Generating immersive animation content from static images using generative artificial intelligence

Abstract

The invention relates to generating immersive animation content from static images using generative artificial intelligence. The methods presented herein may be used to generate animated images using two-dimensional (2D) still images. The input image may be used to generate a first image corresponding to a foreground of the input image and at least one second image corresponding to a background of the input image. The second image may include an image restoration region based on a mask generated from the first image. The first image and the second image may be provided with configuration settings for rendering on a client device.

Description

Background Technology

[0001] Users requesting content and / or accessing resources may experience waiting times as resources are configured and / or as content is identified and served. During these waiting times, users should be presented with information indicating that their request is pending, rather than a blank screen or image, which could confuse them by giving the impression that their request has not been received or is frozen. Providing static images may reduce computational costs but could provide a less immersive experience. Furthermore, maintaining static images indefinitely without any indication of the status of the user's request can present the same confusion issues as a blank screen. Providing a better waiting experience is likely preferable, encouraging future user engagement with the service. However, generating specific content solely for waiting times could be prohibitively costly and could increase waiting times due to bandwidth usage, network connectivity, and / or limited computational resources. Attached Figure Description

[0002] Various embodiments according to this disclosure will be described with reference to the accompanying drawings, in which:

[0003] Figure 1A Example environments for content generation in a provider environment according to various embodiments are shown;

[0004] Figure 1B Example environments of content generation engines according to various embodiments are shown;

[0005] Figure 2A Example environments for retrieving input images according to various embodiments are shown;

[0006] Figure 2B Example environments for extracting foreground regions from images, according to various embodiments, are shown;

[0007] Figure 2C Example environments for generating masks for foreground regions in an image, according to various embodiments, are shown;

[0008] Figure 2D Example environments for image inpainting within a masked area are shown according to various embodiments;

[0009] Figure 2E Example environments for outpainting along image boundary regions, according to various embodiments, are shown;

[0010] Figure 2F Example environments for generating hierarchical output configurations according to various embodiments are shown;

[0011] Figure 3 Example call diagrams for generating and providing animation profiles to users, according to various embodiments, are shown.

[0012] Figure 4A Example processes for generating animation effects according to various embodiments are shown;

[0013] Figure 4B Example processes for selecting and providing animation effects according to various embodiments are shown;

[0014] Figure 4C Example processes for generating animation effects according to various embodiments are shown;

[0015] Figure 5 Example processes for generating configuration files to perform animations using two or more images, according to various embodiments, are shown.

[0016] Figure 6 Components of a distributed system, according to at least one embodiment, are shown that can be used to update or perform inference using a machine learning model;

[0017] Figure 7A The inference and / or training logic according to at least one embodiment is illustrated;

[0018] Figure 7B The inference and / or training logic according to at least one embodiment is illustrated;

[0019] Figure 8 An example data center system according to at least one embodiment is shown;

[0020] Figure 9 A computer system according to at least one embodiment is shown;

[0021] Figure 10 A computer system according to at least one embodiment is shown;

[0022] Figure 11 At least a portion of a graphics processor according to one or more embodiments is shown;

[0023] Figure 12 At least a portion of a graphics processor according to one or more embodiments is shown;

[0024] Figure 13 This is an example data flow diagram of an advanced computing pipeline according to at least one embodiment;

[0025] Figure 14 This is a system diagram of an example system for training, adapting, instantiating, and deploying machine learning models in an advanced computing pipeline, according to at least one embodiment.

[0026] Figure 15A and Figure 15B A data flow diagram of the process for training a machine learning model according to at least one embodiment is shown, as well as a client-server architecture for enhancing annotation tools using a pre-trained annotation model. Detailed Implementation

[0027] In the following description, various embodiments will be described. Specific configurations and details are set forth for illustrative purposes in order to provide a thorough understanding of the embodiments. However, those skilled in the art will also understand that the embodiments can be practiced without specific details. Furthermore, well-known features may be omitted or simplified so as not to obscure the described embodiments.

[0028] The systems and methods described herein can be used by, but are not limited to, non-autonomous vehicles or machines, semi-autonomous vehicles or machines (e.g., in-cabin infotainment or digital or driver virtual assistant applications), autonomous vehicles or machines, manned and unmanned robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles connected to one or more trailers, airships, boats, space shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, construction vehicles, trains, underwater vehicles, remotely controlled vehicles (e.g., drones), and / or other vehicle types. Furthermore, the systems and methods described herein can be used for a variety of purposes, such as, but not limited to, machine control, machine motion, machine driving, synthetic data generation, model training or updating, perception, augmented reality, virtual reality, mixed reality, robotics, security and surveillance, simulation and digital twins, autonomous or semi-autonomous machine applications, deep learning, environmental simulation, object or participant simulation and / or digital twins, data center processing, conversational artificial intelligence (AI), generative AI with large language models (LLM) and visual language models (VLM), optical transport simulation (e.g., ray tracing, path tracing, etc.), collaborative content creation of 3D assets, cloud computing and / or any other suitable application.

[0029] The disclosed embodiments can be included in a variety of different systems, such as automotive systems (e.g., control systems for autonomous or semi-autonomous machines, perception systems for autonomous or semi-autonomous machines), systems implemented using robots, aviation systems, medical systems, boating systems, smart area monitoring systems, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twin operations, systems implemented using edge devices, systems containing one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least partially in a data center, systems for performing conversational AI operations, systems for performing generative AI operations, systems for performing operations using LLM and / or VLM, systems for performing optical transmission simulation, systems for performing collaborative content creation for 3D assets, systems implemented at least partially using cloud computing resources, and / or other types of systems.

[0030] Methods according to various embodiments can be used to generate one or more parameters for a content generation environment. In at least one embodiment, a trained machine learning (ML) and / or artificial intelligence (AI) system (e.g., a large language model (LLM) or a visual language model (VLM)) can be used to generate parameters for the content generation environment, such as (but not limited to) camera settings, scene lighting, video parameters, and / or others for displaying objects in a scene. These parameters can be based on input provided by a user or user agent to a trained language model (e.g., an LLM, a VLM, etc.), which can then generate one or more settings based on that input. Various embodiments can be used to generate settings in two-dimensional (2D) or three-dimensional (3D) settings. For embodiments involving one or more language models (i.e., one or more LLMs, one or more VLMs, or a combination of LLMs and VLMs), the language models can receive input (e.g., prompts, requests, queries, etc.) that is parsed or otherwise formatted to generate deterministic output. For example, input provided to the language model may include a specific format for the output result, an example of the expected output result, a specific list of parameters and their respective formatting, etc. An input generator (e.g., a cue generator) can be driven or otherwise guided by one or more AI and / or ML systems to generate this input based on initial input received from a user, device, agent, and / or others. The modified input generated by the input generator can then be provided to a language model, which will generate a set of parameters for the output. This output can be further evaluated by a reviewer or other system to ensure its appropriateness. Subsequently, a profile can be generated and / or the parameters can be directly provided to the environment to configure different components (e.g., camera settings, lighting, etc.) based on the parameters generated by the language model.

[0031] Various embodiments of this disclosure are designed to generate immersive experiences for users who would otherwise experience static screens, such as during loading screens or for storefronts. Embodiments use one or more trained neural networks to transform an input image into several (e.g., two, three, four, etc.) separate images that can be used to generate animated effects presented to the user instead of static images. In an example implementation with three separate images, the initial input image (e.g., a two-dimensional (2D) image), such as a "hero image" from a video game, an item for sale, or an image selected by the user, can be processed to identify and extract the foreground. The extracted foreground components, or at least some of the extracted foreground components, can be identified and subsequently saved as a new image. In at least one embodiment, a mask associated with the extracted foreground components can be generated using the original image, and then image inpainting can be performed using one or more trained neural networks to fill in the mask, thereby creating a second image. Furthermore, a third image can be generated by one or more neural networks to perform image augmentation on the original image. Then, a collection of multiple images (e.g., foreground, image-restored background, image-expanded background, other derived objects from the scene, etc.) can be used to generate one or more effects, such as panning, scaling, and / or similar. These images can be processed offline and then provided to the user's device as images for rendering at runtime. This allows for an improved, more interactive experience for the user.

[0032] The systems and methods disclosed herein can be used to solve and overcome several problems existing in existing systems where static images are presented to users, which may have limited interactivity and / or may bore or mislead users. For example, static images may incorrectly lead users to believe their requests are “frozen” or unprocessed. Furthermore, users may gradually become tired of seeing static images and may choose to view different screens or use entirely different services. Embodiments of this disclosure can address these problems by using one or more trained machine learning models in at least one embodiment for foreground / background separation and / or image inpainting / image expansion to generate one or more immersive effects. Embodiments can be deployed in situations where there is a “wait time” for one or more operations or services, such as loading screens for game browsers, loading screens for specific games, shopping environments, resource requests, and / or the like.

[0033] In at least one embodiment, the system and method can be used to engage a user by providing an immersive and compelling way of interacting with content (e.g., game content, storefront content, etc.) while the user is waiting for some action and / or before the user performs the action. For example, a user may be waiting for an application to load or for resources to be configured and become available. As another example, a user may visit an online storefront and be presented with static images of potential products for sale. Previous solutions displayed static images or videos that may be rarely (or never) updated and / or are costly to deploy and render. Consequently, at least a portion of the user interface (UI) for a given application or action becomes outdated, occupying space that could otherwise be used to please or otherwise engage the user. Adding effects to at least a portion of that space can improve the user experience and / or draw the user's attention to a specific area, thereby increasing engagement and / or usage of the service or storefront.

[0034] The embodiments can be used to generate various types of content, such as immersive content generated using different static images. The content can be user-specific (e.g., based on user preferences, user-provided, etc.) and / or general (e.g., content- or platform-based). In at least one embodiment, static images are made interactive by using one or more generative artificial intelligence (AI) techniques and then providing the generated content for rendering on a client device. The content can be personalized for a given user, for example, by using user-provided images or options such as integrating the user into a gamified environment. Furthermore, the embodiments can be used to combine immersive content with informative content (e.g., loading bars or expected wait times) to further enhance the user experience. Interactivity can also be provided by offering users input options with the generated content (e.g., tracking input actions). Therefore, users can have an improved waiting experience.

[0035] It will be apparent to those skilled in the art that, based on the teachings and suggestions contained herein, various other such functions may also be used within the scope of the various embodiments.

[0036] Figure 1 illustrates an example environment 100 that can be used with embodiments of this disclosure. In this example, user equipment 102 can request use of one or more resources 104 and / or access to content in content data storage 106 from provider environment 108. For example, user equipment 102 can be an agent and / or automated workflow for one or more users that prompts or directs user equipment 102 to submit requests to provider environment 108 through one or more networks. In at least one embodiment, user equipment 102 can be any suitable computing or processing device, such as a desktop or laptop computer, smartphone, tablet, wearable computer (e.g., smartwatch, glasses, contact lenses, headphones, etc.), server, or other such systems or devices. Additionally, one or more networks can include any suitable network, such as the Internet, local area network (LAN), cellular network, Ethernet, or other such wired and / or wireless networks.

[0037] In at least one embodiment, provider environment 108 may grant access to one or more resources 104 and / or content in response to determining that user device 102 is authorized to access resources 104 and / or content. For example, provider environment 108 may be associated with a distributed computing environment (e.g., a “cloud”) that allows users to access diverse resources and / or content, which may include resources or content for remotely executing one or more applications from the user device itself. For example, resource 104 may include computing resources, such as a central processing unit (CPU) and / or a graphics processing unit (GPU), which may include hardware with greater capabilities than the hardware associated with user device 102. By using resource 104 of provider environment 108, the user of user device 102 can gain access to a wider variety of content and / or applications due to the enhanced processing power of resource 104.

[0038] As a non-limiting example, provider environment 108 could be associated with a cloud gaming or entertainment service, such as Nvidia's GeForce NOW. Provider environment 108 allows users (e.g., through a subscription service) to access high-performance resources using one or more network connections. Therefore, users do not need to purchase resources themselves, but can efficiently rent or otherwise access resource 104 using provider environment 108. When a user submits a request, manager 110 can determine the authorization or access level of the requesting user, for example, by querying account data storage 112 to verify credentials, access restrictions, and / or etc. For example, users can be granted access to multiple "tiers" or levels, some of which offer higher-performance resources, greater content library access, reduced latency, and / or combinations thereof. After verifying the access level, manager 110 can submit one or more calls to configuration engine 114 to configure resource 104 in response to the request. For example, resource 104 could include one or more GPUs to run a gaming application using content from content data storage 106.

[0039] In operation, provider environment 108 can be used to serve hundreds or thousands of users simultaneously or semi-simultaneously. As a result, delays or latency may occur associated with verifying account access permissions, configuring resources, and / or preparing applications for execution on the configured resources. Furthermore, a user's tier or level may cause a waiting period for that user. For example, a user may submit a request to execute an application with high processing demands but may only have access to lower-level hardware, resulting in a delay or latency in the request. Typically, a loading screen or waiting screen may be presented to the user during the waiting period. As described herein, loading screens may contain static images or repetitive videos that are rarely updated, thus occupying screen space without providing value to the user. Embodiments of this disclosure address and overcome these problems by deploying one or more content generation engines 116 that generate an animated set of images from one or more static images, which can be used to provide a more immersive experience for the user during the loading period. In at least one embodiment, one or more content generation engines 116 may receive one or more static images (e.g., from content data storage area 106), extract foreground and background portions, perform image inpainting, and then generate one or more animations on user device 102. In this way, the user loading time can be more immersive compared to conventional static or repetitive images.

[0040] Figure 1BAn example environment 120 is illustrated that can be used with embodiments of this disclosure. In this example, one or more features of a content generation engine 116 are illustrated, which may include one or more trained machine learning systems, such as neural networks for object recognition, object extraction, segmentation, masking, inpainting, outpainting, and / or combinations thereof. Systems and methods may include one or more content generation pipelines that can be used to generate one or more images from an initial input image and then produce an output configuration that can be used to generate effects or animations to be rendered on one or more client devices. As described herein, systems and methods may be designed to pre-generate output configurations or output configurations generated in response to one or more user inputs or requests associated with a provider environment.

[0041] The content generation pipeline may include one or more trained machine learning systems and may also generate one or more intermediate images from a first input or base image, among other options. In at least one embodiment, the generated intermediate images may be provided as part of an output configuration or may be used to generate one or more additional images. As an example, the pipeline may include input images provided to the content generation engine 116. Input images may be high-definition images associated with content elements, user-selected images, randomly selected images, and / or combinations thereof. For example, randomly selected high-definition images may be used to generate different types of output animations for different content. While high-definition images are described as an example, embodiments may also be used with standard-definition projects, with frames extracted from video, with models, and / or combinations thereof. In at least one embodiment, a processing engine 122 (e.g., a preprocessing engine) may be used to receive and prepare input images for use within the pipeline, for example by validating image formats, cropping or rotating images, adjusting one or more parameters of the image, and / or combinations thereof. The processing engine 122 may include various preprocessing steps to prepare the image according to one or more settings (e.g., desired output size, desired resolution, and / or combinations thereof).

[0042] Extraction engine 124 can be used to extract one or more regions of an image, such as content elements or foreground regions from background regions. For example, one or more deep learning or instance / semantic segmentation networks can be used to identify and extract foreground regions of an image, which can be based on depth information, continuous object detection, specific object recognition, and / or combinations thereof. The extracted foreground portion can then be saved as a new image, for example, an image containing only the extracted foreground portion. Furthermore, in various embodiments, additional objects associated with the foreground object can also be extracted. As an example, in a football match scene, a player may be running on the field and extracted as a foreground object, while the associated object may correspond to the football being passed to that player. Thus, each of these objects can be identified and extracted as one or both of the foreground image and / or the associated object image, which can be used to animate waiting screens or otherwise provide a more immersive experience, as described herein.

[0043] Embodiments can further process the image by generating a mask corresponding to the extracted foreground region, for example, using a masking engine 126. In some embodiments, the masking engine 126 and the extraction engine 124 may use the same neural network architecture and / or may be different outputs generated by a common network. However, in other embodiments, different networks may be used. In some embodiments, the mask may be converted into a monochrome image.

[0044] One or more embodiments may also include an image inpainting engine 128, which may be a prompt-based trained machine learning system, such as a diffusion model, for generating content within masked regions. In some embodiments, the prompt is at least partially based on the context of the input image. For example, if the input image is related to a first-person shooter game, the prompt information may include the game's context, the game setting's environment (e.g., outer space, World War II, etc.), and / or user preferences. The prompt may also be based on features of the extracted foreground, such as, for example, objects identified in the foreground and subsequently used to formulate the prompt for the diffusion model. If the foreground is used to extract a football player, the prompt may include an identifier of the sport (e.g., football) associated with the content, which may then inform the diffusion model to generate content that can be used for image inpainting of one or more regions (e.g., within masked regions). In at least one embodiment, the prompt may be automatically generated using one or more machine learning systems, such as receiving an input image and / or the extracted foreground and then generating a VLM describing the image content. Furthermore, the prompt may be tailored for a specific use case and / or image context or otherwise designed. For example, cues for sports games might follow one format, while cues for first-person shooter games might follow another, and cues for content elements in a storefront might follow different formats based on their type. By performing image inpainting within the masked area, a new image can be generated that corresponds to a new background image. For example, image inpainting can be used to replace pixels that were previously "covered" or otherwise set to represent an extracted foreground image. Thus, the initial input image can be used to generate two output images at that point in the pipeline, corresponding to the foreground (e.g., the extracted portion) and the background (e.g., the background with the image-inpainted masked area).

[0045] Various embodiments may include an image expansion engine 130, which may also be a prompt-based trained machine learning system, such as a diffusion model. In at least one embodiment, each of the image inpainting engine 128 and the image expansion engine 130 may share different layers or architectures, or may be a generic model. As described herein, image expansion may refer to generating a larger canvas or size for an image and may include adding pixels to boundary regions of the input image. The output engine 130 may also receive prompts to guide the diffusion model or other content generation model to form another new output image. Thus, this example pipeline may result in a total of three output images (e.g., foreground, background, and a new, larger canvas), but various embodiments may include other images, and the three output images are provided only as a non-limiting example.

[0046] The image can then be provided to output content generator 132, which can prepare one or more output configurations for rendering by an end user on a client device. For example, one or more animation effects can be applied to the image (e.g., an image generated by the pipeline) to provide a more immersive effect and environment than a static image. For example, effects such as parallax, scaling, panning, and / or combinations thereof can be used to prepare the output configuration for use and rendering on the client device. In this way, the initial input image can be processed through the pipeline to generate immersive, animated output configurations. Output configuration 132 can be provided as JSON or other instruction sets, which can identify specific effects, effect parameters, and / or identify the image used for the effect. Various different effects can be generated using the same image; for example, one effect is zooming, while others are panning through. One or more content generation pipelines can include instructions for generating different types of output configurations, which can be based on attributes of the image's purpose, the application's context, and / or combinations thereof.

[0047] The systems and methods disclosed herein can be used to generate immersive content from one or more initial static images associated with content elements (e.g., frames from a game), among other options. The systems and methods can process the original input image, extract the foreground (e.g., one or more objects within the image, portions from the foreground of the image, one or more related objects, etc.), inpaint the background area associated with the mask, output a larger canvas, and then merge the foreground and background. Even starting with only static images, this approach allows the foreground to be animated separately from the background to provide an immersive 3D effect. Therefore, various embodiments can use one or more trained machine learning systems (e.g., content generation models based on generative AI) to create immersive content displays, including animation mechanisms applied to the original static content. The embodiments address the problem of solid-color background images, which, for example, lack immersion and / or fail to enhance the user's experience of a product or service during the waiting or loading period when a user requests content or resources. While video may be considered more immersive than images, animated video is performance-intensive and may be difficult to run on client devices with insufficient hardware capabilities at high resolutions. Some environments may be designed to run on thin clients, such as when using distributed services that utilize other hardware resources to perform tasks, where video decoding can be overly resource-intensive. Some implementations address this problem by using a limited or targeted set of static images (e.g., two or more) combined with one or more animation effects to provide an immersive, video-like appearance during the waiting period without the overhead associated with video decoding.

[0048] Various embodiments provide a workflow or pipeline for generating immersive content with 3D effects from static images. As input to the workflow, one or more images may be received in association with a content request, such as a "hero image" from a game, an image of a product for sale in a market, an image selected by a user, an image captured during user activity associated with the content or resource, and / or a combination thereof. Foreground / background extraction tools, possibly trained on one or more machine learning systems, may be included to separate the main object (e.g., the foreground portion) from the background of the image. The tools may also be used to identify and separate objects associated with the main object. An example extraction tool may include an Inverse Saliency Pyramid Reconstruction Network (InSPyReNet), a salient object detection framework that may be based on a Res2Net or Swin Transformer backbone. The generated foreground object can be used to generate a mask, and then, using image inpainting cues, embodiments may use one or more generative AI workflows to fill the masked region. As an example, a diffusion network may be used to generate the filled background image. The same or different models may also be used to perform the image expansion step using image expansion cues. The generated image can then be treated as a separate layer, generating an output configuration for rendering on the client device. This output configuration can include one or more effects, such as parallax, geometric 3D, panning, scaling, and / or similar effects. Similarly, multiple output configurations can be generated and then played during a waiting period, such as looping or random / semi-random selection. This allows for a more immersive user experience during the waiting period.

[0049] Figures 2A-2F An example sequence of pipeline 200 that can be used with embodiments of this disclosure is shown. In this example, the sequence may include, for example, the steps of: acquiring an image, extracting the foreground, generating a mask, performing image inpainting on the mask, expanding the background of the image, and then generating an output configuration including layers for animate the foreground relative to the background. For this pipeline and other pipelines, the steps may be more or fewer, and other images may be used and / or generated within the scope of this disclosure.

[0050] Figure 2AA pipeline section is shown, which, for example, retrieves image 202 from content data storage 106 in response to a request. This request may be provided by one or more client devices (and by their extensions, one or more users), and / or may be part of an automated workflow. For example, if the content is associated with video games, several popular games may be identified, and different animation configurations may be generated and stored for a subset of the list. As another example, content data storage 106 may include content provided by game publishers, such as "hero images" or high-definition images. Furthermore, content data storage 106 may include images and / or videos associated with users of the content, which can be used to generate different immersive animations for use during waiting screens. As described herein, different embodiments may process images in real-time or near real-time, and / or may process images offline and then store one or more images and / or intermediate images.

[0051] Image 202 in this example contains a frame from a video game, including character 204 within scene 206. Because character 204 may be the user's primary focus or the most important part of the scene, character 204 can be considered the "foreground" of scene 206. In some embodiments, image 202 may be processed with one or more preprocessing steps, such as steps to enhance the color or resolution of image 202.

[0052] Pipeline 200 Figure 2B The process continues by providing image 202 to extraction engine 124. As described herein, extraction engine 124 can be used to identify object 204 within scene 206, and then extract object 204 (which may correspond to the foreground) to generate foreground image 208. Foreground image 208 may be based at least in part on connected components associated with object 204. In at least one embodiment, other objects may also be associated with or otherwise combined with object 204, for example, if object 204 is holding a weapon. In some embodiments, object 204 may be automatically detected in scene 206. However, in other embodiments, object 204 may be specified or otherwise prompted, for example, if image 202 contains depth information or other distinguishing information associated with the foreground. The generated foreground image 208 may be saved or otherwise stored for use within pipeline 200. Furthermore, foreground image 208 may be saved separately for use by one or more other downstream processes.

[0053] Figure 2CThe masking process is illustrated, wherein the foreground image 208 is processed by the masking engine 126 to form a masked image 210 containing the masked region 212 corresponding to the object 204. In some embodiments, a transformation step may also be included to generate the masked image 210. In this way, regions related to the foreground can be identified in the initial image, and as... Figure 2D As shown, this area can be used for image inpainting of the background, thereby adding additional information to scene 206, for example, using one or more generative AI systems. In this example, the masked image 210 and the cue are provided to the image inpainting engine 128 to generate a background image 214. The background image 214 may correspond to the background of the original image 202 and includes an image inpainting region 216 corresponding to the masked region 212. In other words, the image inpainting engine 128 is used to fill in the content that is now missing due to the removal of the object 204 associated with the masked region 212.

[0054] Figure 2E Another portion of pipeline 200 is shown, in which image expansion engine 130 uses one or more images to generate canvas image 218. In this example, canvas image 218 may be generated using background image 214 or one or both of images 202. Additionally, a variety of other images may be used within the scope of this disclosure. Furthermore, prompts may be used to generate canvas image 218, which may be prompts provided to one or more expansion models, as described herein. In at least one embodiment, the prompt is associated with the context or theme of image 202, such as “first-person shooter game” or “football match” or “brand waiting screen”, and various other options. Prompts and image inputs may be used to generate canvas image 218, which may include additional pixels at different edges. Canvas image 218 shows added regions 220 and 222, but it should be understood that there may be more or fewer added regions 220 and 222, for example, regions added along the top or bottom of canvas image 218. Compared to the initial image, more content is provided in the added regions 220 and 222, which can be used when scaling actions or other effects are added to the configuration discussed in this article.

[0055] Figure 2FThe generation of output configuration 224 using output content generation engine 132 is illustrated. In this example, a set of images 226 may include some or all of image 202, foreground image 206, background image 214, and / or canvas image 218. Output content generation engine 126 can use these images to apply a layered set of images as part of output configuration 224, thereby allowing different areas or layers to move relative to other areas or layers to provide animation while using static images. In this example, canvas image 218 provides the bottom layer, followed by background image 214 and foreground image 206. These images may be stacked or otherwise grouped within a target configuration, which may be a JSON configuration that can be provided to a user device for rendering on that device. In at least one embodiment, the JSON configuration may identify the target effect (including annotations), provide layer identifiers to select the desired images for the effect, and establish layer or effect parameters (e.g., layer offset, orientation, duration, etc.). In this way, users can have a more immersive experience while waiting at a waiting screen, while also reducing the computational and bandwidth usage associated with video or processing-intensive waiting screen images or content. Furthermore, embodiments of this disclosure may also overlay additional information as other layers on the various images, such as countdowns or other indicators for the user.

[0056] The implementation can also provide an interactive experience associated with the configuration. For example, one or more properties of the output configuration can be adjusted based on user interaction during rendering. Interactions can take the form of user-provided input, such as mouse input, and can include modifying the speed of animations, pausing interaction, triggering animations, and / or combinations thereof. As another example, input can be received as instructions to pan or rotate the output configuration. As a non-limiting example, moving the mouse to the left may cause the animation to rotate to the left, moving the mouse to the right may cause the animation to rotate to the right, and so on. Furthermore, scrolling or other actions can be received as commands to zoom in or out or pan. In this way, a more immersive experience can be provided because the user can control or otherwise influence the animated content on the waiting screen.

[0057] One or more embodiments may also use this configuration to enable mini-games or activities for waiting users. For example, multiple layers may be used to generate the output configuration, which may include layers for the main object in the scene and layers for related objects. In at least one embodiment, user input may be used to control the movement of the main object or related objects, such as controlling parallax effects or panning to align the related object with a target position, and other options. Thus, the system and method can provide engaging activities for users while they wait, thereby increasing user enjoyment when they would otherwise only be able to watch static images while waiting in previous implementations.

[0058] Figure 3 An example call diagram 300 is shown that can be used with embodiments of this disclosure. In this example, a series of calls, actions, and / or responses may represent the generation of one or more immersive animated content elements that can be provided to a user during a waiting period (e.g., a waiting period for an application or resource), among other options. In this example, user device 102 may use one or more applications that can generate content 302, which may be stored in one or more content data storage areas 106. For example, user device 102 may launch and play a video game and perform actions within the game, which may be saved or otherwise recorded. User device 102 may be used to establish one or more settings for recording or otherwise saving content, which may include saving content in response to commands, saving content periodically, or not saving content, among other options and combinations thereof. User device 102 may allow access to content for the generation of additional supplementary content, as discussed herein.

[0059] In this example, manager 110 can be used to transmit instructions for generating content that can be used during a waiting period by submitting instruction 304 to content generation engine 116. Instruction 304 may include commands or requests for generating specific types of supplementary content, which may include parameters associated with the generated content. For example, instructions may include specific related content elements, required length, required output resolution, and / or combinations thereof. Furthermore, the request may be provided offline (e.g., before the waiting period), allowing content to be generated and stored for later use based on requests from user devices. That is, a pre-made set of content can be generated and then provided upon user request, which can save computational resources and / or reduce latency compared to generating content instantly or in real-time / near real-time. In this example, content generation engine 116 responds to instructions received from manager 110, requesting 306 content from one or more content data storage areas 106. For example, one or more content data storage areas 106 may contain “hero images” from video games, user-generated content, images of products for sale in the market, and / or combinations of the above. Content generation engine 116 may then receive 308 content and perform one or more content generation operations 310, which, as described herein, may include generating an output configuration. The output configuration may then be transferred 312 to one or more content data storage areas 106. In at least one embodiment, content generation engine 116 may return information 314 about a completed request to manager 110. Instead of providing content to one or more content data storage areas 106, or as an addition to providing content to one or more content data storage areas 106, as discussed herein, content generation engine 116 may also provide content to user device 102 as content is generated in real-time or near real-time.

[0060] In at least one embodiment, user device 102 may submit a request 316 to manager 110 for access to one or more resources. For example, these resources could be computing resources used to execute one or more applications (e.g., video games). As another example, the request could direct access to a login page for an online marketplace to purchase one or more goods (e.g., physical or electronic goods). Manager 110 may query 318 account data storage 112 to obtain 320 credentials to determine the level of access permitted to user device 102. Once it is determined that user device 102 is authorized to execute request 316, manager 110 may send instructions 322 to configuration engine 114 to configure / deploy 324 one or more resources and / or applications.

[0061] As described herein, there may be waiting periods associated with the startup of resources and / or applications. For example, a user with a lower “level” in a distributed service may wait longer than other users with higher levels. Furthermore, in some embodiments, there may be additional waiting times if a user requests a resource-intensive application. In some embodiments, while the user waits for resource deployment to complete, manager 110 may request 326 one or more output content configurations from one or more content data stores 106. Request 326 may include specific selections of output configurations, such as those specific to a particular application or associated with a particular resource. Additionally, if it is determined that configuration / deployment 324 may exceed a certain threshold amount of time, request 326 may include multiple content requests. Upon receiving 328 of the output content configuration, manager 110 may provide 330 of the output content configuration to user device 110, which may continue rendering the output content configuration until configuration / deployment 324 is complete. In this way, the user can be served immersive content while waiting for resource deployment.

[0062] Figure 4A An example flowchart of an example process 400 for generating animated content using static images is shown. It should be understood that, with respect to this and other processes presented herein, unless explicitly stated otherwise, additional, fewer, or alternative operations may be performed in a similar or alternative order or at least partially in parallel within the scope of the various embodiments. In this example, a first image corresponding to the foreground of input image 402 is generated. This first image may include an image in which one or more foreground objects are extracted from the input image, for example, using one or more machine learning systems for object recognition, segmentation, and / or extraction. A second image corresponding to a first background of input image 404 may be generated. For example, the first background may correspond to the original image in which the foreground has not been extracted. As another example, the first background may correspond to the original image in which image inpainting has been performed on the regions associated with the foreground using one or more models.

[0063] In at least one embodiment, a third image corresponding to a second background of the input image 406 can be generated. The second background may be associated with one or more image expansion processes, such as an image in which background pixels have been increased compared to the original input image. Animated effects can be generated using the first image and at least one of the second or third image 408. For example, an animated effect may include translating or scaling one or all of the images used to create the effect, thereby creating an animated appearance without using the computational resources of the decoded video.

[0064] Figure 4BAn example flowchart is shown for a sample process 420 for providing content configurations for rendering animated effects on a user device. In this example, a request to access one or more resources associated with a distributed computing environment 422 is received. For example, a user may submit a request to access certain content or use a resource to execute one or more applications, among other options. A latency period for configuration and / or deployment of one or more resources can be determined, and it can be determined that the latency period exceeds a threshold 424. For example, a user may have a “tier” or access level associated with a resource, and for some tiers, the latency period may be long enough that it may be desirable to provide the user with some immersive content to compensate for the latency period, thereby providing an improved experience. One or more content configurations can be selected from a content configuration database 426. Content configurations can be selected at least in part based on one or more attributes of the request. For example, if a user requests access to a specific video game, one or more content configurations may be associated with that video game. As another example, if a user has a specific state, content configurations of increased variety or complexity may be available. One or more content configurations can then be provided for rendering on the requesting user device 482. In at least one embodiment, the content configuration may identify one or more images and a set of associated parameters for applying animation effects. Therefore, the user device can be used to render immersive content while the user awaits resource deployment for use.

[0065] Figure 4C An example flowchart of an example process 440 for generating animated content using static images is shown. In this example, a first image corresponding to the foreground of input image 442 is generated. The first image may include an image in which one or more foreground objects are extracted from the input image, for example, using one or more machine learning systems for object recognition, segmentation, and / or extraction. One or more second images corresponding to the background of input image 444 may be generated. For example, the background may correspond to the original image where the foreground has not been extracted. As another example, the background may correspond to the original image where the region associated with the foreground has been inpainted using one or more models. As a further example, one or more second images may also be associated with an image generated using one or more image expansion processes, for example, an image in which background pixels have been increased compared to the original input image. In at least one embodiment, an animated effect generated using at least one of the first image and one or more second images may be shown at 446. For example, the animated effect may include translating or scaling one or all of the images used to create the effect, thereby creating the appearance of animation without using the computational resources of the decoded video.

[0066] Figure 5An example flowchart of a sample process 500 for generating content configurations for rendering animated effects on a user device is shown. In this example, extraction 502 identifies one or more foreground components within an input image. The foreground components may be associated with one or more objects in the input image, which can be identified using one or more operations (e.g., object recognition, segmentation, and / or combinations thereof). A first image containing at least a portion of one or more foreground components 504 can be generated. Furthermore, a mask region associated with one or more foreground components 506 can be determined. For example, a mask can be generated to provide a mask region within the input image where one or more foreground components 506 have pixel values.

[0067] In at least one embodiment, one or more replacement pixels can be generated for mask region 508. The one or more replacement pixels may be associated with a generative AI system (e.g., a diffusion model) that can generate one or more replacement pixels using input prompts. At least one second image can be generated that includes one or more replacement pixels replacing mask region 510. For example, a new background image can be generated that includes a background from the original input image but replaces one or more foreground components with the generated pixels. Generation 512 can then configure the effect to be performed using a specific second image from the first image and at least one second image. For example, this configuration may correspond to a JSON configuration that provides the desired effect, identification information for the first and second images, attributes of the effect, etc.

[0068] As described above, aspects of the various methods presented herein can be lightweight enough to be executed in real time on devices such as client devices, like personal computers or game consoles. This processing can be performed on or for content generated on or received by the client device or received from an external source, such as streaming data or other content received via at least one network. In some cases, the processing and / or determination of this content can be performed by one of these other devices, systems, or entities and then provided to the client device (or another such receiver) for presentation or another such purpose.

[0069] As an example, Figure 6An example network configuration 600 is shown, which can be used to provide, generate, modify, encode, process, and / or transmit image data or other such content. In at least one embodiment, client device 602 can use components of control application 604 on client device 602 and data locally stored on the client device to generate or receive session data. In at least one embodiment, content application 624 executing on server 620 (e.g., cloud server or edge server) can initiate a session associated with at least one client device 602, can utilize a session manager and user data stored in user database 636, and can cause content (e.g., one or more digital assets (e.g., object representations)) from asset repository 634 to be determined by content manager 626. Content manager 626 can work with image compositing module 628 to generate or composite new objects, digital assets, or other such content for presentation via client device 602. In at least one embodiment, image compositing module 628 can use one or more neural network or machine learning models that can be trained or updated using training module 632 or a system located on or communicating with server 620. This may include training and / or using diffusion model 630 to generate content tiles 628 that can be used by the image compositing module, for example, applying non-repeating textures to areas of the environment to be rendered via client device 602 for image or video data. At least a portion of the generated content may be transmitted to client device 602 using a suitable transmission manager 622 for transmission via download, streaming, or another such transmission channel. An encoder may be used to encode and / or compress this data before transmitting at least a portion of it to client device 602. In at least one embodiment, client device 602 receiving such content may provide it to a corresponding control application 604, which may also or alternatively include a graphical user interface 610, a content manager 612, and an image compositing or diffusion module 614 for providing, compositing, modifying, or using content on or rendered by client device 602 (or for other purposes). The decoder can also be used to decode data received via network 640 for presentation via client device 602, such as image or video content presented via display 606 and audio (e.g., sound and music) presented via at least one audio playback device 608 (e.g., speaker or headphones). In at least one embodiment, at least some of the content may already be stored on client device 602, rendered on client device 602, or accessible to client device 602, such that at least that portion of the content does not need to be transmitted via network 640, for example, where the content may have been previously downloaded or stored locally on a hard disk or optical disc.In at least one embodiment, the content may be transferred from server 620 or user database 636 to client device 602 using a transport mechanism (e.g., data streaming). In at least one embodiment, at least a portion of the content may be obtained, enhanced, and / or streamed from another source (e.g., third-party service 660 or other client device 650), which may also include a content application 662 for generating, enhancing, or providing content. In at least one embodiment, a portion of this functionality may be performed using multiple computing devices or multiple processors within one or more computing devices (e.g., a combination of CPU and GPU).

[0070] In this example, these client devices can include any suitable computing device, such as desktop computers, laptops, set-top boxes, streaming devices, game consoles, smartphones, tablets, VR headsets, AR goggles, wearable computers, or smart TVs. Each client device can submit requests across at least one wired or wireless network, which can include the Internet, Ethernet, a local area network (LAN), or a cellular network, among other such options. In this example, these requests can be submitted to an address associated with a cloud provider that operates or controls one or more electronic resources within a cloud provider environment, such as a data center or server cluster. In at least one embodiment, the request can be received or processed by at least one edge server located at the network edge and outside at least one security layer associated with the cloud provider environment. In this way, latency can be reduced by enabling client devices to interact with servers in closer proximity, while also improving the security of resources within the cloud provider environment.

[0071] In at least one embodiment, such a system can be used to perform graphics rendering operations. In other embodiments, such a system can be used for other purposes, such as providing image or video content to test or validate autonomous machine applications, or for performing deep learning operations. In at least one embodiment, such a system can be implemented using edge devices, or can be combined with one or more virtual machines (VMs). In at least one embodiment, such a system can be implemented at least partially in a data center or at least partially using cloud computing resources.

[0072] Reasoning and training logic

[0073] Figure 7A Inference and / or training logic 715 is shown for performing inference and / or training operations associated with one or more embodiments. The following is in conjunction with... Figure 7A and / or Figure 7B Provide details about reasoning and / or training logic 715.

[0074] In at least one embodiment, inference and / or training logic 715 may include, but is not limited to, code and / or data storage 701 for storing forward and / or output weights and / or input / output data, and / or other parameters configuring neurons or layers of a neural network trained for and / or used for inference in one or more embodiments. In at least one embodiment, training logic 715 may include or be coupled to code and / or data storage 701 for storing graph code or other software to control timing and / or sequence, wherein weight and / or other parameter information is loaded to configure logic, including integer and / or floating-point units (collectively, arithmetic logic units (ALUs)). In at least one embodiment, code (such as graph code) loads weight or other parameter information into the processor ALU based on the architecture of the neural network to which the code corresponds. In at least one embodiment, code and / or data storage 701 stores weight parameters and / or input / output data of each layer of a neural network trained or used in one or more embodiments during forward propagation of input / output data and / or weight parameters during training and / or inference using one or more embodiments. In at least one embodiment, any portion of the code and / or data storage 701 may be included within other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory.

[0075] In at least one embodiment, any portion of the code and / or data storage 701 may be internal or external to one or more processors or other hardware logic devices or circuits. In at least one embodiment, the code and / or data storage 701 may be a cache memory, dynamic random access memory (“DRAM”), static random access memory (“SRAM”), non-volatile memory (e.g., flash memory), or other storage. In at least one embodiment, the choice of whether the code and / or data storage 701 is internal or external to the processor, for example, or composed of DRAM, SRAM, flash memory, or some other storage type, may depend on the available on-chip or off-chip storage space, the latency requirements of the training and / or inference functions being performed, the batch size of the data used in the inference and / or training of the neural network, or some combination of these factors.

[0076] In at least one embodiment, inference and / or training logic 715 may include, but is not limited to, code and / or data storage 705 for storing backpropagation and / or output weights and / or input / output data corresponding to neurons or layers of a neural network trained and / or used for inference in one or more embodiments. In at least one embodiment, during training and / or inference using one or more embodiments, code and / or data storage 705 stores weight parameters and / or input / output data for each layer of a neural network trained or used in one or more embodiments during backpropagation of input / output data and / or weight parameters. In at least one embodiment, training logic 715 may include or be coupled to code and / or data storage 705 for storing graph code or other software to control timing and / or sequence, wherein weight and / or other parameter information is loaded to configure logic including integer and / or floating-point units (collectively, an arithmetic logic unit (ALU)). In at least one embodiment, code (such as graph code) loads weight or other parameter information into the processor ALU based on the architecture of the neural network to which the code corresponds. In at least one embodiment, any portion of the code and / or data storage 705 may be included together with other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory. In at least one embodiment, any portion of the code and / or data storage 705 may be internal or external to one or more processors or other hardware logic devices or circuits. In at least one embodiment, the code and / or data storage 705 may be cache memory, DRAM, SRAM, non-volatile memory (e.g., flash memory), or other storage. In at least one embodiment, the choice between the code and / or data storage 705 being internal or external to the processor, for example, whether it consists of DRAM, SRAM, flash memory, or some other type of storage, depends on whether the available storage is on-chip or off-chip, the latency requirements of the training and / or inference functions being performed, the data batch size used in the inference and / or training of the neural network, or some combination of these factors.

[0077] In at least one embodiment, code and / or data storage 701 and code and / or data storage 705 may be separate storage structures. In at least one embodiment, code and / or data storage 701 and code and / or data storage 705 may be the same storage structure. In at least one embodiment, code and / or data storage 701 and code and / or data storage 705 may be partially identical and partially separate storage structures. In at least one embodiment, any portion of code and / or data storage 701 and code and / or data storage 705 may be included with other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory.

[0078] In at least one embodiment, the inference and / or training logic 715 may include, but is not limited to, one or more arithmetic logic units (“ALUs”) 710 (including integer and / or floating-point units) for performing logical and / or mathematical operations at least in part based on or instructed by training and / or inference code (e.g., graph code), the results of which may produce activations (e.g., output values ​​from layers or neurons within a neural network) stored in activation storage 720, which are functions of input / output and / or weight parameter data stored in code and / or data storage 701 and / or code and / or data storage 705. In at least one embodiment, activation is activated in response to execution instructions or other code, and linear algebraic and / or matrix-based mathematical generation performed by ALU 710 is stored in activation storage 720, wherein weight values ​​stored in code and / or data storage 705 and / or code and / or data storage 701 are used as operands with other values, such as bias values, gradient information, momentum values, or other parameters or hyperparameters, and any or all of these can be stored in code and / or data storage 705 or code and / or data storage 701 or other on-chip or off-chip storage.

[0079] In at least one embodiment, one or more processors or other hardware logic devices or circuits include one or more ALUs 710, while in another embodiment, one or more ALUs 710 may be located outside the processor or other hardware logic device or the circuitry using them (e.g., a coprocessor). In at least one embodiment, one or more ALUs 710 may be included within an execution unit of a processor, or otherwise included in a group of ALUs accessible by the execution unit of the processor, which may be within the same processor or distributed among different processors of different types (e.g., a central processing unit, a graphics processing unit, a fixed-function unit, etc.). In at least one embodiment, code and / or data storage 701, code and / or data storage 705, and activation storage 720 may be on the same processor or other hardware logic device or circuitry, while in another embodiment, they may be on different processors or other hardware logic devices or circuitries, or in some combination of the same and different processors or other hardware logic devices or circuitries. In at least one embodiment, any portion of activation storage 720 may be included together with other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory. Furthermore, inference and / or training code may be stored together with other code accessible to the processor or other hardware logic or circuitry, and may be retrieved and / or processed using the processor’s fetch, decode, schedule, execute, exit, and / or other logic circuitry.

[0080] In at least one embodiment, the active memory 720 may be a cache memory, DRAM, SRAM, non-volatile memory (e.g., flash memory), or other memory. In at least one embodiment, the active memory 720 may be wholly or partially located inside or outside one or more processors or other logic circuits. In at least one embodiment, the choice of whether the active memory 720 is internal to or external to the processor may depend on the available on-chip or off-chip storage, the latency requirements for training and / or inference functions, the batch size of data used in inference and / or training the neural network, or some combination of these factors. For example, it may include DRAM, SRAM, flash memory, or other memory types. In at least one embodiment, Figure 7A The inference and / or training logic 715 shown can be used in conjunction with an application-specific integrated circuit (“ASIC”), such as those from Google. Processing unit, from Graphcore TM Inference processing unit (IPU) or from Intel Corp. (e.g., "LakeCrest") processor. In at least one embodiment, Figure 7A The inference and / or training logic 715 shown can be used in conjunction with central processing unit (“CPU”) hardware, graphics processing unit (“GPU”) hardware, or other hardware such as field programmable gate array (“FPGA”)

[0081] Figure 7B Inference and / or training logic 715 according to at least one or more embodiments is illustrated. In at least one embodiment, the inference and / or training logic 715 may include, but is not limited to, hardware logic, wherein computational resources are dedicated or otherwise uniquely used in conjunction with weight values ​​or other information corresponding to one or more layers of neurons within a neural network. In at least one embodiment, Figure 7B The inference and / or training logic 715 shown can be used in conjunction with an application-specific integrated circuit (ASIC), such as those from Google. Processing unit, from Graphcore TM Inference processing unit (IPU) or from Intel Corp. (e.g., "LakeCrest") processor. In at least one embodiment, Figure 7BThe inference and / or training logic 715 shown can be used in conjunction with central processing unit (CPU) hardware, graphics processing unit (GPU) hardware, or other hardware (e.g., field-programmable gate array (FPGA)). In at least one embodiment, the inference and / or training logic 715 includes, but is not limited to, code and / or data storage 701 and code and / or data storage 705, which can be used to store code (e.g., graph code), weight values, and / or other information, including bias values, gradient information, momentum values, and / or other parameter or hyperparameter information. Figure 7B In at least one embodiment shown, each of code and / or data storage 701 and code and / or data storage 705 is associated with dedicated computing resources (e.g., computing hardware 702 and computing hardware 706), respectively. In at least one embodiment, each of computing hardware 702 and computing hardware 706 includes one or more ALUs that perform mathematical functions (e.g., linear algebraic functions) only on the information stored in code and / or data storage 701 and code and / or data storage 705, respectively, and the results of the function execution are stored in activation storage 720.

[0082] In at least one embodiment, each of the code and / or data storage 701 and 705 and the corresponding computing hardware 702 and 706 corresponds to a different layer of the neural network, such that activation obtained from one “store / computation pair 701 / 702” of the code and / or data storage 701 and computing hardware 702 provides input as input to the next “store / computation pair 705 / 706” of the code and / or data storage 705 and computing hardware 706, in order to reflect the conceptual organization of the neural network. In at least one embodiment, each store / computation pair 701 / 702 and 705 / 706 may correspond to more than one neural network layer. In at least one embodiment, additional store / computation pairs (not shown) may be included in the inference and / or training logic 715 after or in parallel with the store / computation pairs 701 / 702 and 705 / 706.

[0083] Data Center

[0084] Figure 8 An example data center 800 that can be used with at least one embodiment is shown. In at least one embodiment, the data center 800 includes a data center infrastructure layer 810, a framework layer 820, a software layer 830, and an application layer 840.

[0085] In at least one embodiment, such as Figure 8As shown, the data center infrastructure layer 810 may include a resource coordinator 812, grouped computing resources 814, and node computing resources (“nodes CR”) 816(1)-816(N), where “N” represents any positive integer. In at least one embodiment, nodes CR 816(1)-816(N) may include, but are not limited to, any number of central processing units (“CPUs”) or other processors (including accelerators, field-programmable gate arrays (FPGAs), graphics processors, etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid-state drives or disk drives), network input / output (“NWI / O”) devices, network switches, virtual machines (“VMs”), power modules, and cooling modules, etc. In at least one embodiment, one or more nodes CR 816(1)-816(N) may be servers having one or more of the aforementioned computing resources.

[0086] In at least one embodiment, the grouped computing resources 814 may include individual groups (not shown) of node CRs housed in one or more racks, or a plurality of racks (also not shown) housed in data centers in various geographic locations. Individual groups of node CRs within the grouped computing resources 814 may include computing, networking, memory, or storage resources that can be configured or allocated to support groups of one or more workloads. In at least one embodiment, several node CRs, including CPUs or processors, may be grouped within one or more racks to provide computing resources to support one or more workloads. In at least one embodiment, the one or more racks may also include any number of power modules, cooling modules, and network switches, in any combination.

[0087] In at least one embodiment, resource coordinator 812 may configure or otherwise control one or more nodes CR816(1)-816(N) and / or grouped computing resources 814. In at least one embodiment, resource coordinator 812 may include a software design infrastructure (“SDI”) management entity for data center 800. In at least one embodiment, resource coordinator 812 may include hardware, software, or some combination thereof.

[0088] In at least one embodiment, such as Figure 8As shown, framework layer 820 includes a job scheduler 822, a configuration manager 824, a resource manager 826, and a distributed file system 828. In at least one embodiment, framework layer 820 may include a framework of software 832 supporting software layer 830 and / or one or more applications 842 supporting application layer 840. In at least one embodiment, software 832 or application 842 may respectively include web-based service software or applications, such as services or applications provided by Amazon Web Services, Google Cloud, and Microsoft Azure. In at least one embodiment, framework layer 820 may be, but is not limited to, a free and open-source software web application framework, such as Apache Spark™ (hereinafter referred to as "Spark") which can leverage distributed file system 828 for large-scale data processing (e.g., "big data"). In at least one embodiment, job scheduler 832 may include Spark drivers to facilitate the scheduling of workloads supported by the various layers of data center 800. In at least one embodiment, configuration manager 824 may be able to configure different layers, such as software layer 830 and framework layer 820 including Spark and distributed file system 828 for supporting large-scale data processing. In at least one embodiment, resource manager 826 is capable of managing cluster or group computing resources mapped to or allocated to support distributed file system 828 and job scheduler 822. In at least one embodiment, cluster or group computing resources may include group computing resources 814 on data center infrastructure layer 810. In at least one embodiment, resource manager 826 may coordinate with resource coordinator 812 to manage these mapped or allocated computing resources.

[0089] In at least one embodiment, the software 832 included in the software layer 830 may include software used by at least a portion of the nodes CR816(1)-816(N), the grouped computing resources 814, and / or the distributed file system 828 of the framework layer 820. One or more types of software may include, but are not limited to, Internet web page search software, email virus scanning software, database software, and streaming video content software.

[0090] In at least one embodiment, the application layer 840 may include one or more applications 842 that can be used by at least a portion of nodes CR816(1)-816(N), grouped computing resources 814, and / or the distributed file system 828 of the framework layer 820. The one or more types of applications may include, but are not limited to, any number of genomics applications, cognitive computing and machine learning applications, including training or inference software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.), or other machine learning applications used in conjunction with one or more embodiments.

[0091] In at least one embodiment, any of the configuration manager 824, resource manager 826, and resource coordinator 812 can implement any number and type of self-modification actions based on any amount and type of data acquired in any technically feasible manner. In at least one embodiment, self-modification actions can mitigate potentially poor configuration decisions by data center operators of data center 800 and can prevent underutilization and / or poor performance of the data center.

[0092] In at least one embodiment, data center 800 may include tools, services, software, or other resources to train one or more machine learning models or to use one or more machine learning models to predict or infer information according to one or more embodiments described herein. For example, in at least one embodiment, a machine learning model can be trained by calculating weight parameters based on a neural network architecture using the software and computing resources described above with respect to data center 800. In at least one embodiment, information can be inferred or predicted using trained machine learning models corresponding to one or more neural networks using the resources described above with respect to data center 800 by using weight parameters calculated through one or more training techniques described herein.

[0093] In at least one embodiment, the data center may use a CPU, application-specific integrated circuit (ASIC), GPU, FPGA, or other hardware to utilize the aforementioned resources to perform training and / or inference. Furthermore, one or more of the aforementioned software and / or hardware resources may be configured as a service to allow a user to train or perform information inference, such as image recognition, speech recognition, or other artificial intelligence services.

[0094] Inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 7A and / or Figure 7BDetails are provided regarding the inference and / or training logic 715. In at least one embodiment, the inference and / or training logic 715 can be implemented in the system. Figure 8 Used in systems for reasoning or predicting operations based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architectures or neural network use cases described herein.

[0095] These components can be used for content generation.

[0096] Computer System

[0097] Figure 9 This is a block diagram illustrating an exemplary computer system according to at least one embodiment. The exemplary computer system may be a system of interconnected devices and components, a system-on-a-chip (SoC), or some combination thereof formed with a processor, which may include an execution unit to execute instructions. In at least one embodiment, according to this disclosure, such as the embodiments described herein, computer system 900 may include, but is not limited to, components such as processor 902, whose execution unit includes logic to execute algorithms for process data. In at least one embodiment, computer system 900 may include a processor, such as one available from Intel Corporation of Santa Clara, California. Processor family, Xeon TM , XScale TM and / or StrongARM TM , Core TM or Nervana TM A microprocessor may be used, although other systems (including PCs, engineering workstations, set-top boxes, etc.) with other microprocessors may also be used. In at least one embodiment, computer system 900 may execute a version of the Windows operating system available from Microsoft Corporation of Redmond, Washington, although other operating systems (such as UNIX and Linux), embedded software, and / or graphical user interfaces may also be used.

[0098] The embodiments can be used in other devices, such as handheld devices and embedded applications. Some examples of handheld devices include cellular phones, Internet Protocol (IP) devices, digital cameras, personal digital assistants (“PDAs”), and handheld PCs. In at least one embodiment, the embedded application may include a microcontroller, a digital signal processor (“DSP”), a system-on-a-chip (SoC), a network computer (“NetPC”), a set-top box, a network hub, a wide area network (“WAN”) switch, or any other system that can execute one or more instructions according to at least one embodiment.

[0099] In at least one embodiment, the computer system 900 may include, but is not limited to, a processor 902, which may include, but is not limited to, one or more execution units 908, to perform machine learning model training and / or inference according to the techniques described herein. In at least one embodiment, the computer system 900 is a single-processor desktop or server system, but in another embodiment, the computer system 900 may be a multiprocessor system. In at least one embodiment, the processor 902 may include, but is not limited to, a Complex Instruction Set Computing (“CISC”) microprocessor, a Reduced Instruction Set Computing (“RISC”) microprocessor, a Very Long Instruction Word (“VLIW”) microprocessor, a processor implementing instruction set combination, or any other processor device, such as a digital signal processor. In at least one embodiment, the processor 902 may be coupled to a processor bus 910, which can transmit data signals between the processor 902 and other components in the computer system 900.

[0100] In at least one embodiment, processor 902 may include, but is not limited to, a Level 1 (“L1”) internal cache memory (“cache”) 904. In at least one embodiment, processor 902 may have a single internal cache or multiple levels of internal cache. In at least one embodiment, the cache memory may reside external to processor 902. Depending on specific implementation and requirements, other embodiments may also include a combination of internal and external caches. In at least one embodiment, register file 906 may store different types of data in various registers, including but not limited to integer registers, floating-point registers, status registers, and instruction pointer registers.

[0101] In at least one embodiment, a logic execution unit 908, including but not limited to performing integer and floating-point operations, is also located within the processor 902. In at least one embodiment, the processor 902 may further include a microcode (“ucode”) read-only memory (“ROM”) for storing microcode of certain macro instructions. In at least one embodiment, the execution unit 908 may include logic for processing a packaged instruction set 909. In at least one embodiment, by including the packaged instruction set 909 in the instruction set of a general-purpose processor, along with the associated circuitry for executing the instructions, the packaged data in the processor 902 can be used to perform operations used by numerous multimedia applications. In one or more embodiments, the execution of numerous multimedia applications can be accelerated and performed more efficiently by using the full width of the processor’s data bus to perform operations on the packaged data, which may eliminate the need to transfer smaller data units on the processor’s data bus to perform one or more operations on one data element at a time.

[0102] In at least one embodiment, the execution unit 908 may also be used in a microcontroller, embedded processor, graphics device, DSP, and other types of logic circuitry. In at least one embodiment, the computer system 900 may include, but is not limited to, memory 920. In at least one embodiment, memory 920 may be implemented as a dynamic random access memory (“DRAM”) device, a static random access memory (“SRAM”) device, a flash memory device, or other storage device. In at least one embodiment, memory 920 may store instructions 919 and / or data 921 represented by data signals that can be executed by processor 902.

[0103] In at least one embodiment, the system logic chip may be coupled to the processor bus 910 and the memory 920. In at least one embodiment, the system logic chip may include, but is not limited to, a memory controller hub (“MCH”) 916, and the processor 902 may communicate with the MCH 916 via the processor bus 910. In at least one embodiment, the MCH 916 may provide a high-bandwidth memory path 918 to the memory 920 for instruction and data storage, as well as for storage of graphics commands, data, and textures. In at least one embodiment, the MCH 916 may initiate data signals between the processor 902, the memory 920, and other components in the computer system 900, and bridge data signals between the processor bus 910, the memory 920, and the system I / O 922. In at least one embodiment, the system logic chip may provide a graphics port for coupling to a graphics controller. In at least one embodiment, the MCH 916 may be coupled to the memory 920 via the high-bandwidth memory path 918, and the graphics / video card 912 may be coupled to the MCH 916 via an Accelerated Graphics Port (“AGP”) interconnect 914.

[0104] In at least one embodiment, computer system 900 may use system I / O 922, which is a proprietary hub interface bus, to couple MCH 916 to I / O controller hub (“ICH”) 930. In at least one embodiment, ICH 930 may provide direct connectivity to certain I / O devices via a local I / O bus. In at least one embodiment, the local I / O bus may include, but is not limited to, a high-speed I / O bus for connecting peripheral devices to memory 920, chipset, and processor 902. Examples may include, but are not limited to, audio controller 929, firmware hub (“Flash BIOS”) 928, wireless transceiver 926, data storage 924, a conventional I / O controller 923 including user input and keyboard interface 925, serial expansion port 927 (e.g., Universal Serial Bus (USB) port), and network controller 934. Data storage 924 may include hard disk drives, floppy disk drives, CD-ROM devices, flash memory devices, or other mass storage devices.

[0105] In at least one embodiment, Figure 9 A system including interconnected hardware devices or "chips" is shown, while in other embodiments, Figure 9An exemplary system-on-a-chip (SoC) may be illustrated. In at least one embodiment, the device may be interconnected with a proprietary interconnect, a standardized interconnect (e.g., PCIe), or some combination thereof. In at least one embodiment, one or more components of the computer system 900 are interconnected using a compute fast link (CXL) interconnect.

[0106] The inference and / or training logic 715 is used to perform inference and / or training operations related to one or more embodiments. (The following is in conjunction with...) Figure 7A and / or Figure 7B Details are provided regarding the inference and / or training logic 715. In at least one embodiment, the inference and / or training logic 715 can be... Figure 9 Used in systems for reasoning or predicting operations based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architectures or neural network use cases described herein.

[0107] These components can be used for content generation.

[0108] Figure 10 This is a block diagram illustrating an electronic device 1000 for utilizing a processor 1010 according to at least one embodiment. In at least one embodiment, the electronic device 1000 may be, for example, but not limited to, a laptop computer, tower server, rack server, blade server, desktop computer, tablet computer, mobile device, telephone, embedded computer, or any other suitable electronic device.

[0109] In at least one embodiment, the electronic device 1000 may be communicatively coupled to a processor 1010 of any suitable number or type of components, peripherals, modules, or devices. In at least one embodiment, the processor 1010 is coupled using a bus or interface, such as an I2C bus, a system management bus (“SMBus”), a low pin count (LPC) bus, a serial peripheral interface (“SPI”), a high-definition audio (“HDA”) bus, a serial advanced technology accessory (“SATA”) bus, a universal serial bus (“USB”) (versions 1, 2, and 3), or a universal asynchronous receiver / transmitter (“UART”) bus.

[0110] In at least one embodiment, Figure 10 The system shown includes interconnected hardware devices or "chips," while in other embodiments, Figure 10 An exemplary system-on-a-chip (SoC) can be illustrated. In at least one embodiment, Figure 10 The device shown can be interconnected with proprietary interconnects, standardized interconnects (e.g., PCIe), or some combination thereof. In at least one embodiment, Figure 10One or more components are interconnected using Computational Fast Link (CXL) interconnects.

[0111] In at least one embodiment, Figure 10 It may include a display 1024, a touch screen 1025, a touchpad 1030, a near field communication unit (“NFC”) 1045, a sensor hub 1040, a thermal sensor 1046, a fast chipset (“EC”) 1035, a trusted platform module (“TPM”) 1038, a BIOS / firmware / flash (“BIOS, FW Flash”) 1022, a DSP 1060, a drive 1020 (e.g., a solid-state drive (“SSD”) or a hard disk drive (“HDD”)), a wireless local area network unit (“WLAN”) 1050, a Bluetooth unit 1052, a wireless wide area network unit (“WWAN”) 1056, a global positioning system (GPS) 1055, a camera (“USB 3.0 camera”) 1054 (e.g., a USB 3.0 camera) and / or a low-power double data rate (“LPDDR”) memory unit (“LPDDR3”) 1015 implemented in, for example, the LPDDR3 standard. These components can each be implemented in any suitable way.

[0112] In at least one embodiment, other components may be communicatively coupled to processor 1010 via the components described above. In at least one embodiment, accelerometer 1041, ambient light sensor (“ALS”) 1042, compass 1043, and gyroscope 1044 may be communicatively coupled to sensor hub 1040. In at least one embodiment, thermal sensor 1039, fan 1037, keyboard 1036, and touchpad 1030 may be communicatively coupled to EC 1035. In at least one embodiment, speaker 1063, earphone 1064, and microphone (“mic”) 1065 may be communicatively coupled to audio unit (“audio codec and Class D amplifier”) 1062, which in turn may be communicatively coupled to DSP 1060. In at least one embodiment, audio unit 1062 may include, for example, but not limited to, audio encoder / decoder (“codec”) and Class D amplifier. In at least one embodiment, SIM card (“SIM”) 1057 may be communicatively coupled to WWAN unit 1056. In at least one embodiment, components such as WLAN unit 1050, Bluetooth unit 1052, and WWAN unit 1056 can be implemented as next-generation form factor (NGFF).

[0113] Inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. The following is combined with... Figure 7A and / or Figure 7BDetails are provided regarding the inference and / or training logic 715. In at least one embodiment, the inference and / or training logic 715 can be... Figure 10 The system is used to infer or predict operations based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architectures or neural network use cases described herein.

[0114] These components can be used for content generation.

[0115] Figure 11 This is a block diagram of a processing system according to at least one embodiment. In at least one embodiment, system 1100 includes one or more processors 1102 and one or more graphics processors 1108, and may be a single-processor desktop system, a multi-processor workstation system, or a server system having a large number of processors 1102 or processor cores 1107. In at least one embodiment, system 1100 is a processing platform incorporated within a system-on-a-chip (SoC) integrated circuit for use in mobile, handheld, or embedded devices.

[0116] In at least one embodiment, system 1100 may include or be integrated into a server-based gaming platform, including a game console, mobile game console, handheld game console, or online game console, which are game and media consoles. In at least one embodiment, system 1100 is a mobile phone, smartphone, tablet computing device, or mobile internet device. In at least one embodiment, processing system 1100 may also include components coupled to or integrated into a wearable device, such as a smartwatch, smart glasses, augmented reality, or virtual reality device. In at least one embodiment, processing system 1100 is a television or set-top box device having one or more processors 1102 and a graphical interface generated by one or more graphics processors 1108.

[0117] In at least one embodiment, each of the one or more processors 1102 includes one or more processor cores 1107 for processing instructions that, when executed, perform operations against the system and user software. In at least one embodiment, each of the one or more processor cores 1107 is configured to process a particular set of instructions 1109. In at least one embodiment, the instruction set 1109 may facilitate Complex Instruction Set Computing (CISC), Reduced Instruction Set Computing (RISC), or computation via Very Long Instruction Word (VLIW). In at least one embodiment, the one or more processor cores 1107 may each process a different set of instructions 1109, which may include instructions that facilitate the emulation of other instruction sets. In at least one embodiment, the one or more processor cores 1107 may also include other processing devices, such as digital signal processors (DSPs).

[0118] In at least one embodiment, one or more processors 1102 include cache memory 1104. In at least one embodiment, one or more processors 1102 may have a single internal cache or multiple levels of internal caches. In at least one embodiment, the cache memory is shared among the various components of one or more processors 1102. In at least one embodiment, one or more processors 1102 also use an external cache (e.g., a Level 3 (L3) cache or a last-level cache (LLC)) (not shown), which can be shared among one or more processor cores 1107 using known cache coherence techniques. In at least one embodiment, one or more processors 1102 further include a register file 1106, and the processor may include different types of registers (e.g., integer registers, floating-point registers, status registers, and instruction pointer registers) for storing different types of data. In at least one embodiment, register file 1106 may include general-purpose registers or other registers.

[0119] In at least one embodiment, one or more processors 1102 are coupled to one or more interface buses 1110 to transmit communication signals, such as address, data, or control signals, between the processors 1102 and other components in the system 1100. In at least one embodiment, the one or more interface buses 1110 may be a processor bus, such as a version of the Direct Media Interface (DMI) bus. In at least one embodiment, the one or more interface buses 1110 are not limited to the DMI bus and may include one or more peripheral component interconnect buses (e.g., PCI, PCI Express), memory buses, or other types of interface buses. In at least one embodiment, the processor 1102 includes an integrated memory controller 1116 and a platform controller hub 1130. In at least one embodiment, the memory controller 1116 facilitates communication between memory devices and other components of the processing system 1100, while the platform controller hub (PCH) 1130 provides connectivity to I / O devices via a local I / O bus.

[0120] In at least one embodiment, memory device 1120 may be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, a flash memory device, a phase-change memory device, or a device with suitable performance for use as processor memory. In at least one embodiment, memory device 1120 may be used as system memory of processing system 1100 to store data 1122 and instructions 1121 for use when one or more processors 1102 execute an application or process. In at least one embodiment, memory controller 1116 is also coupled to an optional external graphics processor 1112, which may communicate with one or more graphics processors 1108 of one or more processors 1102 to perform graphics and media operations. In at least one embodiment, display device 1111 may be connected to processor 1102. In at least one embodiment, display device 1111 may include one or more internal display devices, such as in mobile electronic devices or laptop devices, or external display devices connected via a display interface (e.g., DisplayPort). In at least one embodiment, the display device 1111 may include a head-mounted display (HMD), such as a stereoscopic display device for virtual reality (VR) or augmented reality (AR) applications.

[0121] In at least one embodiment, the platform controller hub 1130 enables peripheral devices to connect to the memory device 1120 and one or more processors 1102 via a high-speed I / O bus. In at least one embodiment, the I / O peripheral devices include, but are not limited to, an audio controller 1146, a network controller 1134, a firmware interface 1128, a wireless transceiver 1126, a touch sensor 1125, and a data storage device 1124 (e.g., a hard disk drive, flash memory, etc.). In at least one embodiment, the data storage device 1124 may be connected via a storage interface (e.g., SATA) or via a peripheral bus, such as a peripheral component interconnect bus (e.g., PCI, PCIe). In at least one embodiment, the touch sensor 1125 may include a touchscreen sensor, a pressure sensor, or a fingerprint sensor. In at least one embodiment, the wireless transceiver 1126 may be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile network transceiver, such as a 3G, 4G, or LTE transceiver. In at least one embodiment, the firmware interface 1128 enables communication with the system firmware and may be, for example, a Unified Extensible Firmware Interface (UEFI). In at least one embodiment, network controller 1134 may enable network connectivity to a wired network. In at least one embodiment, a high-performance network controller (not shown) is coupled to one or more interface buses 1110. In at least one embodiment, audio controller 1146 is a multi-channel high-definition audio controller. In at least one embodiment, processing system 1100 includes an optional legacy I / O controller 1140 for coupling legacy (e.g., Personal System 2 (PS / 2)) devices to system 1100. In at least one embodiment, platform controller hub 1130 may also be connected to one or more Universal Serial Bus (USB) controllers 1142 that connect input devices, such as a keyboard and mouse combination 1143, a camera 1144, or other USB input devices.

[0122] In at least one embodiment, instances of the memory controller 1116 and platform controller hub 1130 may be integrated into a discrete external graphics processor, such as external graphics processor 1112. In at least one embodiment, the platform controller hub 1130 and / or the memory controller 1116 may be external to one or more processors 1102. For example, in at least one embodiment, system 1100 may include external memory controller 1116 and platform controller hub 1130, which may be configured as a memory controller hub and peripheral controller hub in a system chipset communicating with processor 1102.

[0123] Inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. The following is combined with... Figure 7A and / or Figure 7B Details regarding the inference and / or training logic 715 are provided. In at least one embodiment, some or all of the inference and / or training logic 715 may be incorporated into the graphics processor 1100. For example, in at least one embodiment, the training and / or inference techniques described herein may use one or more ALUs embodied in the graphics processor. Furthermore, in at least one embodiment, the inference and / or training operations described herein may use, in addition to Figure 7A and / or Figure 7B The logic is performed using logic other than that shown. In at least one embodiment, the weight parameters may be stored in on-chip or off-chip memory and / or registers (shown or not shown), which configure the ALU of the graphics processor to execute one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

[0124] These components can be used for content generation.

[0125] Figure 12 This is a block diagram of a processor 1200 having one or more processor cores 1202A-1202N, an integrated memory controller 1214, and an integrated graphics processor 1208 according to at least one embodiment. In at least one embodiment, the processor 1200 may include additional cores, up to and including additional cores 1202N indicated by dashed boxes. In at least one embodiment, each of the one or more processor cores 1202A-1202N includes one or more internal cache units 1204A-1204N. In at least one embodiment, each processor core may also access one or more shared cache units 1206.

[0126] In at least one embodiment, one or more internal cache units 1204A-1204N and one or more shared cache units 1206 represent a cache memory hierarchy within the processor 1200. In at least one embodiment, one or more cache memory units 1204A-1204N may include at least one level of instruction and data cache within each processor core and one or more levels of cache in a shared intermediate cache, such as Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache, wherein the highest level of cache preceding external memory is classified as LLC. In at least one embodiment, cache coherence logic maintains coherence between the various cache units 1206 and 1204A-1204N.

[0127] In at least one embodiment, the processor 1200 may further include a set of one or more bus controller units 1216 and a system agent core 1210. In at least one embodiment, one or more bus controller units 1216 manage a set of peripheral buses, such as one or more PCI or PCIe buses. In at least one embodiment, the system agent core 1210 provides management functions for various processor components. In at least one embodiment, the system agent core 1210 includes one or more integrated memory controllers 1214 to manage access to various external memory devices (not shown).

[0128] In at least one embodiment, one or more processor cores 1202A-1202N include support for concurrent multithreading. In at least one embodiment, system agent core 1210 includes components for coordinating one or more processor cores 1202A-1202N during multithreaded processing. In at least one embodiment, system agent core 1210 may additionally include a power control unit (PCU) including logic and components for regulating one or more power states of one or more processor cores 1202A-1202N and graphics processor 1208.

[0129] In at least one embodiment, processor 1200 further includes a graphics processor 1208 for performing graph processing operations. In at least one embodiment, graphics processor 1208 is coupled to one or more shared cache units 1206 and a system proxy core 1210 including one or more integrated memory controllers 1214. In at least one embodiment, system proxy core 1210 further includes a display controller 1211 for driving graphics processor outputs to one or more coupled displays. In at least one embodiment, display controller 1211 may also be a separate module coupled to graphics processor 1208 via at least one interconnect, or it may be integrated within graphics processor 1208.

[0130] In at least one embodiment, the ring-based interconnect unit 1212 is used to couple internal components of the processor 1200. In at least one embodiment, alternative interconnect units, such as point-to-point interconnects, switched interconnects, or other technologies, may be used. In at least one embodiment, the graphics processor 1208 is coupled to the ring-based interconnect unit 1212 via I / O link 1213.

[0131] In at least one embodiment, I / O link 1213 represents at least one of a variety of I / O interconnects, including packaged I / O interconnects that facilitate communication between various processor components and high-performance embedded memory module 1218 (e.g., eDRAM module). In at least one embodiment, each of one or more processor cores 1202A-1202N and graphics processor 1208 uses embedded memory module 1218 as a shared last-level cache.

[0132] In at least one embodiment, one or more processor cores 1202A-1202N are homogeneous cores executing a common instruction set architecture. In at least one embodiment, one or more processor cores 1202A-1202N are heterogeneous in terms of instruction set architecture (ISA), with one or more processor cores 1202A-1202N executing a common instruction set, while one or more other cores of one or more processor cores 1202A-1202N execute a subset of the common instruction set or a different instruction set. In at least one embodiment, in terms of microarchitecture, one or more processor cores 1202A-1202N are heterogeneous, with one or more cores having relatively high power consumption coupled to one or more power cores having lower power consumption. In at least one embodiment, processor 1200 may be implemented on one or more chips or implemented as a SoC integrated circuit.

[0133] Inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. The following is combined with... Figure 7A and / or Figure 7B Details regarding the inference and / or training logic 715 are provided. In at least one embodiment, some or all of the inference and / or training logic 715 may be incorporated into the processor 1200. For example, in at least one embodiment, the training and / or inference techniques described herein may use one or more ALUs embodied in... Figure 12 The graphics processor 1208, one or more graphics cores 1202A-1202N, or other components are used. Furthermore, in at least one embodiment, the inference and / or training operations described herein can use, except... Figure 7A and / or Figure 7B The logic is performed using logic other than that shown. In at least one embodiment, the weight parameters may be stored in on-chip or off-chip memory and / or registers (shown or not shown), which configure the ALU of the graphics processor 1200 to execute one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

[0134] These components can be used for content generation.

[0135] Virtualization computing platform

[0136] Figure 13 This is an example data flow diagram of process 1300 for generating and deploying an image processing and inference pipeline according to at least one embodiment. In at least one embodiment, process 1300 may be deployed for use with imaging devices, processing devices, and / or other device types at one or more facilities 1302. Process 1300 may be executed within training system 1304 and / or deployment system 1306. In at least one embodiment, training system 1304 may be used to train, deploy, and implement machine learning models (e.g., neural networks, object detection algorithms, computer vision algorithms, etc.) for use in deployment system 1306. In at least one embodiment, deployment system 1306 may be configured to offload processing and computing resources in a distributed computing environment to reduce the infrastructure requirements of facility 1302. In at least one embodiment, one or more applications in the pipeline may use or invoke services of deployment system 1306 (e.g., inference, visualization, computation, AI, etc.) during application execution.

[0137] In at least one embodiment, some applications used in the advanced processing and inference pipeline may use machine learning models or other AI to perform one or more processing steps. In at least one embodiment, a machine learning model may be trained at facility 1302 using data 1308 (e.g., imaging data) generated at facility 1302 (and stored on one or more Picture Archiving and Communication System (PACS) servers at facility 1302), imaging or sequencing data 1308 from another or more facilities, or a combination thereof. In at least one embodiment, training system 1304 may be used to provide applications, services, and / or other resources to generate a deployable machine learning model for the work of deploying system 1306.

[0138] In at least one embodiment, the model registry 1324 may be supported by an object storage system that supports version control and object metadata. In at least one embodiment, the object storage may be accessed from within a cloud platform via, for example, a cloud storage-compatible application programming interface (API). In at least one embodiment, machine learning models within the model registry 1324 may be uploaded, listed, modified, or deleted by the developer or partner of a system interacting with the API. In at least one embodiment, the API may provide access to methods that allow users with appropriate credentials to associate models with applications, enabling the models to be executed as part of the containerized instantiation of the application.

[0139] In at least one embodiment, training system 1304 ( Figure 13This can include situations where facility 1302 is training its own machine learning model or has an existing machine learning model that needs optimization or updating. In at least one embodiment, imaging data 1308 generated by imaging equipment, sequencing equipment, and / or other types of equipment can be received. In at least one embodiment, once the imaging data 1308 is received, AI-assisted annotation 1310 can be used to help generate annotations corresponding to the imaging data 1308 for use as ground-based data for machine learning models. In at least one embodiment, AI-assisted annotation 1310 can include one or more machine learning models (e.g., convolutional neural networks (CNNs)) that can be trained to generate annotations corresponding to certain types of imaging data 1308 (e.g., from certain equipment). In at least one embodiment, AI-assisted annotation 1310 can then be used directly or adjusted or fine-tuned using annotation tools to generate ground-based data. In at least one embodiment, AI-assisted annotation 1310, labeled data 1312, or a combination thereof can be used as ground-based data for training machine learning models. In at least one embodiment, the trained machine learning model may be referred to as (one or more) output models 1316 and may be used by deployment system 1306 as described herein.

[0140] In at least one embodiment, the training pipeline may include a scenario where facility 1302 requires a machine learning model to perform one or more processing tasks for deploying one or more applications in system 1306, but facility 1302 may not currently have such a machine learning model (or may not have a model optimized, efficient, or effective for this purpose). In at least one embodiment, an existing machine learning model may be selected from model registry 1324. In at least one embodiment, model registry 1324 may include machine learning models trained to perform various inference tasks on imaging data. In at least one embodiment, the machine learning model in model registry 1324 may be trained on imaging data from a different facility (e.g., a remote facility) instead of facility 1302. In at least one embodiment, the machine learning model may have already been trained on imaging data from one location, two locations, or any number of locations. In at least one embodiment, when training on imaging data from a specific location, training may be performed at that location, or at least in a manner that protects the confidentiality of the imaging data or restricts the off-site transfer of the imaging data. In at least one embodiment, once a model has been trained or partially trained at one location, a machine learning model can be added to model registry 1324. In at least one embodiment, the machine learning model can then be retrained or updated at any number of other facilities, and the retrained or updated model can be used in model registry 1324. In at least one embodiment, a machine learning model (referred to as output model 1316) can then be selected from model registry 1324, and can be used in deployment system 1306 to perform one or more processing tasks for one or more applications of the deployment system.

[0141] In at least one embodiment, the scenario may include facility 1302, which requires a machine learning model to perform one or more processing tasks for deploying one or more applications in system 1306, but facility 1302 may not currently have such a machine learning model (or may not have an optimized, efficient, or effective model). In at least one embodiment, the machine learning model selected from model registry 1324 may not be fine-tuned or optimized for the imaging data 1308 generated at facility 1302 due to population variability, robustness, anomalous diversity of training data, and / or other problems with the training data used to train the machine learning model. In at least one embodiment, AI-assisted annotation 1310 may be used to help generate annotations corresponding to the imaging data 1308 for use as ground-based data for training or updating the machine learning model. In at least one embodiment, labeled clinical data 1312 may be used as ground-based data for training the machine learning model. In at least one embodiment, retraining or updating the machine learning model may be referred to as model training 1314. In at least one embodiment, model training 1314 (e.g., AI-assisted annotation 1310, labeled data 1312, or a combination thereof) can be used as ground-based real-world data to retrain or update the machine learning model. In at least one embodiment, the trained machine learning model can be referred to as output model 1316 and can be used by deployment system 1306, as described herein.

[0142] In at least one embodiment, deployment system 1306 may include software 1318, service 1320, hardware 1322, and / or other components, features, and functions. In at least one embodiment, deployment system 1306 may include a software "stack" such that software 1318 can be built on top of service 1320 and can be used to perform some or all of the processing tasks, and service 1320 and software 1318 can be built on top of hardware 1322 and use hardware 1322 to perform the deployment system's processing, storage, and / or other computational tasks. In at least one embodiment, software 1318 may include any number of different containers, each of which can perform an instantiation of an application. In at least one embodiment, each application can perform one or more processing tasks (e.g., inference, object detection, feature detection, segmentation, image enhancement, calibration, etc.) in a high-level processing and inference pipeline. In at least one embodiment, in addition to receiving and configuring imaging data for use by each container and / or by facility 1302 after processing through the pipeline, advanced processing and inference pipelines (e.g., to convert output back to available data types) can be defined based on the selection of different containers desired or required for processing imaging data 1308. In at least one embodiment, a combination of containers within software 1318 (e.g., constituting a pipeline) may be referred to as a virtual instrument (as described in more detail herein), and the virtual instrument may utilize service 1320 and hardware 1322 to perform some or all of the processing tasks of an application instantiated within the container.

[0143] In at least one embodiment, the data processing pipeline may receive input data (e.g., imaging data 1308) in a specific format in response to an inference request (e.g., a request from a user of deployment system 1306). In at least one embodiment, the input data may represent one or more images, videos, and / or other data representations generated by one or more imaging devices. In at least one embodiment, the data may be preprocessed as part of the data processing pipeline to prepare it for processing by one or more applications. In at least one embodiment, post-processing may be performed on the output of one or more inference tasks or other processing tasks of the pipeline to prepare output data for the next application and / or to prepare output data for user transmission and / or use (e.g., as a response to an inference request). In at least one embodiment, the inference task may be performed by one or more machine learning models, such as trained or deployed neural networks, which may include the output model 1316 of training system 1304.

[0144] In at least one embodiment, the tasks of the data processing pipeline can be encapsulated in containers, each container representing a discrete, fully functional instantiation of an application and a virtualized computing environment capable of referencing a machine learning model. In at least one embodiment, containers or applications can be published to a private (e.g., limited access) area of ​​a container registry (described in more detail herein), and trained or deployed models can be stored in a model registry 1324 and associated with one or more applications. In at least one embodiment, an image of an application (e.g., a container image) can be used in the container registry, and once a user selects an image from the container registry for deployment in the pipeline, that image can be used to generate containers for instantiation of the application for use by the user's system.

[0145] In at least one embodiment, a developer (e.g., a software developer, clinician, physician, etc.) can develop, publish, and store an application (e.g., as a container) for performing image processing and / or inference on provided data. In at least one embodiment, a software development kit (SDK) associated with the system can be used to perform development, publication, and / or storage (e.g., to ensure that the developed application and / or container conforms to or is compatible with the system). In at least one embodiment, the developed application can be tested locally using the SDK (e.g., at a first facility, testing data from a first facility), the SDK serving as a system (e.g.,...). Figure 12 System 1200 may support at least some services 1320. In at least one embodiment, since a DICOM object may contain one to hundreds of images or other data types, and due to variations in the data, the developer may be responsible for managing (e.g., setting up constructs for preprocessing built into the application, etc.) the extraction and preparation of incoming data. In at least one embodiment, once verified by system 1300 (e.g., for accuracy), the application becomes available in the container registry for user selection and / or implementation to perform one or more processing tasks on data at the user's facility (e.g., a second facility).

[0146] In at least one embodiment, the developer can then share the application or container over a network for the system (e.g., Figure 13The system 1300 allows for user access and use. In at least one embodiment, completed and validated applications or containers may be stored in a container registry, and associated machine learning models may be stored in a model registry 1324. In at least one embodiment, a requesting entity (which provides an inference or image processing request) may browse the container registry and / or model registry 1324 to obtain applications, containers, datasets, machine learning models, etc., select desired combinations of elements to include in the data processing pipeline, and submit an image processing request. In at least one embodiment, the request may include input data necessary to execute the request (and, in some examples, patient-related data), and / or may include selections of applications and / or machine learning models to be executed when processing the request. In at least one embodiment, the request may then be passed to one or more components of the deployment system 1306 (e.g., the cloud) to perform processing in the data processing pipeline. In at least one embodiment, processing performed by the deployment system 1306 may include referencing elements (e.g., applications, containers, models, etc.) selected from the container registry and / or model registry 1324. In at least one embodiment, once the results are generated through the pipeline, the results can be returned to the user for reference (e.g., for viewing in a suite of viewing applications executed locally, on a local workstation, or on a terminal).

[0147] In at least one embodiment, service 1320 may be utilized to assist in processing or executing applications or containers in the pipeline. In at least one embodiment, service 1320 may include computing services, artificial intelligence (AI) services, visualization services, and / or other service types. In at least one embodiment, service 1320 may provide functionality common to one or more applications in software 1318, thus abstracting functionality into services that can be invoked or utilized by applications. In at least one embodiment, the functionality provided by service 1320 can operate dynamically and more efficiently, while also allowing applications to process data in parallel (e.g., using parallel computing platform 1230). Figure 12To allow for good scaling. In at least one embodiment, it is not required that every application providing the same functionality as service 1320 must have a corresponding instance of service 1320, but rather service 1320 can be shared between and among various applications. In at least one embodiment, as a non-limiting example, the service may include an inference server or engine that can be used to perform detection or segmentation tasks. In at least one embodiment, a model training service may be included, which can provide the ability to train and / or retrain machine learning models. In at least one embodiment, a data augmentation service may be further included, which can provide GPU-accelerated data (e.g., DICOM, RIS, CIS, conforming to REST, RPC, raw, etc.) extraction, resizing, scaling, and / or other enhancements. In at least one embodiment, a visualization service may be used, which can add image rendering effects (e.g., ray tracing, rasterization, denoising, sharpening, etc.) to add realism to two-dimensional (2D) and / or three-dimensional (3D) models. In at least one embodiment, a virtual instrument service may be included, which provides beamforming, segmentation, inference, imaging, and / or support for other applications within the virtual instrument pipeline.

[0148] In at least one embodiment, where service 1320 includes an AI service (e.g., an inference service), as part of application execution, one or more machine learning models can be executed by invoking (e.g., as an API call) the inference service (e.g., an inference server) to execute one or more machine learning models or their processing. In at least one embodiment, where another application includes one or more machine learning models for a segmentation task, the application can invoke the inference service to execute the machine learning models for performing one or more processing operations associated with the segmentation task. In at least one embodiment, software 1318 implementing advanced processing and inference pipelines, including a segmentation application and an anomaly detection application, can be pipelined because each application can invoke the same inference service to execute one or more inference tasks.

[0149] In at least one embodiment, hardware 1322 may include a GPU, CPU, graphics card, AI / deep learning system (e.g., an AI supercomputer, such as NVIDIA's DGX), cloud platform, or a combination thereof. In at least one embodiment, different types of hardware 1322 may be used to provide efficient, specially built support for software 1318 and services 1320 in deployment system 1306. In at least one embodiment, GPU processing may be used to perform local processing (e.g., at facility 1302) within the AI / deep learning system, in the cloud system, and / or other processing components of deployment system 1306 to improve the efficiency, accuracy, and performance of image processing and generation. In at least one embodiment, as a non-limiting example, software 1318 and / or services 1320 may be optimized for GPU processing in relation to deep learning, machine learning, and / or high-performance computing. In at least one embodiment, at least some of the computing environment of deployment system 1306 and / or training system 1304 may be executed in a data center, one or more supercomputers, or high-performance computing systems with GPU-optimized software (e.g., a hardware and software combination of an NVIDIA DGX system). In at least one embodiment, as described herein, hardware 1322 may include any number of GPUs that can be invoked to perform data processing in parallel. In at least one embodiment, the cloud platform may also include GPU-optimized execution for deep learning tasks, GPU processing for machine learning tasks, or other computational tasks. In at least one embodiment, an AI / deep learning supercomputer and / or GPU-optimized software (e.g., as provided on NVIDIA's DGX systems) may be used as a hardware abstraction and scaling platform to execute the cloud platform (e.g., NVIDIA's NGC). In at least one embodiment, the cloud platform may integrate application container cluster systems or coordination systems (e.g., Kubernetes) across multiple GPUs to achieve seamless scaling and load balancing.

[0150] Figure 14 This is a system diagram of an example system 1400 for generating and deploying an imaging deployment pipeline according to at least one embodiment. In at least one embodiment, system 1400 can be used to implement Figure 13 The process 1300 and / or other processes include advanced processing and inference pipelines. In at least one embodiment, system 1400 may include training system 1304 and deployment system 1306. In at least one embodiment, training system 1304 and deployment system 1306 may be implemented using software 1318, service 1320 and / or hardware 1322, as described herein.

[0151] In at least one embodiment, system 1400 (e.g., training system 1304 and / or deployment system 1306) may be implemented in a cloud computing environment (e.g., using cloud 1426). In at least one embodiment, system 1400 may be implemented locally (in relation to a healthcare facility) or as a combination of cloud computing resources and local computing resources. In at least one embodiment, access to the API in cloud 1426 may be restricted to authorized users by establishing security measures or protocols. In at least one embodiment, the security protocol may include a network token, which may be signed by an authentication service (e.g., AuthN, AuthZ, Gluecon, etc.) and may carry appropriate authorization. In at least one embodiment, the API of the virtual instrument (described herein) or other instances of system 1400 may be restricted to a set of public IPs that have been audited or authorized for interaction.

[0152] In at least one embodiment, the various components of system 1400 may communicate with each other using any of a variety of different network types, including but not limited to local area networks (LANs) and / or wide area networks (WANs) via wired and / or wireless communication protocols. In at least one embodiment, communication between facilities and components of system 1400 (e.g., for sending inference requests, for receiving the results of inference requests, etc.) may be transmitted via one or more data buses, wireless data protocols (Wi-Fi), wired data protocols (e.g., Ethernet), etc.

[0153] In at least one embodiment, similar to the description herein. Figure 13 As described, training system 1304 can execute one or more training pipelines 1404. In at least one embodiment, deployment system 1306 uses one or more machine learning models in one or more deployment pipelines 1410, and one or more training pipelines 1404 can be used to train or retrain one or more (e.g., pre-trained) models, and / or implement one or more pre-trained models 1406 (e.g., without retraining or updating). In at least one embodiment, one or more output models 1316 can be generated as a result of one or more training pipelines 1404. In at least one embodiment, one or more training pipelines 1404 can include any number of processing steps, such as, but not limited to, transformation or adaptation of imaging data (or other input data). In at least one embodiment, different training pipelines 1404 can be used for different machine learning models used by deployment system 1306. In at least one embodiment, similar to the description of... Figure 13 One or more training pipelines 1404 described in the first example can be used for the first machine learning model, similar to the one described above. Figure 13One or more training pipelines 1404 described in the second example can be used for a second machine learning model, similar to the one described above. Figure 13 One or more training pipelines 1404 of the described third example can be used for a third machine learning model. In at least one embodiment, any combination of tasks within the training system 1304 can be used according to the requirements of each corresponding machine learning model. In at least one embodiment, one or more machine learning models may have already been trained and are ready for deployment, so the training system 1304 may not perform any processing on the machine learning models, and one or more machine learning models may be implemented by the deployment system 1306.

[0154] In at least one embodiment, depending on the implementation or embodiment, the output model 1316 and / or the pre-trained model 1406 may include any type of machine learning model. In at least one embodiment, and not limited thereto, the machine learning model used by system 1400 may include models using linear regression, logistic regression, decision trees, support vector machines (SVM), Naive Bayes, k-nearest neighbors (Knn), k-means clustering, random forests, dimensionality reduction algorithms, gradient boosting algorithms, neural networks (e.g., autoencoders, convolutions, recursion, perceptrons, long / short-term memory (LSTM), Hopfield, Boltzmann, deep belief, deconvolution, generative adversarial, liquid state machines, etc.), and / or other types of machine learning models.

[0155] In at least one embodiment, one or more training pipelines 1404 may include AI-assisted annotations, as described herein regarding at least Figure 14More specifically, in at least one embodiment, labeled clinical data 1312 can be generated using any number of techniques (e.g., conventional annotation). In at least one embodiment, in some examples, labels or other annotations can be generated by drawing programs (e.g., annotation programs), computer-aided design (CAD) programs, tagging programs, another type of application suitable for generating annotations or labels for ground reality, and / or can be hand-drawn. In at least one embodiment, ground reality data can be synthetically generated (e.g., generated from computer models or renderings), realistically generated (e.g., designed and generated from real-world data), machine-generated (e.g., extracting features from data using feature analysis and learning, and then generating labels), human-annotated (e.g., taggers or annotation experts, defining the placement of labels), and / or combinations thereof. In at least one embodiment, for each instance of imaging data 1308 (or other data types used by machine learning models), there may be corresponding ground reality data generated by training system 1304. In at least one embodiment, AI-assisted annotation can be performed as part of deployment pipeline 1410; supplementing or replacing AI-assisted annotation included in training pipeline 1404. In at least one embodiment, system 1400 may include a multi-layer platform, which may include a software layer (e.g., software 1318) of a diagnostic application (or other application type) capable of performing one or more medical imaging and diagnostic functions. In at least one embodiment, system 1400 may be communicatively coupled (e.g., via an encrypted link) to a network of PACS servers in one or more facilities. In at least one embodiment, system 1400 may be configured to access and reference data from PACS servers to perform operations such as training machine learning models, deploying machine learning models, image processing, inference, and / or other operations.

[0156] In at least one embodiment, the software layer may be implemented as a secure, encrypted, and / or certified API that can invoke (e.g., call) an application or container from an external environment (e.g., facility 1302). In at least one embodiment, the application may then invoke or execute one or more services 1320 to perform computational, AI, or visualization tasks associated with their respective applications, and the software 1318 and / or service 1320 may utilize hardware 1322 to perform processing tasks efficiently and effectively. In at least one embodiment, a pair of DICOM adapters 1402A, 1402B may be used to send or receive communications to or from the training system 1304 and deployment system 1306.

[0157] In at least one embodiment, deployment system 1306 may execute one or more deployment pipelines 1410. In at least one embodiment, one or more deployment pipelines 1410 may include any number of applications, which may be sequential, non-sequential, or otherwise applied to imaging data (and / or other data types) – including AI-assisted annotation, the imaging data being generated by imaging devices, sequencing devices, genomics devices, etc., as described above. In at least one embodiment, as described herein, deployment pipeline 1410 for an individual device may be referred to as a virtual instrument for the device (e.g., a virtual ultrasound instrument, a virtual CT scanner, a virtual sequencing instrument, etc.). In at least one embodiment, for a single device, there may be more than one deployment pipeline 1410, depending on the desired information from the data generated from the device. In at least one embodiment, one or more first deployment pipelines 1410 may exist if it is desired to detect an anomaly from an MRI machine, and one or more second deployment pipelines 1410 may exist if it is desired to perform image enhancement from the output of the MRI machine.

[0158] In at least one embodiment, the image generation application may include processing tasks that utilize machine learning models. In at least one embodiment, a user may wish to use their own machine learning model or select a machine learning model from the model registry 1324. In at least one embodiment, a user may implement their own machine learning model or select a machine learning model to be included in the application performing the processing tasks. In at least one embodiment, the application may be optional and customizable, and by defining the application's construction, the deployment and implementation of the application for a specific user is presented as a more seamless user experience. In at least one embodiment, by leveraging other features of system 1400 (e.g., service 1320 and hardware 1322), one or more deployment pipelines 1410 may be more user-friendly, provide easier integration, and produce more accurate, efficient, and timely results.

[0159] In at least one embodiment, deployment system 1306 may include a user interface (“UI”) 1414 (e.g., a graphical user interface, a web interface, etc.) that can be used to select applications to be included in deployment pipeline 1410, deploy applications, modify or change applications or their parameters or configurations, use and interact with deployment pipeline 1410 during setup and / or deployment, and / or otherwise interact with deployment system 1306. In at least one embodiment, although not shown with respect to training system 1304, UI 1414 (or different user interfaces) can be used to select models to be used in deployment system 1306, to select models to be trained or retrained in training system 1304, and / or to otherwise interact with training system 1304.

[0160] In at least one embodiment, in addition to the application coordination system 1428, a pipeline manager 1412 may be used to manage interactions between applications or containers deployed through the pipeline 1410 and services 1320 and / or hardware 1322. In at least one embodiment, the pipeline manager 1412 may be configured to facilitate interactions from application to application, from application to service 1320, and / or from application or service to hardware 1322. In at least one embodiment, although shown as included in software 1318, this is not intended to be limiting, and in some examples, the pipeline manager 1412 may be included in service 1320. In at least one embodiment, the application coordination system 1428 (e.g., Kubernetes, DOCKER, etc.) may include a container coordination system that can group applications into containers as logical units for coordination, management, scaling, and deployment. In at least one embodiment, by associating applications from the deployment pipeline 1410 (e.g., rebuilding applications, splitting applications, etc.) with individual containers, each application can execute in a self-contained environment (e.g., at the kernel level) to improve speed and efficiency.

[0161] In at least one embodiment, each application and / or container (or its image) can be developed, modified, and deployed independently (e.g., a first user or developer can develop, modify, and deploy a first application, and a second user or developer can develop, modify, and deploy a second application separate from the first user or developer). This allows focus on the tasks of a single application and / or container without being hindered by the tasks of another application or container. In at least one embodiment, the pipeline manager 1412 and the application coordination system 1428 can facilitate communication and collaboration between different containers or applications. In at least one embodiment, the application coordination system 1428 and / or the pipeline manager 1412 can facilitate communication and resource sharing between and within each application or container, provided that the expected inputs and / or outputs of each container or application are known to the system (e.g., based on the construction of the application or container). In at least one embodiment, since one or more applications or containers in the deployment pipeline 1410 can share the same services and resources, the application coordination system 1428 can coordinate, load balance, and determine the sharing of services or resources between and within the various applications or containers. In at least one embodiment, the scheduler can be used to track the resource requirements of applications or containers, the current or planned use of these resources, and resource availability. Therefore, in at least one embodiment, the scheduler can allocate resources to different applications and distribute resources between and among applications, taking into account the system's needs and availability. In some examples, the scheduler (and / or other components of the application coordination system 1428) can determine resource availability and distribution based on constraints imposed on the system (e.g., user constraints), such as Quality of Service (QoS), the urgency of data output (e.g., to determine whether to perform real-time processing or delayed processing), etc.

[0162] In at least one embodiment, service 1320, utilized and shared by applications or containers in deployment system 1306, may include computing service 1416, AI service 1418, visualization service 1420, and / or other service types. In at least one embodiment, an application may invoke (e.g., execute) one or more services 1320 to perform processing operations for the application. In at least one embodiment, an application may utilize computing service 1416 to perform supercomputing or other high-performance computing (HPC) tasks. In at least one embodiment, one or more computing services 1416 may be utilized to perform parallel processing (e.g., using parallel computing platform 1430) to process data substantially simultaneously through one or more applications and / or one or more tasks of a single application. In at least one embodiment, parallel computing platform 1430 (e.g., NVIDIA's CUDA) may implement general-purpose computing on a GPU (GPGPU) (e.g., GPU / graphics 1422). In at least one embodiment, the software layer of parallel computing platform 1430 may provide access to the GPU's virtual instruction set and parallel computing elements to execute computing kernels. In at least one embodiment, the parallel computing platform 1430 may include memory, and in some embodiments, memory may be shared between and within multiple containers, and / or between and within different processing tasks within a single container. In at least one embodiment, inter-process communication (IPC) calls may be generated for multiple containers and / or multiple processes within containers to enable the use of the same data (e.g., multiple different stages of one or more applications processing the same information) from a shared memory segment of the parallel computing platform 1430. In at least one embodiment, instead of copying data and moving it to different locations in memory (e.g., read / write operations), the same data in the same memory location can be used for any number of processing tasks (e.g., at the same time, at different times, etc.). In at least one embodiment, this information about the new location of the data can be stored and shared between applications because the resulting data from processing is used to generate new data. In at least one embodiment, the location of the data, and the location of the updated or modified data, may be part of the definition of how the payload in the container is understood.

[0163] In at least one embodiment, one or more AI services 1418 may be used to perform inference services for executing machine learning models associated with the application (e.g., tasks for performing one or more processing tasks of the application). In at least one embodiment, one or more AI services 1418 may utilize AI system 1424 to execute machine learning models (e.g., neural networks such as CNNs) for segmentation, reconstruction, object detection, feature detection, classification, and / or other inference tasks. In at least one embodiment, the application deploying pipeline 1410 may use one or more output models 1316 of the self-training system 1304 and / or other models of the application to perform inference on imaging data. In at least one embodiment, two or more examples of using the application coordination system 1428 (e.g., a scheduler) for inference may be available. In at least one embodiment, a first category may include high-priority / low-latency paths that can implement higher service level protocols, such as for performing inference on urgent requests in emergency situations or for radiologists during diagnostic procedures. In at least one embodiment, a second category may include standard priority paths that can be used for requests that may not be urgent or for situations where analysis can be performed at a later time. In at least one embodiment, the application coordination system 1428 may allocate resources (e.g., services 1320 and / or hardware 1322) based on priority paths for different inference tasks of the AI ​​service 1418.

[0164] In at least one embodiment, shared memory may be installed in one or more AI services 1418 of system 1400. In at least one embodiment, shared memory may operate as a cache (or other storage device type) and may be used to process inference requests from applications. In at least one embodiment, when an inference request is submitted, a set of API instances of deployment system 1306 may receive the request and may select one or more instances (e.g., for best fit, for load balancing, etc.) to process the request. In at least one embodiment, to process the request, the request may be fed into a database, and if not already in the cache, a machine learning model may be located from model registry 1324. A verification step may ensure that an appropriate machine learning model is loaded into the cache (e.g., shared memory), and / or a copy of the model may be saved to the cache. In at least one embodiment, if the application is not already running or there are not enough instances of the application, a scheduler (e.g., the scheduler of pipeline manager 1412) may be used to start the application referenced in the request. In at least one embodiment, if an inference server has not yet been started to execute the model, an inference server may be started. Any number of inference servers may be started for each model. In at least one embodiment, in a pull model that clusters inference servers, the model can be cached whenever load balancing is favorable. In at least one embodiment, the inference servers can be statically loaded into the corresponding distributed servers.

[0165] In at least one embodiment, an inference server running in a container can be used to perform inference. In at least one embodiment, an instance of the inference server can be associated with a model (and optionally multiple versions of the model). In at least one embodiment, if an instance of the inference server does not exist when a request to perform inference on the model is received, a new instance can be loaded. In at least one embodiment, when the inference server is started, a model can be passed to the inference server, allowing the same container to be used to serve different models, as long as the inference server runs as different instances.

[0166] In at least one embodiment, during application execution, an inference request for a given application can be received, and a container (e.g., an instance of a hosted inference server) can be loaded (if not already loaded), and a launcher can be invoked. In at least one embodiment, preprocessing logic within the container can (e.g., using a CPU and / or GPU) load, decode, and / or perform any additional preprocessing on the incoming data. In at least one embodiment, once the data is ready for inference, the container can infer the data as needed. In at least one embodiment, this can include a single inference call for an image (e.g., a hand X-ray) or can request inference for hundreds of images (e.g., a chest CT scan). In at least one embodiment, the application can summarize the results before completion, which may include, but is not limited to, a single confidence score, pixel-level segmentation, voxel-level segmentation, generating visualizations, or generating text to summarize the results. In at least one embodiment, different priorities can be assigned to different models or applications. For example, some models may have a real-time (TAT less than 1 minute) priority, while other models may have a lower priority (e.g., TAT less than 10 minutes). In at least one embodiment, model execution time can be measured from the requesting agency or entity, and may include cooperative network traversal time and inference service execution time.

[0167] In at least one embodiment, the transfer of requests between service 1320 and the inference application can be hidden behind a software development kit (SDK) and robust transfer can be provided via queues. In at least one embodiment, requests are placed in queues via an API for individual application / tenant ID combinations, and the SDK pulls requests from the queues and provides them to the application. In at least one embodiment, the name of the queue can be provided in the environment where the SDK picks up the queue. In at least one embodiment, asynchronous communication via queues may be useful because it allows any instance of the application to pick up work when it becomes available. Results can be sent back via queues to ensure no data loss. In at least one embodiment, queues can also provide the ability to partition work, as the highest priority work can go into a queue connected to a majority of instances of the application, while the lowest priority work can go into a queue connected to a single instance that processes tasks in the order they are received. In at least one embodiment, the application can run on a GPU-accelerated instance generated in cloud 1426, and the inference service can perform inference on the GPU.

[0168] In at least one embodiment, visualization service 1420 can be used to generate visualizations for viewing the output of application and / or deployment pipeline 1410. In at least one embodiment, visualization service 1420 can utilize GPU / graphics 1422 to generate visualizations. In at least one embodiment, visualization service 1420 can implement rendering effects such as ray tracing to generate higher quality visualizations. In at least one embodiment, visualizations can include, but are not limited to, 2D image rendering, 3D volume rendering, 3D volume reconstruction, 2D tomographic slicing, virtual reality display, augmented reality display, etc. In at least one embodiment, a virtualized environment can be used to generate virtual interactive displays or environments (e.g., virtual environments) for system users (e.g., doctors, nurses, radiologists, etc.) to interact with. In at least one embodiment, visualization service 1420 can include an internal visualizer, cinematic and / or other rendering or image processing capabilities or functions (e.g., ray tracing, rasterization, internal optics, etc.).

[0169] In at least one embodiment, hardware 1322 may include GPU / graphics 1422, AI system 1424, cloud 1426, and / or any other hardware for performing training system 1304 and / or deployment system 1306. In at least one embodiment, GPU / graphics 1422 (e.g., NVIDIA's TESLA and / or QUADRO GPUs) may include any number of GPUs that can be used to perform processing tasks for any feature or function of computing service 1416, AI service 1418, visualization service 1420, other services, and / or software 1318. For example, for AI service 1418, GPU / graphics 1422 may be used to perform preprocessing on imaging data (or other data types used by machine learning models), postprocessing on the output of machine learning models, and / or inference (e.g., to execute machine learning models). In at least one embodiment, cloud 1426, AI system 1424, and / or other components of system 1400 may use GPU / graphics 1422. In at least one embodiment, cloud 1426 may include a GPU-optimized platform for deep learning tasks. In at least one embodiment, AI system 1424 may use a GPU, and one or more AI systems 1424 may be used to perform cloud 1426 (or at least part of a task for deep learning or inference). Similarly, although hardware 1322 is shown as a discrete component, this is not intended to be limiting, and any component of hardware 1322 may be combined with or utilized by any other component of hardware 1322.

[0170] In at least one embodiment, AI system 1424 may include a specially built computing system (e.g., a supercomputer or HPC) configured for inference, deep learning, machine learning, and / or other artificial intelligence tasks. In at least one embodiment, in addition to CPU, RAM, memory, and / or other components, features, or functions, AI system 1424 (e.g., NVIDIA's DGX) may also include software (e.g., a software stack) that can be used to perform GPU-optimized tasks using multiple GPUs / graphics 1422. In at least one embodiment, one or more AI systems 1424 may be implemented in a cloud 1426 (e.g., in a data center) to perform some or all of the AI-based processing tasks of system 1400.

[0171] In at least one embodiment, cloud 1426 may include GPU-accelerated infrastructure (e.g., NVIDIA's NGC) that can provide a GPU-optimized platform for performing processing tasks of system 1400. In at least one embodiment, cloud 1426 may include AI system 1424 for performing one or more AI-based tasks of system 1400 (e.g., as a hardware abstraction and scaling platform). In at least one embodiment, cloud 1426 may be integrated with application coordination system 1428 utilizing multiple GPUs to achieve seamless scaling and load balancing between and within applications and services 1320. In at least one embodiment, as described herein, cloud 1426 may be responsible for performing at least some of the services 1320 of system 1400, including one or more computing services 1416, one or more AI services 1418, and / or one or more visualization services 1420. In at least one embodiment, cloud 1426 may perform large and small batch inference (e.g., perform NVIDIA's TENSORRT), provide accelerated parallel computing APIs and platform 1430 (e.g., NVIDIA's CUDA), perform application coordination system 1428 (e.g., KUBERNETES), provide graphics rendering APIs and platform (e.g., for ray tracing, 2D graphics, 3D graphics and / or other rendering techniques to produce higher quality cinematic effects), and / or provide other functionalities for system 1400.

[0172] Figure 15A A data flow diagram of a process 1500 for training, retraining, or updating a machine learning model according to at least one embodiment is shown. In at least one embodiment, a non-limiting example can be used. Figure 14The system 1400 executes the process 1500. In at least one embodiment, the process 1500 may utilize services and / or hardware as described herein. In at least one embodiment, the refined model 1512 generated by the process 1500 may be executed by a deployment system for one or more containerized applications in the deployment pipeline.

[0173] In at least one embodiment, model training 1514 may include retraining or updating the initial model 1504 (e.g., a pre-trained model) using new training data (e.g., new input data, such as customer dataset 1506, and / or new ground reality data associated with the input data). In at least one embodiment, to retrain or update the initial model 1504, the output or loss layer of the initial model 1504 may be reset, deleted, and / or replaced with an updated or new output or loss layer. In at least one embodiment, the initial model 1504 may have previously fine-tuned parameters (e.g., weights and / or biases) retained from previous training, so training or retraining 1514 may not require as much time or processing as training the model from scratch. In at least one embodiment, during model training 1514, when generating predictions on the new customer dataset 1506 by resetting or replacing the output or loss layer of the initial model 1504, the parameters of the new dataset may be updated and readjusted based on the loss calculation associated with the accuracy of the output or loss layer.

[0174] In at least one embodiment, the pre-trained model 1506 may be stored in a data store or registry. In at least one embodiment, the pre-trained model 1506 may have been trained at least partially at one or more facilities other than the facility performing process 1500. In at least one embodiment, to protect the privacy and rights of patients, subjects, or customers at different facilities, the pre-trained model 1506 may have been trained locally using locally generated customer or patient data. In at least one embodiment, the pre-trained model 1306 may be trained using cloud and / or other hardware, but confidential, privacy-protected patient data may not be transferred to, used by, or accessed by any component of the cloud (or other non-local hardware). In at least one embodiment, if the pre-trained model 1506 is trained using patient data from more than one facility, the pre-trained model 1506 may have been trained separately for each facility before training on patient or customer data from another facility. In at least one embodiment, such as when customer or patient data has been published for privacy reasons (e.g., by abandonment, for experimental purposes, etc.), or where customer or patient data is included in a public dataset, customer or patient data from any number of facilities can be used to train a pre-trained model 1506 locally and / or externally, such as in a data center or other cloud computing infrastructure.

[0175] In at least one embodiment, when selecting an application for use in the deployment pipeline, the user may also select a machine learning model for a specific application. In at least one embodiment, the user may not have a model available, so the user may select a pre-trained model to use with the application. In at least one embodiment, the pre-trained model may not be optimized to generate accurate results on the user facility's customer dataset 1506 (e.g., based on patient diversity, demographics, type of medical imaging equipment used, etc.). In at least one embodiment, the pre-trained model may be updated, retrained, and / or fine-tuned for use at various facilities before being deployed into the deployment pipeline for use with one or more applications.

[0176] In at least one embodiment, a user may select a pre-trained model to update, retrain, and / or fine-tune, and the pre-trained model may be referred to as the initial model 1504 of the training system in process 1500. In at least one embodiment, a client dataset 1506 (e.g., imaging data, genomic data, sequencing data, or other data types generated by equipment at the facility) may be used to perform model training (which may include, but is not limited to, transfer learning) on ​​the initial model 1504 to generate a refined model 1512. In at least one embodiment, ground-based data corresponding to the client dataset 1506 may be generated by the training system 1304. In at least one embodiment, ground-based data may be generated at the facility, at least in part, by clinicians, scientists, physicians, or practitioners.

[0177] In at least one embodiment, AI-assisted annotation may be used to generate ground-based data in some examples. In at least one embodiment, AI-assisted annotation (e.g., implemented using an AI-assisted annotation SDK) may leverage machine learning models (e.g., neural networks) to generate suggested or predicted ground-based data for a customer dataset. In at least one embodiment, a user may use the annotation tool within a user interface (graphical user interface (GUI)) on a computing device.

[0178] In at least one embodiment, user 1510 can interact with the GUI via computing device 1508 to edit or fine-tune annotations or automatic annotations. In at least one embodiment, polygon editing features can be used to move the vertices of a polygon to more precise or fine-tuned positions.

[0179] In at least one embodiment, once the customer dataset 1506 has associated ground-based data, the ground-based data (e.g., from AI-assisted annotations, manual labeling, etc.) can be used to generate a refined model 1512 during model training. In at least one embodiment, the customer dataset 1506 can be applied to the initial model 1504 an arbitrary number of times, and the ground-based data can be used to update the parameters of the initial model 1504 until an acceptable level of accuracy is achieved for the refined model 1512. In at least one embodiment, once the refined model 1512 is generated, it can be deployed in one or more deployment pipelines at the facility to perform one or more processing tasks related to medical imaging data.

[0180] In at least one embodiment, the refined model 1512 can be uploaded to a pre-trained model registry for selection by another facility. In at least one embodiment, this process can be performed at any number of facilities, allowing the refined model 1512 to be further refined an arbitrary number of times on a new dataset to generate a more general model.

[0181] Figure 15B This is an example illustration of a client-server architecture 1532 for enhancing an annotation tool using a pre-trained annotation model, according to at least one embodiment. In at least one embodiment, an AI-assisted annotation tool 1536 may be instantiated based on the client-server architecture 1532. In at least one embodiment, the AI-assisted annotation tool 1536 in an imaging application can assist radiologists, for example, in identifying organs and abnormalities. In at least one embodiment, the imaging application may include software tools, as a non-limiting example, that help user 1510 identify several extreme points on a specific organ of interest in a raw image 1534 (e.g., in a 3D MRI or CT scan) and receive automatic annotation results for all 2D slices of that specific organ. In at least one embodiment, the results may be stored in a data store as training data 1538 and used as (e.g., but not limited to) ground-based data for training. In at least one embodiment, when computing device 1508 sends extreme points for AI-assisted annotation, for example, a deep learning model may receive this data as input and return inference results for segmenting organs or abnormalities. In at least one embodiment, a pre-instantiated annotation tool (e.g., Figure 15B The AI-assisted annotation tool 1536 can be enhanced by making API calls (e.g., API call 1544) to a server (such as annotation assistant server 1540), which may include a set of pre-trained models 1542 stored, for example, in an annotation model registry. In at least one embodiment, the annotation model registry may store pre-trained models 1542 (e.g., machine learning models, such as deep learning models) that have been pre-trained to perform AI-assisted annotation on specific organs or anomalies. In at least one embodiment, these models can be further updated using a training pipeline. In at least one embodiment, the pre-installed annotation tool can be improved over time as new labeled data is added.

[0182] Various embodiments may be described by the following terms:

[0183] 1. At least one processor, comprising:

[0184] Processing circuitry, which is used for:

[0185] Generate a first image corresponding to the foreground of the input image using at least one machine learning model; generate one or more second images corresponding to the background of the input image using the at least one machine learning model; and

[0186] This enables the display to generate an animated effect using the first image and at least one of the one or more second images.

[0187] 2. At least one processor according to item 1, wherein the input image is a two-dimensional image.

[0188] 3. The at least one processor according to item 1, wherein the processing circuitry is further configured to:

[0189] Generate a mask associated with the foreground of the first image;

[0190] One or more replacement pixels are generated for the mask using one or more trained neural networks.

[0191] 4. The at least one processor according to item 1, wherein the processing circuitry is further configured to:

[0192] Identify one or more edge regions of a specific second image in the one or more second images; and

[0193] One or more boundary pixels are generated using one or more trained neural networks.

[0194] 5. At least one processor according to item 1, wherein the processing circuitry is further configured to:

[0195] Determine one or more parameters associated with the user; and

[0196] The animation effect is selected based on one or more of the parameters.

[0197] 6. At least one processor according to item 1, wherein the processing circuitry is further configured to:

[0198] Identify one or more components associated with the foreground; and

[0199] Extract one or more components from the input image.

[0200] 7. At least one processor according to item 6, wherein the processing circuitry is further configured to:

[0201] Generate at least one of the one or more second images, the at least one second image including one or more second objects associated with the one or more components in the foreground.

[0202] 8. At least one processor according to item 1, wherein the processing circuitry is further configured to:

[0203] Generate a configuration file associated with the animation effect, the configuration file including at least one of the following: the effect, one or more effect parameters, or identification information for at least one of the first image and the one or more second images.

[0204] 9. The at least one processor according to item 1, wherein the at least one processor is included in at least one of the following:

[0205] A system used to perform simulation operations;

[0206] A system used to perform simulations to test or validate autonomous machine applications;

[0207] Systems used to perform digital twin operations;

[0208] A system for performing optical transmission simulation;

[0209] A system used for rendering graphics output;

[0210] A system used to perform deep learning operations;

[0211] Systems implemented using edge devices;

[0212] Systems used to generate or present virtual reality (VR) content;

[0213] A system for generating or presenting augmented reality (AR) content;

[0214] A system for generating or presenting mixed reality (MR) content;

[0215] A system containing one or more virtual machines (VMs);

[0216] A system used to perform operations for conversational AI applications;

[0217] A system for performing operations for generative AI applications;

[0218] A system for performing operations using a language model;

[0219] A system for performing one or more operations using a large language model LLM;

[0220] A system for performing one or more operations using a visual language model (VLM);

[0221] A system that is at least partially implemented in a data center;

[0222] Systems implemented using robots;

[0223] A system for performing hardware tests using simulation;

[0224] A system for performing one or more generative content operations using a language model;

[0225] Systems for generating synthetic data;

[0226] A collaborative content creation platform for 3D assets; or

[0227] A system that utilizes cloud computing resources at least in part.

[0228] 10. A computer-implemented method, comprising:

[0229] Extract one or more foreground components identified in the input image;

[0230] Generate a first image based on the input image, including at least a portion of the one or more foreground components;

[0231] Determine a mask region associated with one or more foreground components in the input image; generate one or more replacement pixels for the mask region;

[0232] Generate at least one second image based on the input image, including the one or more replacement pixels that replace the mask region; and

[0233] An animation is generated using a specific second image from the first image and the at least one second image. 11. The computer-implemented method according to item 10, further comprising:

[0234] Based on the context of the input image, identify one or more foreground components in the input image.

[0235] 12. The computer-implemented method according to item 10 further includes:

[0236] Determine one or more settings associated with a user requesting access to one or more resources; and based on said one or more settings, select at least one of said animation or said animation attributes.

[0237] 13. The computer-implemented method according to item 10, wherein one or more of the input image, the first image, and the at least one second image are two-dimensional images.

[0238] 14. The computer-implemented method according to item 10 further includes:

[0239] Identify one or more edges of a specific second image within the at least one second image; generate one or more boundary pixels extending beyond the one or more edges of the specific second image; and

[0240] Based on the input image, a specific second image including the one or more boundary pixels is generated.

[0241] 15. The computer-implemented method according to item 10, wherein the one or more replacement pixels are generated using a trained neural network, and further comprising:

[0242] Receive one or more prompts associated with the one or more replacement pixels.

[0243] 16. A system comprising:

[0244] One or more processors are configured to generate an animated image for presentation during a waiting period of an application using a first two-dimensional (2D) image and at least one second 2D image; to generate the first 2D image from an input image associated with the application by extracting one or more foreground objects; and to generate the at least one second 2D image by image repair of a masked region of the input image corresponding to the one or more foreground objects.

[0245] 17. The system according to item 16, wherein the animated image is rendered on the client device during the waiting period based on a received set of configuration settings.

[0246] 18. The system according to item 16, wherein the one or more processors are further configured to receive one or more cues to adjust the trained neural network to perform the image inpainting.

[0247] 19. The system according to item 16, wherein the one or more processors are further configured to extend the at least one second 2D image by adding additional boundary pixels.

[0248] 20. The system according to item 16, wherein the system is included in at least one of the following:

[0249] A system used to perform simulation operations;

[0250] A system used to perform simulations to test or validate autonomous machine applications;

[0251] Systems used to perform digital twin operations;

[0252] A system for performing optical transmission simulation;

[0253] A system used for rendering graphics output;

[0254] A system used to perform deep learning operations;

[0255] Systems implemented using edge devices;

[0256] Systems used to generate or present virtual reality (VR) content;

[0257] A system for generating or presenting augmented reality (AR) content;

[0258] A system for generating or presenting mixed reality (MR) content;

[0259] A system containing one or more virtual machines (VMs);

[0260] A system used to perform operations for conversational AI applications;

[0261] A system for performing operations for generative AI applications;

[0262] A system for performing operations using a language model;

[0263] A system for performing one or more operations using a large language model LLM;

[0264] A system for performing one or more operations using a visual language model (VLM);

[0265] A system that is at least partially implemented in a data center;

[0266] Systems implemented using robots;

[0267] A system for performing hardware tests using simulation;

[0268] A system for performing one or more generative content operations using a language model;

[0269] Systems for generating synthetic data;

[0270] A collaborative content creation platform for 3D assets; or

[0271] A system that utilizes cloud computing resources at least in part.

[0272] Other variations are within the spirit of this disclosure. Therefore, although the disclosed technology is readily adaptable to various modifications and alternative constructions, certain embodiments thereof are illustrated in the accompanying drawings and have been described in detail above. However, it should be understood that the disclosure is not intended to be limited to one or more specific forms disclosed, but rather, it is intended to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of this disclosure as defined in the appended claims.

[0273] Unless otherwise stated or obviously contradicted by the context, the terms “a,” “an,” and “the,” and similar references, used in the context of describing the disclosed embodiments (particularly in the context of the appended claims), should be interpreted as encompassing both singular and plural forms, rather than as definitions of the terms. Unless otherwise stated, the terms “comprising,” “having,” “including,” and “containing” should be interpreted as open-ended terms (meaning “including, but not limited to”). The term “connection” (referring to a physical connection where not modified) should be interpreted as partially or wholly contained, attached to, or joined together, even with some intervention. Unless otherwise indicated herein, references to numerical ranges herein are intended only as a way of abbreviating each individual value falling within that range, and each individual value is incorporated into the specification as if it were separately described herein. Unless otherwise indicated or contradicted by the context, the use of the terms “set” (e.g., “item set”) or “subset” should be interpreted as a non-empty set comprising one or more members. Furthermore, unless otherwise indicated or contradicted by the context, the term "subset" of a corresponding set does not necessarily refer to an appropriate subset of the corresponding set, but rather the subset and the corresponding set can be equal.

[0274] Unless otherwise explicitly stated or clearly contradicted by the context, connective phrases such as “at least one of A, B, and C” or “at least one of A, B, and C” are understood in the context to generally refer to items, terms, etc., which can be A or B or C, or any non-empty subset of the set A, B, and C. For example, in an illustrative example of a set with three members, the connective phrases “at least one of A, B, and C” and “at least one of A, B, and C” refer to any of the following sets: {A}, {B}, {C}, {A, B}, {A, C}, {B, C}, {A, B, C}. Therefore, such connective language is generally not intended to imply that some embodiments require the presence of at least one of A, at least one of B, and at least one of C. Additionally, unless otherwise stated or contradicted by the context, the term “multiple” indicates a plural state (e.g., “multiple items” means multiple items). The number of items in a multiple item is at least two, but may be more if explicitly indicated or indicated by the context. Furthermore, unless otherwise stated or clearly understood from the context, the phrase “based on” means “at least partially based on” rather than “based on only”.

[0275] Unless otherwise indicated herein or clearly contradicted by the context, the operations of the processes described herein may be performed in any suitable order. In at least one embodiment, processes such as those described herein (or variations thereof and / or combinations thereof) are executed under the control of one or more computer systems configured with executable instructions and are implemented as code (e.g., executable instructions, one or more computer programs, or one or more application programs) that is executed jointly on one or more processors via hardware or a combination thereof. In at least one embodiment, the code is stored on a computer-readable storage medium, for example, in the form of a computer program comprising a plurality of instructions executable by one or more processors. In at least one embodiment, the computer-readable storage medium is a non-transitory computer-readable storage medium that excludes transient signals (e.g., propagating transient electrical or electromagnetic transmissions) but includes non-transitory data storage circuitry (e.g., buffers, caches, and queues). In at least one embodiment, code (e.g., executable code or source code) is stored on one or more non-transitory computer-readable storage media (or other memory for storing executable instructions) on which executable instructions are stored, which, when executed by one or more processors of a computer system (i.e., as a result of execution), cause the computer system to perform the operations described herein. In at least one embodiment, the set of non-transitory computer-readable storage media comprises multiple non-transitory computer-readable storage media, and one or more of the individual non-transitory storage media lack all the code, but the multiple non-transitory computer-readable storage media collectively store all the code. In at least one embodiment, the executable instructions are executed such that different instructions are executed by different processors; for example, the non-transitory computer-readable storage media store the instructions, and the main central processing unit (“CPU”) executes some instructions while the graphics processing unit (“GPU”) executes other instructions. In at least one embodiment, different components of the computer system have separate processors, and the different processors execute different subsets of the instructions.

[0276] Therefore, in at least one embodiment, the computer system is configured to implement one or more services that perform the operations of the processes described herein, either individually or collectively, and such a computer system is configured with suitable hardware and / or software to enable the implementation of the operations. Furthermore, the computer system implementing at least one embodiment of this disclosure is a single device, and in another embodiment it is a distributed computer system comprising multiple devices operating in different ways, such that the distributed computer system performs the operations described herein, and that a single device does not perform all the operations.

[0277] The use of any and all examples or exemplary language (e.g., “such as”) provided herein is intended only to better illustrate embodiments of this disclosure and does not constitute a limitation on the scope of the disclosure unless otherwise required. No language in the specification should be construed as indicating that any unclaimed element is essential to the practice of the disclosure.

[0278] All references cited in this article, including publications, patent applications and patents, are incorporated herein by reference as if each reference were individually and specifically indicated to be incorporated herein by reference and the entire contents of which are described herein.

[0279] The terms “coupled” and “connected”, and their derivatives, may be used in the specification and claims. It should be understood that these terms may not be intended to be synonyms with each other. Rather, in certain examples, “connected” or “coupled” may be used to indicate that two or more elements are in direct or indirect physical or electrical contact with each other. “Coupled” may also mean that two or more elements are not in direct contact with each other, but still cooperate or interact with each other.

[0280] Unless otherwise expressly stated, it will be understood that throughout this specification, terms such as “processing,” “computing,” “determining,” etc., refer to the actions and / or processes of a computer or computing system or similar electronic computing device that process and / or convert data represented as physical quantities (e.g., electrons) in the registers and / or memory of the computing system into other data represented as physical quantities in the memory, registers, or other such information storage, transmission, or display devices of the computing system.

[0281] In a similar manner, the term "processor" can refer to any device or part of memory that processes electronic data from registers and / or memory and converts that electronic data into other electronic data that can be stored in registers and / or memory. As a non-limiting example, a "processor" can be a CPU or a GPU. A "computing platform" can include one or more processors. As used herein, a "software" process can include, for example, software and / or hardware entities that perform work over time, such as tasks, threads, and intelligent agents. Similarly, each process can refer to multiple processes that execute instructions sequentially or intermittently, sequentially, or in parallel. The terms "system" and "method" are used interchangeably herein, provided that a system can embody one or more methods, and a method can be considered a system.

[0282] This document refers to the process of acquiring, obtaining, receiving, or inputting analog or digital data into a subsystem, computer system, or computer-implemented machine. Analog and digital data can be acquired, obtained, received, or input in various ways, such as by receiving data as a parameter to a function call or a call to an application programming interface (API). In some implementations, the process of acquiring, obtaining, receiving, or inputting analog or digital data can be accomplished by transmitting data via a serial or parallel interface. In another implementation, the process of acquiring, obtaining, receiving, or inputting analog or digital data can be accomplished by transmitting data from a providing entity to an acquiring entity via a computer network. Reference can also be made to providing, outputting, transmitting, sending, or presenting analog or digital data. In various examples, the process of providing, outputting, transmitting, sending, or presenting analog or digital data can be implemented by transmitting data as an input or output parameter to a function call, an API, or an inter-process communication mechanism.

[0283] While the discussion above illustrates example implementations of the described technologies, other architectures can be used to implement the described functionality and are intended to fall within the scope of this disclosure. Furthermore, although specific assignments of responsibilities have been defined above for discussion purposes, various functions and responsibilities can be assigned and divided in different ways depending on the circumstances.

[0284] Furthermore, although the subject matter has been described in language specific to structural features and / or methodological actions, it should be understood that the subject matter claimed in the appended claims is not necessarily limited to the specific features or actions described. Rather, specific features and actions are disclosed as exemplary forms for implementing the claims.

Claims

1. At least one processor, comprising: Processing circuitry, which is used for: Use at least one machine learning model to generate a first image corresponding to the foreground of the input image; The at least one machine learning model is used to generate one or more second images corresponding to the background of the input image; as well as This enables the display to generate an animated effect using the first image and at least one of the one or more second images.

2. The at least one processor according to claim 1, wherein the input image is a two-dimensional image.

3. The at least one processor according to claim 1, wherein the processing circuitry is further configured to: Generate a mask associated with the foreground of the first image; One or more replacement pixels are generated for the mask using one or more trained neural networks.

4. The at least one processor according to claim 1, wherein the processing circuitry is further configured to: Identify one or more edge regions of a specific second image in the one or more second images; and One or more boundary pixels are generated using one or more trained neural networks.

5. The at least one processor according to claim 1, wherein the processing circuitry is further configured to: Determine one or more parameters associated with the user; and The animation effect is selected based on one or more of the parameters.

6. The at least one processor according to claim 1, wherein the processing circuitry is further configured to: Identify one or more components associated with the foreground; and Extract one or more components from the input image.

7. The at least one processor according to claim 6, wherein the processing circuitry is further configured to: Generate at least one of the one or more second images, the at least one second image including one or more second objects associated with the one or more components in the foreground.

8. The at least one processor according to claim 1, wherein the processing circuitry is further configured to: Generate a configuration file associated with the animation effect, the configuration file including at least one of the following: the effect, one or more effect parameters, or identification information for at least one of the first image and the one or more second images.

9. The at least one processor according to claim 1, wherein the at least one processor is included in at least one of the following: A system used to perform simulation operations; A system used to perform simulations to test or validate autonomous machine applications; Systems used to perform digital twin operations; A system for performing optical transmission simulation; A system used for rendering graphics output; A system used to perform deep learning operations; Systems implemented using edge devices; Systems used to generate or present virtual reality (VR) content; A system for generating or presenting augmented reality (AR) content; A system for generating or presenting mixed reality (MR) content; A system containing one or more virtual machines (VMs); A system used to perform operations for conversational AI applications; A system for performing operations for generative AI applications; A system for performing operations using a language model; A system for performing one or more operations using a large language model LLM; A system for performing one or more operations using a visual language model (VLM); A system that is at least partially implemented in a data center; Systems implemented using robots; A system for performing hardware tests using simulation; A system for performing one or more generative content operations using a language model; Systems for generating synthetic data; A collaborative content creation platform for 3D assets; or A system that utilizes cloud computing resources at least in part.

10. A computer-implemented method, comprising: Extract one or more foreground components identified in the input image; Generate a first image based on the input image, including at least a portion of the one or more foreground components; Determine the mask region associated with the one or more foreground components in the input image; Generate one or more replacement pixels for the masked region; Generate at least one second image based on the input image, including the one or more replacement pixels that replace the mask region; as well as An animation is generated using the first image and a specific second image from the at least one second image.

11. The computer-implemented method according to claim 10, further comprising: Based on the context of the input image, identify one or more foreground components in the input image.

12. The computer-implemented method according to claim 10, further comprising: Determine one or more settings associated with a user requesting access to one or more resources; as well as Based on one or more of the settings, select at least one of the animation or the properties of the animation.

13. The computer-implemented method of claim 10, wherein one or more of the input image, the first image, and the at least one second image are two-dimensional images.

14. The computer-implemented method according to claim 10, further comprising: Identify one or more edges of a specific second image within the at least one second image; Generate one or more boundary pixels that extend beyond the one or more edges of the specific second image; and Based on the input image, a specific second image including the one or more boundary pixels is generated.

15. The computer-implemented method of claim 10, wherein the one or more replacement pixels are generated using a trained neural network, and further comprising: Receive one or more prompts associated with the one or more replacement pixels.

16. A system comprising: One or more processors are used to generate animated images for presentation during the application's waiting period, using a first 2D image and at least one second 2D image. Used to generate the first 2D image from an input image associated with the application by extracting one or more foreground objects; And for generating the at least one second 2D image by performing image inpainting on a masked region of the input image corresponding to the one or more foreground objects.

17. The system of claim 16, wherein the animated image is rendered on the client device during the waiting period based on a received set of configuration settings.

18. The system of claim 16, wherein the one or more processors are further configured to receive one or more cues to adjust the trained neural network to perform the image restoration.

19. The system of claim 16, wherein the one or more processors are further configured to extend the at least one second 2D image by adding additional boundary pixels.

20. The system according to claim 16, wherein, The system is included in at least one of the following: A system used to perform simulation operations; A system used to perform simulations to test or validate autonomous machine applications; Systems used to perform digital twin operations; A system for performing optical transmission simulation; A system used for rendering graphics output; A system used to perform deep learning operations; Systems implemented using edge devices; Systems used to generate or present virtual reality (VR) content; A system for generating or presenting augmented reality (AR) content; A system for generating or presenting mixed reality (MR) content; A system containing one or more virtual machines (VMs); A system used to perform operations for conversational AI applications; A system for performing operations for generative AI applications; A system for performing operations using a language model; A system for performing one or more operations using a large language model LLM; A system for performing one or more operations using a visual language model (VLM); A system that is at least partially implemented in a data center; Systems implemented using robots; A system for performing hardware tests using simulation; A system for performing one or more generative content operations using a language model; Systems for generating synthetic data; A collaborative content creation platform for 3D assets; or A system that utilizes cloud computing resources at least in part.

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