Abstract: A system and method for ad break detection, including: a computer processor; a scene break detection service executing on the computer processor and comprising functionality to (i) receive a request for ad break detection on a media item, (ii) perform audio break detection on an audio component of the media item to obtain a set of audio break timestamps, (iii) identify a set of video break timestamps, each corresponding to at least one frame of a video component of the media item, (iv) identify a set of candidate ad break timestamps corresponding to instances of the set of the audio break timestamps and the set of video break timestamps within a predefined proximity, (v) execute a computer vision model to generate a scene break score for each candidate ad break timestamp, and (vi) select a final set of ad break timestamps based at least on the scene break scores.

Abstract: Systems and methods for entity detection using artificial intelligence, including: a deep learning model service configured to: select and analyze a set of frames from a media item to determine a set of candidate brand-probability pairs; a voting engine configured to: determining that a first brand-probability pair of a set of candidate brand-probability pairs based on at least one obtained hyperparameter value does not meet a threshold for determining whether candidate brand-probability pairs are to be included in a result set; excluding the first brand-probability pair from the result set based on the determination; sorting the result set; and selecting at least one final brand-probability pair from the result set; and an offline transcoding service configured to: store the final brand-probability pair in a repository with a relation to an identifier of the media item.

Abstract: A system and method for promoting content items in a content platform, including: a computer processor, a content promoter service executing on the computer processor and including functionality to identify an impression budget and a pacing parameter for impressions of an unvetted content item and utilize the impression budget and the pacing parameter to control availability of the unvetted content item for artificial promotion, a content recommender service including functionality to receive a request for content for a container, identify a set of vetted content items based on historical performance, artificially promote the unvetted content item by injecting it into the set of vetted content items and providing the final set of content in response to the request, and a content cold-start service configured to select the unvetted content item as a candidate for injection based on similarity to a surrogate content item in the set of vetted content items.

Abstract: A system and method for augmented semantic search, including: a query execution service including functionality to receive a search request including a query string; a query classification service including functionality to execute a first large language model using a first prompt to generate a classification object representing classification of the query string in a structured classification format; a filter extraction service including functionality to execute a second large language model using a second prompt to generate a filter object including a set of filters inferred for the query string in the structured filter format; and a recaller service including functionality to: use the filter object to constrain search space of a vector store and execute a vector similarity operation on a query vector to generate a match set of embeddings; and provide a result set based on the match set of embeddings.

Abstract: A system and method for augmented semantic search, including: a vector store including a set of embeddings, each representing a structured data representation of a media perspective of a media item; a query execution service including functionality to receive a search request including a query string from a client application; a query classification service including functionality to execute a first machine learning model to generate a classification object in a structured classification format; a filter extraction service including functionality to execute a second machine learning model to generate a filter object including a set of filters in a structured filter format; a recaller service including functionality to: execute an encoder model on the input query and execute a vector similarity operation on the query embedding to generate a result set; and a re-ranking service including functionality to: execute a large language model to re-rank the result set.

Abstract: A system and method for structured data representation of a set of media perspectives, including: an autodata generation system executing on a computer processor and configured to: identify a set of caption data for a set of media items; for each of the set of media items, generate a set of structured data representations of the set of media perspectives, by: (i) generating a prompt including caption data of the media item and definition of a structured data representation of a media perspective, and (ii) executing a large language model using the prompt to generate the structured data representation; and store the structured data representations in a document store; and an indexer service including functionality to: execute an encoder model on the structured data representations to generate a set of embeddings; and store the embeddings in a vector store for execution of semantic search using a vector similarity operation.

Abstract: A system and method for contextual advertising that analyzes video content through multimodal examination of visual, audio, and textual elements to create detailed contextual understanding of individual scenes. The system segments video content into discrete scenes and simultaneously processes each scene to extract contextual characteristics including objects, settings, dialogue, music, and emotional tone. These characteristics are classified according to advertising industry taxonomies and converted into numerical embeddings that enable semantic similarity matching. During video playback, when advertisement opportunities occur, the system identifies the current scene context, analyzes available advertisements using similar techniques, computes similarity scores between scene and advertisement characteristics, and selects contextually appropriate advertisements for seamless integration.

Abstract: A system and method for content recommendation, including: (i) a computer processor, (ii) a recaller module configured to receive a request for a secondary media item, and identify a set of candidate secondary media items associated with the source media item and (iii) a ranking module executing on the computer processor and configured to enable the computer processor to generate, based on the user identifier, a set of user vectors, a set of secondary media vectors, and a set of source media vectors, execute a machine learning model using the vectors to generate a ranking score for each of the secondary media items, wherein executing the machine learning model includes performing a contextual multi-armed bandit algorithm involving exploration and exploitation of candidate items based on historical data, and return a subset of the set of candidate secondary media items in response to the request.

Abstract: A system and method for classifying media content, including: a computer processor and a video extraction and inference engine service executing on the computer processor and including functionality to obtain a video component and an audio component of a media item, perform optical character recognition (OCR) on a subset of frames of the video component, generate processed OCR text, and perform feature extraction on the processed OCR text to generate feature vectors representing the video component; an audio extraction and inference engine including functionality to transcribe the audio component to generate transcribed audio text, and perform feature extraction on the transcribed audio text to generate feature vectors representing the audio component; and a classification model serving engine configured to execute a classification-based machine learning model based on the feature vectors to generate a binary inference indicating the likelihood of the media item being associated with a predefined classificati

Abstract: A system and method for channel layout evaluation are disclosed. A computer processor executes a channel detective service that receives a media item with multiple audio channels and analyzes the channels to build a feature representation capturing characteristics such as dialog, silence, and frequency content. Using the feature representation, the service applies a similarity model to group related channels into a mix group. A metadata representation of the media item is then generated to include the mix group along with a service type annotation, such as main, dub, or description. The metadata representation is output for use in streaming or playback, enabling accurate selection and delivery of the proper audio channels.

Abstract: Systems and methods for entity detection using artificial intelligence, including: a deep learning model service configured to: select and analyze a set of frames from a media item to determine a set of candidate brand-probability pairs; a voting engine configured to: determining that a first brand-probability pair of a set of candidate brand-probability pairs based on at least one obtained hyperparameter value does not meet a threshold for determining whether candidate brand-probability pairs are to be included in a result set; excluding the first brand-probability pair from the result set based on the determination; sorting the result set; and selecting at least one final brand-probability pair from the result set; and an offline transcoding service configured to: store the final brand-probability pair in a repository with a relation to an identifier of the media item.

Abstract: Systems and methods for programmatic generation of training data, including: a training module configured to receive human curation input for brand entity detection, generate hybrid training data by combining programmatic and human generated data, and calculate brand-probability pairs by weighting detection results and human input to improve brand detection accuracy for frequency management; an online media service configured to serve training data to recipients during controlled experiments, calculate quality scores based on performance metrics and human input, and exclude low-quality training data from model training; a model training engine configured to train an artificial intelligence model for brand detection using the hybrid training data weighted by quality scores; and a frequency management service configured to execute the trained model on media items to identify brand identifiers with improved accuracy and regulate serving frequency of brand-associated content to recipients.

Abstract: A system and method for channel layout evaluation including: a computer processor and a channel detective service executing on the computer processor and including functionality to: receive a request to perform channel layout evaluation on a media item including a provided set of channels; perform metadata extraction on the media item to generate a metadata representation of the media item and to identify at least one channel layout discrepancy of the provided set of channels; perform layout detection using the metadata representation by executing a similarity model configured to generate a mix group comprising at least a subset of the provided set of channels, annotating a primary language of the mix group, and annotating a service type of the mix group; and updating the metadata representation with the annotated mix group, wherein the updated metadata representation is provided for streaming the media item.

Abstract: A system and method for channel layout evaluation including: a computer processor and a channel detective service executing on the computer processor and including functionality to: receive a request to perform channel layout evaluation on a media item including a provided set of channels; perform metadata extraction on the media item to generate a metadata representation of the media item and to identify at least one channel layout discrepancy of the provided set of channels; perform layout detection using the metadata representation by executing a similarity model configured to generate a mix group comprising at least a subset of the provided set of channels, annotating a primary language of the mix group, and annotating a service type of the mix group; and updating the metadata representation with the annotated mix group, wherein the updated metadata representation is provided for streaming the media item.

Abstract: Systems and methods for frequency management, including: an online media service configured to (i) receive a request for a media item, the request including a recipient identifier, (ii) identify a set of candidate media items relevant to the recipient, and (iii) obtain a set of cross-device identifiers associated with the recipient identifier, the set corresponding to a household; and a frequency management service configured to (i) identify an aggregate quantity of impressions associated with a candidate media item of the set of candidate media items and the set of cross-device identifiers over a preceding duration of time, (ii) identify a maximum frequency threshold, (iii) determine, based on the aggregate quantity of impressions, that the maximum frequency threshold is exceeded, (iv) exclude the candidate media item from a result set based on the maximum frequency threshold being exceeded, and (v) provide the result set in response to the request.

Abstract: Systems and methods for intuitive search and recommendation including a content comprehension engine executing on a computer processor and configured to: receive a recommendation request identifying a source content item; generate a first embedding for the source content item in a first embedding space from content metadata and contextual data; apply a trained neural projection model to map the first embedding to a second embedding space, thereby producing a projected embedding; compute, for content item models stored in a repository, a similarity score between the projected embedding and the content item model, each content item model including word-vector collaborative-filtering representations of an available content item; select, based on the similarity scores, a subset of the content item models; and output a result set including the available content items corresponding to the subset and ordered by the similarity scores.

Abstract: Systems and methods for programmatic generation of training data, including: a training data generation engine configured to: identify an image asset corresponding to an entity; identify a training video; select a consecutive subset of frames of the training video based on a procedure for ranking frames on their candidacy for overlaying content; for at least one frame of the subset of frames: perform an augmentation technique on the identified logo image to generate an augmented image asset; overlay at least one variation of the image asset, including the augmented image asset, onto each of the subset of frames to generate a set of overlayed frames; and generate an augmented version of the training video including the overlayed frames; and a model training engine configured to: train an artificial intelligence model for entity detection using the augmented version of the training video.

Abstract: Systems and methods for frequency management, including: an online media service configured to receive a request for a media item, the request comprising a recipient identifier of a recipient, and identify a set of candidate media items ranked based at least partially on relevance to the recipient; and a frequency management service configured to: (i) identify a quantity of impressions associated with a first candidate media item of the set of candidate media items and the recipient identifier over a preceding duration of time, (ii) identify a maximum frequency threshold, (iii) determine, based on the quantity of impressions, that the maximum frequency threshold is exceeded, (iv) exclude the first candidate media item from a result set based on the frequency threshold being exceeded, and (v) provide the result set comprising an identifier of a second candidate media item in response to the request.

Abstract: A system for executing thin client applications, the system configured to: generate, by a thin client application executing on a client computing device, an initial content request identifying a platform; send the initial content request to a content provider; receive platform-independent initial content from the content provider in response to the initial content request; display the initial content on the client computing device; detect a user input associated with the initial content; send a user engagement request for secondary content; receive user-interactive secondary content server-side rendered in a platform-dependent format of the client computing device based on the platform identification; and display the secondary content on the display component.

Abstract: System and methods for intuitive search operation results using machine learning including: identifying a first candidate content item matching a content item search request; identifying a first content item model corresponding to the first candidate content item including word vector collaborative filtering representations of the first candidate content item; identifying a set of content item models where each: is associated with at least one corresponding available content item, and includes word vector collaborative filtering representations; applying deep neural learning to compare the first content item model with the set of content item models to generate a subset of the content item models most relevant to the first content item model; generating a result set of available content items corresponding to the subset of the content item models most relevant to the first content item model; and providing the result set of available content items.