Disclosed herein is a method and system for collecting attentiveness information associated with a user's response to consuming a piece of media content. The attentiveness information is used to create an attentiveness-labelled behavioural data for the user's response. A computer-implemented attentiveness model may be generated by applying machine learning techniques to the a set of attentiveness-labelled behavioural data from multiple users. The system may comprise an annotation tool that facilitates human labelling of the user's response with attentiveness data. The resulting attentiveness model is therefore based on correlations indicative of attentiveness within the attentiveness-labelled behavioural data and/or physiological data that are based on real human cognition rather than a predetermined feature or combination of features.

Computer technology for making sure that no viewer in an audience of multiple co-viewers will effectively be presented with audiovisual content portions that inappropriate and/or irrelevant for that co-viewer. Avoiding the effective presentation of inappropriate and/or irrelevant content to one or more of the co-viewers may involve currently conventional techniques such as blurring, scrambling, obscuring, distracting, etc.

A system and method for voice-based control of sexual stimulation devices. In some configurations, the system and method involve receiving voice data, analyzing the voice data to detect spoken commands, and generating control signals based on the commands. In some configurations, the system and method involve receiving voice data, analyzing the voice data for non-speech vocalizations, detecting voice stress patterns, and generating control signals based on the detected patterns. In some configurations, the analyses of the voice data are performed by machine learning algorithms which may be trained on associations between speech and non-speech vocalizations of a user while the user engages in one or more voice-based training tasks, associating speech and non-speech vocalizations with controls of the sexual stimulation device. In some configurations, machine learning algorithms are used to make the associations. In some configurations, data from other biometric sensors is included in the associations.

The present invention discloses a home infotainment system having function of non-contact imaging-based physiological signal measurement. In the present invention, an image-based physiological signal (IBPS) measuring device is integrated with a television, and is configured for capture at least one image frame of a user who is watching the television. As such, after detecting detects a face portion from the image frame, the IBPS measuring device is able to subsequently extract at least one physiological signal from the face portion. Consequently, after the physiological signal is applied with at least one signal process by a display processor of the television or the IBPS measuring device, physiological indices of the user are hence calculated, and are subsequently applied with data processes of data storing, data transmitting, and/or displaying the physiological indices by a form of graphs, or diagrams, and/or numeric values.

A system and method for rapidly and scalably tracking attentiveness. The system includes means for collecting relevant data streams from a plurality of client devices while consumers view content, means for analysing the collected data with an AI-driven module that outputs one or more attentiveness metrics indicative of real attention, and means for synchronising the collected data with the attentiveness metrics. The system provides the ability to synchronise attentiveness metrics with other data streams to make accessible the reasons that drive attention. A digital advertising campaign can be optimised using an effectiveness data set that expresses evolution over time of an attentiveness parameter. An effect on the attentiveness parameter caused by an adjustment to a target audience can be predicted and evaluated against a campaign objective, which can be updated for predictions that yield a positive effect.

Provided is an information processing device for effectively using a television that is in a non-use state by using an artificial intelligence function.

An information processing device for controlling operation of a display device by using an artificial intelligence function, includes an acquisition section that acquires sensor information, and an inferring section that infers content, which is to be outputted by the display device according to a use state, by using the artificial intelligence function, on the basis of the sensor information. The inferring section infers content, which is to be outputted by the display device that is in a non-use state, on the basis of information regarding a room where the display device is placed. The information regarding the room is included in the sensor information.

A device may be configured to render at least one spatial region of 360-degree media content, which may include two or more spatial regions. The device may include a receiver configured to receive the 360-degree media content and metadata associated with the 360-degree content. The metadata may include a classification of a respective spatial region of the 360-degree media content. The device may further include a memory configured to store a user preference and a sensor configured to detect a user movement. The device may include a processor configured to determine that the user movement is associated with a rendering of the respective spatial region. The processor may further determine whether the classification complies with the user preference and alter the rendering of the respective spatial region if the classification violates the user preference.

A system and method for thought-based control of sexual stimulation devices using electroencephalography. The system and method involve a training phase and an operation phase. In an embodiment, the training phase comprises placing electrodes on the head of a person, measuring electrical signals produced by the person's brain via the electrodes while the user engages in one or more thought-based training tasks, associating patterns of electrical activity in the person's brain while performing the tasks with controls of the sexual stimulation device. In an embodiment, the operation phase comprises generating control signals for the sexual stimulation device based on the associations when the patterns of electrical activity are detected by the electrodes. In some embodiments, machine learning algorithms are used to detect the patterns of electrical activity and make the associations. In some embodiments, data from other biometric sensors is included in the associations.

The system and methods described herein provide content recommendations to user equipment timed to reduce interruption of content. A content recommendation application detects output of first content and determines viewing metrics for the first content. Using the determined viewing metrics, the content recommendation application determines whether output of a content recommendation should be blocked. In response to determining that the viewing metrics indicate output of the content recommendation should be blocked, the content recommendation application blocks the content recommendation. Upon receiving an input changing output of the first content to output of a second content, the content recommendation application unblocks and outputs the content recommendation.

A system and method for voice-based control of sexual stimulation devices. In some configurations, the system and method involve receiving voice data, analyzing the voice data to detect spoken commands, and generating control signals based on the commands. In some configurations, the system and method involve receiving voice data, analyzing the voice data for non-speech vocalizations, detecting voice stress patterns, and generating control signals based on the detected patterns. In some configurations, the analyses of the voice data are performed by machine learning algorithms which may be trained on associations between speech and non-speech vocalizations of a user while the user engages in one or more voice-based training tasks, associating speech and non-speech vocalizations with controls of the sexual stimulation device. In some configurations, machine learning algorithms are used to make the associations. In some configurations, data from other biometric sensors is included in the associations.