Abstract: Systems, methods, and computer-readable media for delivering recommendations are provided to personalize user experience, optimize online advertising, and maximize revenue for online merchants. An example system can include a computer configured to: receive historic user online actions data and one or more purchase confirmations of a user, train a deep reinforcement learning system based on the received data, receive a current observation characterizing interaction of the user with at least one of the recommendations in an online environment, determine a reward for the deep reinforcement learning system based on the current observation, where the reward depends on a time parameter associated with an intended action of the user, select an action to be performed by an agent based on the reward, and cause the agent to provide or display a new recommendation to the user or another comparable user based on the selected action.

Abstract: An object recognition system and method is provided which uses automated algorithmically determined negative training. Negative training with respect to a particular object classifier allows for more streamlined and efficient targeted negative training, enabling time and cost savings while simultaneously improving the accuracy of recognition based on the targeted negative training. According to certain additional aspects, embodiments of the invention relate to a system and method for structuring an object recognition engine to maximize recognition accuracy and emulate human visual cortex.

Abstract: An object recognition system and method is provided which uses automated algorithmically determined negative training. Negative training with respect to a particular object classifier allows for more streamlined and efficient targeted negative training, enabling time and cost savings while simultaneously improving the accuracy of recognition based on the targeted negative training.

Abstract: Aspects of embodiments of the present invention pertain to a system and method for supplying targeted contextual recommendations, advertisements or commercial offers to mobile users based on their interest graph and spacio-temporal map of each user's mobile activities and behavioral patterns. A novel powerful likely intent score is computed based on leveraging both the interest graph and computing persona similarities based on psychographic analysis and spacio-temporal activity maps.

Abstract: Aspects of the present invention pertains to a system and method for targeted intelligent data gathering from web based applications through the use of mobile computing means devices. Such data is used to compile an interest profile for each mobile user, and further build an interest graph of a community of mobile users. The interest graph can further be used, but not limited to, personalization, curated experiences, as well as targeting of advertisements and commercial offers.