Abstract: A computer-implemented method of dynamically determining when to broadcast video content. The method includes providing one or more content parameters corresponding to broadcasted video content to a machine learning (ML) model, iteratively training the ML model to identify relationships between the one or more content parameters and viewership ratings associated with the broadcasted video content, receiving one or more content parameters corresponding to unaired video content, wherein the one or more content parameters include a desired platform for broadcasting the unaired video content, receiving viewership data associated with the unaired video content and/or the desired platform, providing the one or more content parameters and the viewership data to the trained ML model, and determining, via the trained ML model, a target time period for broadcasting the unaired video content on the desired platform that maximizes a projected viewership rating of the unaired video content.

Abstract: This application relates to systems and methods for generating desired or optimized event schedules. An example computer-implemented method of dynamically generating an event schedule includes: receiving one or more parameters for a series of live events to be held in a plurality of geographic regions; generating a schedule for the series of live events based on the one or more parameters; and automatically updating the schedule based on a change to the one or more parameters.

Abstract: This application relates to systems and methods for generating desired or optimized event schedules. An example computer-implemented method of dynamically generating an event schedule includes: receiving one or more parameters for a series of live events to be held in a plurality of geographic regions; generating a schedule for the series of live events based on the one or more parameters; and automatically updating the schedule based on a change to the one or more parameters.

Abstract: A computer-implemented method of projecting a utility load demand including providing vehicle parameters for a plurality of areas and location parameters associated with the plurality of areas to a machine learning (ML) model, iteratively training the ML model to identify relationships between the vehicle parameters, the location parameters, and historical utility data associated with the plurality of areas, receiving a target area and a future target date, providing the target area and the future target date to the trained ML model, obtaining and providing target vehicle parameters and target location parameters for the target area to the trained ML model, determining, via the trained ML model, an EV charging forecast for the target area at the future target date and projecting, via the trained ML model, a utility load demand within the target area at the future target date based on the EV charging forecast.

Abstract: A computer-implemented method of dynamically generating a charger map for electric vehicle (EV) charger locations. The method includes providing one or more parameters for a plurality of target areas to a machine learning (ML) model. The ML model is iteratively trained to identify relationships between the parameters using historical data corresponding to the plurality of target areas. One or more target parameters for a user-specific target area are received along with one or more user-specific weights representing one or more prioritized charging features associated with the user-specific target area. A charger map is generated via the trained ML model for the user-specific target area including one or more locations for EV chargers within the user-specific target area. The charger map is optimized relative to the one or more prioritized charging features.

Abstract: A computer-implemented method of dynamically determining when to broadcast video content. The method includes providing one or more content parameters corresponding to broadcasted video content to a machine learning (ML) model, iteratively training the ML model to identify relationships between the one or more content parameters and viewership ratings associated with the broadcasted video content, receiving one or more content parameters corresponding to unaired video content, wherein the one or more content parameters include a desired platform for broadcasting the unaired video content, receiving viewership data associated with the unaired video content and/or the desired platform, providing the one or more content parameters and the viewership data to the trained ML model, and determining, via the trained ML model, a target time period for broadcasting the unaired video content on the desired platform that maximizes a projected viewership rating of the unaired video content.

Abstract: This application relates to systems and methods for generating desired or optimized event schedules. An example computer-implemented method of dynamically generating an event schedule includes: receiving one or more parameters for a series of live events to be held in a plurality of geographic regions; generating a schedule for the series of live events based on the one or more parameters; and automatically updating the schedule based on a change to the one or more parameters.

Abstract: This application relates to systems and methods for generating desired or optimized event schedules. An example computer-implemented method of dynamically generating an event schedule includes: receiving one or more parameters for a series of live events to be held in a plurality of geographic regions; generating a schedule for the series of live events based on the one or more parameters; and automatically updating the schedule based on a change to the one or more parameters.

Abstract: This application relates to systems and methods for generating desired or optimized event schedules. An example computer-implemented method of dynamically generating an event schedule includes: receiving ogle or more parameters for a series of live events to be held in a plurality of geographic regions; generating a schedule for the series of live events based on the one or more parameters; and automatically updating the schedule based on a change to the one or more parameters.

Abstract: This application relates to systems and methods for generating desired or optimized event schedules. An example computer-implemented method of dynamically generating an event schedule includes: receiving ogle or more parameters for a series of live events to be held in a plurality of geographic regions; generating a schedule for the series of live events based on the one or more parameters; and automatically updating the schedule based on a change to the one or more parameters.

Abstract: This application relates to systems and methods for generating desired or optimized event schedules. An example computer-implemented method of dynamically generating an event schedule includes: receiving one or more parameters for a series of live events to be held in a plurality of geographic regions; generating a schedule for the series of live events based on the one or more parameters; and automatically updating the schedule based on a change to the one or more parameters.

Abstract: This application relates to systems and techniques for automatically and/or dynamically generating a network map. The network map can be updated based on a changed condition, which can include, among other things, a change in the underlying schedule and/or underlying data (e.g., weather data, news data, etc.). The network map can be updated in real time and/or in accordance with a predetermined schedule.

Abstract: This application relates to systems and techniques for automatically and/or dynamically generating a network map. The network map can be updated based on a changed condition, which can include, among other things, a change in the underlying schedule and/or underlying data (e.g., weather data, news data, etc.). The network map can be updated in real time and/or in accordance with a predetermined schedule.

Abstract: This application relates to systems and techniques for automatically and/or dynamically generating a network map. The network map can be updated based on a changed condition, which can include, among other things, a change in the underlying schedule and/or underlying data (e.g., weather data, news data, etc.). The network map can be updated in real time and/or in accordance with a predetermined schedule.