Abstract: The disclosure provides a method and system for automatically generating video highlights for a video game involving one or more players. To start with, the method and system extracts game-related information or statistics from an in-game event stream and from one or more data sources using a plurality of Application Programming Interfaces (APIs) and analyses the in-game event stream using an AI module. In an ensuing step, the method and system predicts a win probability of the game in real-time based on the game-related information and detects if the win probability of the game fluctuates beyond a predefined threshold using the AI module. In response to detecting that the win probability fluctuates beyond a predefined threshold, the method and system correlates one or more significant in-game events with the win probability fluctuations. Thereafter, video highlights of the game are generated based on the one or more significant in-game events.

Abstract: The invention provides a method and system for assisting game-play of a user. The method and system includes a training module to train an Artificial Intelligence (AI)-based learning model based on a plurality of offline features and a plurality of online features. The plurality of offline features are extracted from one or more of data collected by one or more game developers, a plurality of Application Programming Interfaces (APIs) and a plurality of replay files associated with game-play. On the other hand, the plurality of online features are extracted from a screen state of the user. Further, the method and system includes a coaching module to coach the user in game-play utilizing the AI-based learning model using techniques such as, but not limited to, statistics, post-game analysis and progress tracking of game-play for optimizing the performance of the user, and generates one or more game-play suggestions for the user.

Abstract: The invention provides a method and system for providing product recommendation to a user. The method and system includes a data collection module to collect information related to the user from one or more online social networking platforms. Further, a Sweep Learning structure is used for collecting information related to the user by providing a personalized page to the user which includes one or more products, for receiving one or more selections from the user. The one or more selections from the user may include products liked by the user, product categories filtered by the user, and specific products explored or searched by the user. Subsequently, a neural network model is used to learn information related to the user based on information collected from the one or more online social networking platforms and the Sweep Learning structure, and accordingly provide one or more product recommendations to the user.