The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and efficiently predicting conversion probability scores and key personas for target entities utilizing an artificial intelligence approach. For example, the disclosed systems utilize a conversion activity score neural network to predict conversion activity probability scores for target entities and utilize a persona prediction machine learning model to predict key personas associated with target entities. In particular, the disclosed systems utilize the conversion activity score neural network to generate a predicted conversion activity probability score for a target entity from input data including client device interactions of digital profiles belonging to the target entity as well as an entity feature vector representing characteristics of the target entity. The disclosed systems also (or alternatively) utilize a persona prediction machine learning model to determine a set of key personas for the target entity from the entity feature vector.

According to one implementation, an automated audio mapping system includes a computing platform having a hardware processor and a system memory storing an audio mapping software code including an artificial neural network (ANN) trained to identify multiple different audio content types. The hardware processor is configured to execute the audio mapping software code to receive content including multiple audio tracks, and to identify, without using the ANN, a first music track and a second music track of the multiple audio tracks. The hardware processor is further configured to execute the audio mapping software code to identify, using the ANN, the audio content type of each of the multiple audio tracks except the first music track and the second music track, and to output a mapped content file including the multiple audio tracks each assigned to a respective one predetermined audio channel based on its identified audio content type.

In some embodiments, a method inputs a sequence of historical behaviors for a plurality of instances of content into a prediction network to generate a sequence of values that model the sequence of historical behaviors. A restriction on an operation performed by the prediction network is based on a characteristic of an viewing behavior. A sequence of attention scores is generated based on a similarity of a current behavior for a first instance of content to respective instances of historical behaviors in the sequence of historical behaviors. The method adjusts respective values based on corresponding attention scores to generate an adjusted sequence of values. The adjusted sequence of features are sampled to generate an output from the prediction network that models the sequence of historical behaviors based on the current behavior. The output for determining a prediction if the current behavior is indicative of the viewing behavior.

Systems and methods are described herein for providing content item recommendations based on a video. Using feature vectors corresponding to at least one frame of a video (e.g., generated based on texture and shape intensity of a frame), a recommendation system improves content recommendation using analytic and quantitative characteristics derived from a frame of a content item rather than merely manually labeled bibliographic data (e.g., a genre or producer). The recommendation system may generate a feature vector based on a texture, a shape intensity (e.g., generated from a Generalized Hough Transform), and temporal data corresponding to at least one frame of a video. The feature vector is analyzed using a machine learning model (e.g., a neural network) to produce a machine learning model output. The recommendation system causes a recommended content item to be provided based on the machine learning model output.

Systems and methods are described herein for recommending content based on a mood score associated with a user profile. The system accesses the viewing history of the user profile to determine media assets consumed and the mood score associated with each of the consumed media assets of the plurality of media assets. A cumulative mood score is calculated based at least in part to determine if the total score is below a mood threshold. Based on the cumulative mood score being lower than the mood threshold, the system generates for presentation, on the consumer device, one or more media assets with a positive mood score.

A method and apparatus for deep content tagging. A media device receives one or more first frames of a content item, where the one or more first frames spans a duration of a scene in the content item. The media device detects one or more objects or features in each of the first frames using a neural network model and identifies one or more first genres associated with the first frames based at least in part on the detected objects or features in each of the first frames. The media device further controls playback of the content item based at least in part on the identified first genres.

Aspects of the subject disclosure may include, for example, identifying content consumption data associated with media content consumption at a customer device, and generating a content selection recommendation for the customer device. Some embodiments can include determining a habit-based content selection vector for the customer device. Various embodiments can include determining the habit-based content selection vector based on a habit profile for the customer device. Some embodiments can include adjusting a content selection vector for the customer device based on the habit-based content selection vector for the customer device. Various embodiments can include generating the content selection recommendation for the customer device based on the adjusted content selection vector. Other embodiments are disclosed.

Systems and methods are described herein for curating content that follows a narrative structure. A narrative structure comprises narrative portions that have a defined order. Signature analysis of known content that follows the narrative structure is used to train machine learning models for the narrative structure and the narrative portions that make up the narrative structure. Signature analysis of candidate content segments, along with machine learning models for the narrative portions, are used to identify candidate content segments that match the respective narrative portions. A candidate playlist is generated of the identified candidate content segments in the defined order. In one embodiment, the machine learning model for the narrative structure is used to validate the generated playlist.

An information processing apparatus that processes information on the basis of a gaze degree of a user who views content is provided.

An information processing apparatus includes: an estimation unit that estimates a gaze degree of a user who views content; an acquisition unit that acquires related information to content recommended to the user; and a control unit that controls a user interface that presents the related information on the basis of an estimation result of the gaze degree. The acquisition unit acquires the related information by using an artificial intelligence model that has learned a causal relationship between information on a user and content in which a user shows interest.

A method for identifying a product which appears in a video stream. The method includes playing the video stream on a video playback device, identifying key scenes in the video stream containing product images, selecting product images identified by predetermined categories of trained neural-network object identifiers stored in training datasets. Object identifiers of identified product images are stored in a database. Edge detection and masking is then performed based on at least one of shape, color and perspective of the object identifiers. A polygon annotation of the object identifiers is created using the edge detection and masking. The polygon annotation is annotated to provide correct object identifier content, accuracy of polygon shape, title, description and URL of the object identifier for each identified product image corresponding to the stored object identifiers. Also disclosed is a method for an end user to select and interact with an identified product.