Abstract: System, apparatus, article of manufacture, method and/or computer program embodiments are provided for implementing smart video experiences. An example method includes receiving a request to perform a video control operation during playback of a video; based on a type of video control operation of the video control operation and a playback position of the video, selecting one or more video markers for the video control operation from a plurality of video markers associated with respective video frames from a plurality of video frames of the video, the one or more video markers being associated with one or more video frames of the video; selecting the one or more video frames for the video control operation based on the one or more video markers; and generating a signal configured to trigger the video control operation based on the one or more video frames associated with the one or more video markers.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for automatic preview generation for videos. An example embodiment operates by identifying content for which to generate a preview and a plurality of shots within the content. A subset of frames is selected based on a sharpness indicator. An aesthetic score for each of the subset of sharpest frames is generated based both a positive score and a negative score for each frame as generated by a visual language model (VLM). A preview frame is selected, and a preview is generated for the content based on the preview frame. The preview of the content is provided for display.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for determining an optimal supplemental content for a media stream menu interface to maximize the consumption of the media stream content by users. An example embodiment operates by performing automated content recognition (ACR) on the media stream, thereby determining optimal supplemental content. The embodiment identifies a plurality of potential supplemental content items in the media stream based on the characteristics of the media stream. The embodiment then outputs the optimal supplemental content to a plurality of predetermined media devices.

Abstract: System, apparatus, article of manufacture, method and/or computer program embodiments are provided for implementing smart video experiences. An example method includes receiving a request to perform a video control operation during playback of a video; based on a type of video control operation of the video control operation and a playback position of the video, selecting one or more video markers for the video control operation from a plurality of video markers associated with respective video frames from a plurality of video frames of the video, the one or more video markers being associated with one or more video frames of the video; selecting the one or more video frames for the video control operation based on the one or more video markers; and generating a signal configured to trigger the video control operation based on the one or more video frames associated with the one or more video markers.

Abstract: System, apparatus, article of manufacture, method and/or computer program product embodiments (and/or combinations and sub-combinations thereof) are provided for using AI/ML models to generate context-aware metadata for a media content item based on audio-related text data associated with the media content item. An example method can include obtaining text data associated with a content item, the text data including a transcription/translation of audio associated with the content item; determining a modified version of the text data based on a deviation in a playback timeline of the content item, topics associated with the text data, a chronological timeline associated with the content item, and/or a sequence of events associated with the content item and/or content of the content item; generating a representation of the modified version of the text data; and generating metadata associated with the content item based on the representation of the modified version of the text data.

Abstract: In one aspect, an example method includes (i) determining, by a computing system, an extent of a user's understanding of one or more educational topics; (ii) using, by the computing system, at least the determined extent of the user's understanding of one or more educational topics to generate a personalized curriculum for the user; (iii) using, by the computing system, at least the generated personalized curriculum and one or more trained machine learning (ML) models, to generate personalized educational media content for the user; and (iv) performing, by the computing system, a set of operations to facilitate outputting for presentation via a user interface, the generated personalized educational media content for the user.

Abstract: Aspects of the disclosed technology provide solutions for dynamically controlling user access to computing resources on a need-by-need basis. An example method can include receiving an access request from a user. The access request may specify one or more computing resources to be accessed by the user. The example method further includes retrieving a user profile associated with the user, identifying a policy document specifying one or more user rights policies for the one or more computing resources, and determining, using a machine learning model, whether to grant or deny the access request based on the user profile and the policy document.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for classifying ad break markers. An example method can include receiving a media stream comprising audio data and video data, wherein the media stream includes at least one ad break marker; obtaining closed caption data corresponding to the media stream; and determining a classification for the at least one ad break marker based on the closed caption data.

Abstract: Disclosed herein are computing system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for determining and utilizing sentiments of content items when translating between languages. For example, a computing system may be configured to, for a first content item, obtain text data, audio data, and video data. In some examples, the text data and audio data may be associated with a first language. Additionally, the computing system may be configured to determine a sentiment score associated with a first scene of the first content item based corresponding portions of text data, the audio data and the video data. Further, the computing system may be configured to generate a translation of the first scene based on the sentiment score.

Abstract: There is significant manual work in analyzing product reviews. Even if there are sufficient resources, human reviews can be inconsistent and subjective. A product review analysis platform leveraging engineered prompts and a large language model can address some of these issues. The platform includes a pipeline to summarize reviews, produce sentiment scores to rating categories, detect negative sentiment, extract main categories tags, extract sub-categories tags within a main categories tag, and produce weekly summaries. A dashboard can be included to visualize the enriched reviews. In some cases, synthetic users may fill in data gaps. An action recommendation engine can be included to determine appropriate resolutions. In some cases, the feature vectors generated by the large language model in response to receiving an engineered prompt can be stored in a vector database along with appropriate resolutions, such that incoming reviews can be routed appropriately using the vector database.