viisights has created a unique technology for automatic extraction of meaningful data from any kind of video content. Our technology is based on a combination of approaches from such fields as artificial intelligence, computer vision, deep learning and natural language processing. viisights platform automatically splits any video content into scenes, for each scene it extracts the textual, vocal and visual elements and applies advanced machine learning algorithms for understanding the data semantics. All contextual knowledge that is extracted and generated is ranked according to its importance and intensity. When the user profile is available to viisights, the personalization engine figures out what content scenes are most relevant for the user, how and when to target a personal communication and what content should be recommended for the user to watch next.
The Video Processing Engine (VPE) automatically analyzes a video content or a live stream for understanding its content. The engine output is a Video Content Descriptive Language (VCDL) that describes what actually is shown and what is the atmosphere (joy, fear) in the various scenes.
The system gets an inventory of video clips, processes them and produces tags which describe the video content. These tags can be in several hierarchies as described below, and can be used for various purposes – to analyze the inventory, as part of content recommendation system and as part of targeting system.
The major input to the system are the video clips. Optionally, the system can get textual input from the customer system, e.g. speech to text content, first party and third meta-data information (like content title, description, etc.). For the purpose of creating personalized tagging, the system is able to accept, as an additional input, user personal profiles, enrich them and use them for personalized tagging. If no user profile data is provided the system create a new user profile based on the it’s contextual and behavioral data.