WARNER BROS. ENTERTAINMENT INC.

A systematic method of introducing obfuscating “organic” noise to a user's content engagement history leverages a recommender system by creating a public history on a client device which is a superset of the user's true engagement history. The method builds up the superset history over time through a client's interaction with the recommender system by simulating organic growth in a user's actual engagement history. The organic superset prevents an adversary with access to the underlying recommendation model from readily distinguishing between signal and noise in a user's query and obfuscates the user's engagement history with the recommender system.

Provided is a system for controlling digital cinematic content based on emotional state of characters. A focus on one or more computer-controlled characters appearing in digital cinematic content is determined based on emotion indicators of a first user actively interacting with at least the one or more computer-controlled characters. A set of emotion indicators is inferred for each of the one or more computer-controlled characters based on one or more criteria and multifactor feedback loops are created. A story line of the digital cinematic content and behavioural characteristics of the one or more computer-controlled characters are controlled to achieve a target emotional arc of the first user based on the multifactor feedback loops.

A systematic method of introducing obfuscating “organic” noise to a user's content engagement history leverages a recommender system by creating a public history on a client device which is a superset of the user's true engagement history. The method builds up the superset history over time through a client's interaction with the recommender system by simulating organic growth in a user's actual engagement history. The organic superset prevents an adversary with access to the underlying recommendation model from readily distinguishing between signal and noise in a user's query and obfuscates the user's engagement history with the recommender system.

Provided is a system for controlling digital cinematic content based on emotional state of characters. A focus on one or more computer-controlled characters appearing in digital cinematic content is determined based on emotion indicators of a first user actively interacting with at least the one or more computer-controlled characters. A set of emotion indicators is inferred for each of the one or more computer-controlled characters based on one or more criteria and multifactor feedback loops are created. A story line of the digital cinematic content and behavioural characteristics of the one or more computer-controlled characters are controlled to achieve a target emotional arc of the first user based on the multifactor feedback loops.

A systematic method of introducing obfuscating “organic” noise to a user's content engagement history leverages a recommender system by creating a public history on a client device which is a superset of the user's true engagement history. The method builds up the superset history over time through a client's interaction with the recommender system by simulating organic growth in a user's actual engagement history. The organic superset prevents an adversary with access to the underlying recommendation model from readily distinguishing between signal and noise in a user's query and obfuscates the user's engagement history with the recommender system.

Provided is a system for controlling progress of audio-video content based on sensor data of multiple users, composite neuro-physiological state (CNS) and/or content engagement power (CEP). Sensor data is received from sensors positioned on an electronic device of a first user to sense neuro-physiological responses of the first user and second users that are in field-of-view (FOV) of the sensors. Based on the sensor data and at least one of a CNS value for social interaction application and a CEP value for immersive content, recommendations of action items for first user are predicted. Content of a feedback loop, created based on sensor data, CNS value, CEP value, and predicted recommendations, is rendered on output unit of electronic device during play of the at least one of social interaction application and immersive content experience. Progress of social interaction and immersive content experience is controlled by first user based on predicted recommendations.

A systematic method of introducing obfuscating “organic” noise to a user's content engagement history leverages a recommender system by creating a public history on a client device which is a superset of the user's true engagement history. The method builds up the superset history over time through a client's interaction with the recommender system by simulating organic growth in a user's actual engagement history. The organic superset prevents an adversary with access to the underlying recommendation model from readily distinguishing between signal and noise in a user's query and obfuscates the user's engagement history with the recommender system.