Abstract: A system and a method for automatically identifying key dialogues in media is disclosed herein. In the method disclosed herein, the key dialogues engine receives the media asset and extract transcript data and supplementary data. The key dialogues engine processes the transcript data into a plurality of transcript data elements and associate the transcript data elements with respective data elements selected from the supplementary data.

Abstract: A system including a scene detection engine (SDE) and a method for automatically detecting and marking logical scenes in media content using artificial intelligence are provided. The SDE detects boundaries of shots in media content. The SDE generates an image similarity matrix by extracting color features from a middle frame of each shot. The SDE generates an audio similarity matrix by extracting audio features of each shot. The SDE generates a resultant similarity matrix from the image and audio similarity matrices. The SDE executes a clustering algorithm using the resultant similarity matrix with reduced or no noise to generate an ordered sequence of shots that define a boundary of each scene of the media content. The SDE executes a linear traversal algorithm on the shots that define the boundary of each scene for correcting errors in the defined boundary of each scene.

Abstract: A system including a key moments engine (KME) and a method for automatically identifying and ranking key moments in a media asset. The KME extracts an audio stream from the media asset and stores the audio stream as an audio file. The KME divides the audio file into sub-second audio segments. The KME computes an audio signal level for each of the sub-second audio segments and generates an array of audio signal levels for the audio file. The KME generates clusters of the audio signal levels from the array of audio signal levels. The KME dynamically determines threshold levels for classifying the audio signal levels in the array using the clusters. The KME identifies the key moments from the classified audio signal levels and computes a rank for each of the identified key moments based on ranking criteria.