Abstract: In various embodiments, machine learning-based custom content generation for video streaming content systems and applications is provided. One or more of the embodiments described herein, among other things, provide for a machine learning/generative artificial intelligence (GAI)-based custom video generator model that may be used to synthesize targeted video streaming content (e.g., in real-time), based at least in part on user profile data from a user profile associated with a target user, and content data comprising an indication of one or more video content objectives directed at the target user. In some embodiments, a custom video content generator as disclosed herein may produce one or more prompts based on a set of content data and the user profile data, and apply the prompts to the video generator model to generate targeted video data that may be streamed to user equipment for presentation to the target user.

Abstract: Systems and methods for machine learning-based contextual customization of on-demand video streaming content are provided. A video context adaption engine may adjust one or more elements of video content data based on selected context profiles. A context profile may represent a set of characteristics that defines circumstances and/or features that form the setting for events, scenes, dialogue, actions, and other plot devices appearing in a work of video content. The video content data may be input as a prompt to a generative artificial intelligence (AI) machine learning model that outputs customized video content data that comprises an update or modification to one or more elements of content within the video content data based on the selected context profile(s). The customized video content data may then be served to a client application for presentation on user equipment.

Abstract: Systems and methods for machine learning-based contextual customization of on-demand video streaming content are provided. A video context adaption engine may adjust one or more elements of video content data based on selected context profiles. A context profile may represent a set of characteristics that defines circumstances and/or features that form the setting for events, scenes, dialogue, actions, and other plot devices appearing in a work of video content. The video content data may be input as a prompt to a generative artificial intelligence (AI) machine learning model that outputs customized video content data that comprises an update or modification to one or more elements of content within the video content data based on the selected context profile(s). The customized video content data may then be served to a client application for presentation on user equipment.

Abstract: Systems and methods for machine learning-based contextual customization of on-demand video streaming content are provided. A video context adaption engine may adjust one or more elements of video content data based on selected context profiles. A context profile may represent a set of characteristics that defines circumstances and/or features that form the setting for events, scenes, dialogue, actions, and other plot devices appearing in a work of video content. The video content data may be input as a prompt to a generative artificial intelligence (AI) machine learning model that outputs customized video content data that comprises an update or modification to one or more elements of content within the video content data based on the selected context profile(s). The customized video content data may then be served to a client application for presentation on user equipment.

Abstract: In various embodiments, machine learning-based custom content generation for video streaming content systems and applications is provided. One or more of the embodiments described herein, among other things, provide for a machine learning/generative artificial intelligence (GAI)-based custom video generator model that may be used to synthesize targeted video streaming content (e.g., in real-time), based at least in part on user profile data from a user profile associated with a target user, and content data comprising an indication of one or more video content objectives directed at the target user. In some embodiments, a custom video content generator as disclosed herein may produce one or more prompts based on a set of content data and the user profile data, and apply the prompts to the video generator model to generate targeted video data that may be streamed to user equipment for presentation to the target user.

Abstract: In various embodiments, machine learning-based customization for video stream content delivery systems and applications are provided. In some embodiments, a machine learning model-based content customization engine may modify in real-time how user-selected elements of content are presented at the user's equipment (UE). A request for video content from a UE may include user content selection data and user customization data. The user content selection data is used to initiate streaming of a selected title of video content from a content server, and the user customization data is used as the basis to modify selected elements of streaming video content prior to display by the UE. Video content data from the content server and user customization data may be input to a generative artificial intelligence (GAI) model that outputs customized video content data where one or more elements of content are modified based on the user customization data.

Abstract: This document describes techniques, apparatuses, and systems for autonomous delivery to a dynamic location. A user requests delivery of an item. An autonomous vehicle carrying the item navigates to a delivery location associated with the request. While navigating the autonomous vehicle toward the delivery location, the autonomous vehicle receives one or more estimates of a current location of a wireless device of the user. In response to receiving a respective estimate, the autonomous vehicle updates the delivery location to match the respective estimate of the current location of the wireless device. In this way, the delivery location can be dynamically updated as the user changes location. Once the autonomous vehicle reaches the delivery location, the item can be delivered to the user.

Abstract: Methods are provided for delivering goods to a connected vehicle using unmanned aerial vehicles (UAVs). Upon receiving a request comprising instructions to deliver a package to a customer, a connection is established between the connected vehicle paired to a user device and the UAV authorized to deliver the package to an interface of the connected vehicle. The connection enables data corresponding to the connected vehicle to be communicated to the UAV. In aspect, the data comprises speed and direction of the connected vehicle allowing an optimal route to intercept the connected vehicle to be determined.

Abstract: This document describes techniques, apparatuses, and systems for autonomous delivery to a dynamic location. A user requests delivery of an item. An autonomous vehicle carrying the item navigates to a delivery location associated with the request. While navigating the autonomous vehicle toward the delivery location, the autonomous vehicle receives one or more estimates of a current location of a wireless device of the user. In response to receiving a respective estimate, the autonomous vehicle updates the delivery location to match the respective estimate of the current location of the wireless device. In this way, the delivery location can be dynamically updated as the user changes location. Once the autonomous vehicle reaches the delivery location, the item can be delivered to the user.

Abstract: Techniques for securing a single page application (SPA) are provided. An API server that receives an API call from an SPA to navigate a first user to a first state of the SPA navigates the first user to the first state of the SPA and generates a first token indicating that the first user has accessed the first state of the SPA. When the API server receives a request from the first user to navigate to a second state of the SPA (the request including the first token), the API server verifies that the first token indicating that the first user has accessed the first state of the SPA is valid for the second state of the SPA before navigating the first user to the second state of the SPA. Additionally, the API server expires the first token upon navigating the first user to the second state of the SPA.

Abstract: Techniques for securing a single page application (SPA) are provided. An API server that receives an API call from an SPA to navigate a first user to a first state of the SPA navigates the first user to the first state of the SPA and generates a first token indicating that the first user has accessed the first state of the SPA. When the API server receives a request from the first user to navigate to a second state of the SPA (the request including the first token), the API server verifies that the first token indicating that the first user has accessed the first state of the SPA is valid for the second state of the SPA before navigating the first user to the second state of the SPA. Additionally, the API server expires the first token upon navigating the first user to the second state of the SPA.