements by hub, we have also the elements by project query. And this one, instead of specifying one hub ID, we need to specify a project ID. So let me pick one of the available projects. I'll grab this one that we use in our bootcamps and replace right here. And if we use the same filter, we are going to also return-- we can also see the result, all of our elements from the windows category. OK?

ZHONG WU: Yeah. Cool. Yeah. Looks super easy to do that. Yeah. But how about retrieve elements at a specific version of design? Because that's quite common when we are doing the design. And yeah, it seems like we can use the query elements by element group at version. So can you also show us the details, Joao?

JOAO MARTINS: Sure. For that, we can take advantage of a different query. As you mentioned it, we have one to query elements by this element group at version. And with this query, we need to specify both the element group ID-- let me pick one element group here. I'm going to grab this one because this one has two versions. And we also need to specify the version number. Let me add this in a different line. And in this case, let me pass the version two.

To fetch also only the elements from the windows category, we can add the same filter as we were using before. And after passing this filter, removing this, we can specify the data being returned. So in here, let me add the name of our elements, the ID, and let me also add the properties. This way we can see also the properties from each one of our elements. And if we run this query-- let me just verify this.

And if we run this query once more, resolvers will take place, and it is going to retrieve true as a result. As you can see in this case, we have a empty array because there is no windows in this model. If we check the model I got, it is from HVAC. So we can expect the empty result. But if we change from windows to ducts, we know that HVAC models has ducts. We can now see a list containing all of the available ducts from our design at this specific version. OK, Zhong?

ZHONG WU: Yeah, cool, Joao. Yeah. But let me ask, can we do some more specific query? For example, can I query all the ducts which has a length more then-- longer than some specific value?

JOAO MARTINS: It is never enough for you, right, Zhong. But yeah, we can achieve that. We just need to pass another filter. We can specify property with the syntax property.name-- let's use the length property. And we are intersecting only those ones with values bigger than-- we have 0.1 here. Let me think. Let me see if I can grab a bigger one. 0.2. Let's use bigger than 0.2. And if we run this request, the resolvers take place and return to us only the elements passing this additional filter, only the ones with length bigger than 0.2. OK?

ZHONG WU: Great. Yeah. Thanks, Joao. Yeah. Now we understand how to retrieve elements. Yeah, but there's one specific requirement. Building a quality assurance process, there is a requirement to validate the data property definitions used across a design, like data names, unit, and types. So, Joao, can you demonstrate to us how to get these available property definitions with AEC Data Model query?

JOAO MARTINS: Yes, sure. That's another cool capability. We can check all of the properties available from our designs. To achieve that, first, the entry point would need to be one element group so we can use any query that returns an element group. So let's use this element group tip, and this requires us to specify one element ID. I'm going to use the same element group ID we used before, so we are referring to the same element, to the same element group, same design.

And in here, we have one specific query that returns to us the property definitions. This will be basically a list containing all of the properties definitions as specified. We can add the properties, names, IDs, and description. What's more, as soon as you send this request, resolvers take place and return to us the property definitions from this design. You can see left attachment, part type, and so on. Back to you, Zhong.

ZHONG WU: Yeah, that's awesome. Yeah, this query seems very simple and powerful. Yeah. But sometimes in the real world, seems a little complicated. Here is one case. So I have a model of building which includes a couple of levels, and I want to place different furniture on different level. So I want to build in this kind of relationship. And after that, if I use AEC Data Model queries, how can I use the query to get all the furnitures for one specific level?

JOAO MARTINS: Yes. This is a bit more complex scenario, but since all of this data is available in a graph structure, we can also address that. So for that, to achieve that, we can take advantage of the references between the elements. First thing we can do is retrieve the elements by element group. For this, we need to specify the element group ID. In this case, let me use this different sample right here. The basic sample project. You are probably used to that one.

And now I'm going to pass a filter. And this filter will be slightly different from the one we've been using before. This filter will focus on retrieving the levels. First thing we need to do is retrieving the levels. So we can filter based on the levels category. And let's also add another filter to return only the instances. As you mentioned, Zhong, elements can be referring to instances or types. Since we only want to return the instances, we can take advantage of the element context.

And in here, let me pass the value equal to instance. And now specify what we need to obtain inside the results. Let's add the name and the ID, and that should be enough. As soon as we send the request, this request, we can see all of the levels available in this element group. Now we can take advantage of the reference between the reference property that connects each level with each element from furniture category.

We can do this by applying or adding another query. And this query is the reference by. The elements, the furniture specific, they have a reference with the name level with the connection with the instance levels. So we can add this reference name and also a filter. This filter, it is going to return to us only the elements from the furniture category. Furniture. And let me ask another one. It is missing something here. I need to add as a query. Now it is correct.

And let's also filter to retrieve only the instances. Let me copy this here. And after that, let's list all of our instances from furniture by typing its names, IDs, and also the properties. As soon as we send this request, we can now see the previous list but appended, nested to this list. We can see for each level the list of furnitures available with all of its properties.

And another cool feature from the Explorer is that we can also add the Viewer context. So if I copy this version of the file here, paste it here, and turn on Viewer, now I have access to the Viewer context. And the Explorer works in a way that as soon as I send a query, this query once more, it is going to filter the view, the scene to match only the elements from our response. OK? So we can see that we have all of our elements from furniture category divided by level.

ZHONG WU: Excellent. Thanks, Joao. Yeah. I believe your demo provides a lot of confidence for us to try these AEC Data Model queries. Looks very simple but powerful. And I know that you also created some code samples to demonstrate the usage of AEC Data Model APIs with our very popular and powerful tool, AEC Data Model Explorer, which you used to demo the query, one of them. I think we are pretty interested in the implementation for that, especially how to connect with the IPS Viewer. Can you talk in some details about the implementation of these samples?

JOAO MARTINS: Sure, Zhong. We have that here in some slides. So if we move to the next one. This is, for example, a lecture sharing the Data Model Explorer. As you mentioned it, it connects with Viewer and is a good place for you to experiment focusing entirely on the query. And the cool feature I just shared is that it connects the elements from the response with the elements from Viewer.

And the way it works is because it takes advantage of the properties. More specifically, there is a property called external ID that you can take advantage when connecting the same element from Revit Perspective Viewer or Model Derivative perspective and AEC Data Model perspective. This is what we are doing to match the element in response with the element in the rendered scene by Viewer Library. So basically when you translate designs, you can take advantage of unique IDs. In this case, the unique ID from Revit is a GU ID that is also available in other platform.

Just like that, you can also use, if it makes sense in your workflow, the element ID. The element ID is also present as the Revit element ID in AEC Data Model and it is also available in the name of each element when you query a data through Model Derivative and Viewer. So keep this in mind. If you want to connect across multiple platforms, you can always take advantage of external ID or unique ID.

OK. Before sharing the sample codes, QR codes, let me go over how you can view our samples available. We have one organization, one GitHub organization, and in here you can see repos containing samples across taking care of many of our APIs. For AEC Data Model, we can simply filter by AEC, and you see here a list with all of the available ones. You can take advantage of the Explorer source code. So you can fork, build your own, implementing this code for your own purposes. Up to you.

And we also have another sample that takes advantage of AEC Data Model, and this is the dashboard one. We have seen how you can send requests, but in this one, this case here is focusing about connecting, taking advantage of this data that you can query to build tables and charts. So the sample works in a way that as soon as you sign in, you can select one of the hubs you have available. Let me pick this one, and select one project.

As soon as you select one project, it will start fetching data from using the AEC Data Model API. And in this case, it's going to fetch data about your rooms available and your doors available across the entire project to build these dashboards. Here, you can basically see all of the properties available and this in a chart split by the types that you are using. So it can be very useful. Combining with the power of the queries and the filtering capabilities, you can generate dashboards with all of the flexibility and granularity that this API supports.

The last sample is in cases where you want to connect the data from the AEC Data Model with Viewer, or even compare what's available with Model Derivative and what's available with AEC Data Model. Zhong will share details when to use each service, but this sample is good for you to quickly check how to obtain the properties from Viewer or Model Derivative perspective and how to obtain the same properties from AEC Data Model API.

It is just like the tutorial, we have the hubs browser tutorial we have. You have the tree view where you can specify one hub, expand, and go to one specific project. Let me find this one. Expand here. And as soon as we select a model, we can select one element and view this element's properties. This is the properties. These are the properties coming straight from Viewer, from Model Derivative, from the derivatives of the design. If you click this extension now, you can see side by side the properties from Model Derivative and the properties from AEC Data Model.

Not only the properties from this design, but this sample also takes advantage of the references and released here the properties from type, from its family and its reference level, phase created. Base constraint is a level. Top constraints is another level. And then you can easily see what is available and how to fetch that. OK? To take advantage of each one of these samples, you can either go to our hippo repo or scan one of these QR codes. Feel free to scan this. Let me stay here for 3, 2, 1 seconds. And back to you, Zhong. Thank you very much.

ZHONG WU: Yeah, cool. So yeah, with those code samples, I think you should have a basic idea about how to use AEC Data Model APIs. But last but not least, if you are working with APIs for a while, as Joao mentioned, you must have a couple of different services to get the BIM data. And confused by the differences among them you may be. Yeah. Here are some key point may help you to make the decision.

So I won't talk in very detail, but in general, if you are trying to support a wider range of industrial file format and you don't work with ACC or BIM360, so maybe you can think about a Model Derivative service. If your model is mainly about Revit and you want to do some comprehensive operations to extract some specific data which you cannot get directly from other service, then you need to think about the Revit design automation.

So if your model is hosting ACC or BIM360 and you will do a lot of querying between different versions, then think about the Model Properties service. And if you want to exchange a subset of model to other products, so then Data Exchange should be your best friend. But as we talked most of today, if you want to do a lot of queries based on some specific elements from the whole hub or whole project or the model and you just want to list some data you are interested instead of get the whole data, so then AEC Data Model might be your choice.

JOAO MARTINS: OK, Zhong. So can you share the details about one specific case? Point me to the proper service. Let's say that I want to write information back into the Revit design being, let's say two cases, modify one property available in Revit or append one property for cost, for example. What would be the best solution? What would be the best service to address that?

ZHONG WU: Yeah, that's actually a good question. So yeah, so you are asking about how to modify the existing property or add a customized property to the BIM data, right? Yeah. There are two scenarios. So if we are talking about write back the information to the model, the first scenario is that you want to or want to modify to the original model, which is, for example, Revit model.

So if you are trying to do that, to modify the original model, then the only way or only solution is design automation Revit. So this service provides you the ability to modify the original file, original model. But if you just want to add or modify a property for the cloud data. So for example, I want to add some cost information to the BIM model. You don't need to modify the original model.

Then stay tuned. AEC Data Model will provide you a very powerful extension which help you to support the extension property. So this is still working in progress, but it will come soon. So yeah. With this feature, you can add or modify the AEC Data Model properties very easily. So yeah. That's it. OK. With that, let's hand over to Mike for some successful stories based on AEC Data Model.

MIKE ENGEL: Yep. Thanks, guys. It's always great to see the amazing information that's capable with the AEC Data Model. As an extension of that, before we wrap up, I want to take a few minutes to showcase three of the solutions some of the customers, like many of you, are exploring and have created using these APIs and the capabilities.

So the first example we'll show here is from one team where they lead developments of what they refer to as the Digital Building Logbook. And they envision it to serve as a centralized repository for all of their building related information, supporting the AEC industry towards climate neutrality and building a more circular economy. This logbook is envisioned to include general details, administrative records, construction data, energy performance, and operational information.

This effort aims to streamline the organization of diverse data types and address the challenges related to data interoperability and disconnected data. They found leveraging the AEC Data Model, the DBL, as they refer to it, will enable efficient data queries and robust search capabilities to extract and access this rich data from models without the need of the original authoring tools, effectively mitigating one of their challenges with respect to interoperability and disconnected data issues common across the AEC industry. The DBL enhances the user experiences further by breaking down data for various user personas across the lifecycle of a building.

Second sample we want to share with you is from NTI, another Autodesk platinum partner, and has been closely working with our Autodesk products for quite some time. As part of some early engagement, NTI explored how the AEC Data Model APIs can be leveraged to bring in granularized data in real time from the Revit models into a BIM data management app they are currently building.

The BIM data management app's goals to empower nondesigner personas, those outside of Revit, to efficiently interact with the model and extend, annotate, and approve the information pertaining to the model without needing compulsory interface with the designer or the authoring app who's creating the model.

The third example is from Avixi, which specializes in making BIM achieve better outcomes for its clients. Here, Avixi is using the AEC Data Model API to extract Revit model data in the form of schedules so they can run quality control on this design data outside of the app. Typically, this process happens manually, and they wait for Revit models to load, export the data to Excel, then run the process of verifying the data to get a read on the model health. Taking approximately 15 minutes just to open the model, often, with additional effort to extract and process that data.

They found with this new workflow in the API, they can now accomplish the same task in 30 seconds, and they never need to open Revit in the process. Applying the same workflow over and over throughout a project lifecycle, they envision to save massive amounts of time and impact the quality of the project they deliver. So with that, those samples, we'll hand it back to Zhong to wrap it up.

ZHONG WU: Thank you, Mike. Yeah. So before ending the session, I'd like to influence two coming events here. In November, we will have our APS online training. This is for beginner to get started to learn APS service. And in December, we will have a developer accelerator in Atlanta. So this is for people who are familiar with APS, basically, and want to create a prototype based on your requirements. So with that, thank you, everyone.

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我们通过 Qualtrics 借助调查或联机表单获得您的反馈。您可能会被随机选定参与某项调查,或者您可以主动向我们提供反馈。填写调查之前,我们将收集数据以更好地了解您所执行的操作。这有助于我们解决您可能遇到的问题。. Qualtrics 隐私政策
Akamai mPulse
我们通过 Akamai mPulse 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。. Akamai mPulse 隐私政策
Digital River
我们通过 Digital River 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。. Digital River 隐私政策
Dynatrace
我们通过 Dynatrace 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。. Dynatrace 隐私政策
Khoros
我们通过 Khoros 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。. Khoros 隐私政策
Launch Darkly
我们通过 Launch Darkly 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。. Launch Darkly 隐私政策
New Relic
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。