Abstract: 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.

Abstract: 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.

Abstract: A computer-implement method of predicting commercial effectiveness of a piece of media content without collecting data from potential consumers. The method comprises extracting an information-rich content-based parameter from the media content. The parameter may be a vector having a predetermined, e.g. fixed, dimensionality that is designed as an input in a performance data prediction engine. By enabling the same type of parameter to be extracted from media content of different types and durations, the technique proposed allows media content for which performance data has already been gathered to be used to predict performance data for new media content. The prediction can be done based on a comparison of the content-based parameters extracted from media content. Alternatively, machine learning techniques may be used to generate a model using known performance data, whereby the model can use the content-based parameter from new media content to predict performance data.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments of the present method comprise activating a data recording component on the computing device to receive information relating to the environment of the computer user and executing a quality check module in the computing device operative to analyse the environment of the computer user. The quality check module performs the steps of: assessing a received signal from the data recording component against a predetermined quality metric to ascertain if an informational content of the received signal meets a predetermined minimum quality to permit computer user behavioural data to be collected therefrom, determining and executing a responsive action where the received signal does not satisfy the quality metric, and initiating a computer user behavioural data collection process to computer user collect behavioural data during the interaction between the computer user and the computing device where the received signal satisfies the quality metric.

Abstract: 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.

Abstract: A method of initiating and controlling computer user behavioural data (e.g., emotional state information) collection and processing within the structure of a video ad response provides an analysis of video ad effectiveness as a result of interaction with the computer user. A behavioural data collection and processing module is caused to nut by the execution of a Video Ad Standard Template (VAST) compliant video ad response in a video player. The data collection and processing module may be obtained by calling a resource identifier contained in the video ad response, or by providing an executable application within the served video ad response itself. In more particular methods according to the invention, processing of collected data is managed so as to improve the efficiency of media playback at the client computer and communications with remote servers.

Abstract: Embodiments of the invention provide analytics in regard to behavioural data for improved computer-resource utilization, more particularly, in connection with a computer-implemented method of using behavioural data collected for a user (and in particular emotional response data obtained from facial images of the user) to generate or otherwise control ad inventory or ad display on the fly.

Abstract: Methods and systems of predicting performance data for a piece of media content that is consumable by a user at a client device are provided. In one or more embodiments, the method collects raw input data, such as from a webcam, indicative of a user's response to the media content as the user watches the content. The data is processed to extract and obtain a series of head pose signals and facial expression signals, which is then input to a classification model. The model maps the performance data of the media content over time in response to the signals evaluated by the method to produce a prediction of the performance of the piece of media content.

Abstract: Embodiments of the invention provide a computer-implemented method of collecting computer user behavioral data during an interaction between a computer user and web-based content accessible via a computing device. A behavioral data collection activation script is provided, which may be provided within executable code of a webpage or media player executable on a webpage. The script runs during loading of the webpage or media player or upon execution of a specific operation or action within the webpage or media player. The script triggers activation of a data recording component on the computing device via a browser-based application programming interface (API), and triggers initiation of a behavioral data collection application on the computing device, which receives information from the data recording component as an input.

Abstract: A method of initiating and controlling computer user behavioural data (e.g., emotional state information) collection and processing within the structure of a video ad response provides an analysis of video ad effectiveness as a result of interaction with the computer user. A behavioural data collection and processing module is caused to nut by the execution of a Video Ad Standard Template (VAST) compliant video ad response in a video player. The data collection and processing module may be obtained by calling a resource identifier contained in the video ad response, or by providing an executable application within the served video ad response itself. In more particular methods according to the invention, processing of collected data is managed so as to improve the efficiency of media playback at the client computer and communications with remote servers.

Abstract: A method of initiating and controlling computer user behavioural data (e.g., emotional state information) collection and processing within the structure of a video ad response provides an analysis of video ad effectiveness as a result of interaction with the computer user. A behavioural data collection and processing module is caused to run by the execution of a Video Ad Standard Template (VAST) compliant video ad response in a video player. The data collection and processing module may be obtained by calling a resource identifier contained in the video ad response, or by providing an executable application within the served video ad response itself. In more particular methods according to the invention, processing of collected data is managed so as to improve the efficiency of media playback at the client computer and communications with remote servers.

Abstract: Embodiments of the invention provide a computer-implemented method of collecting computer user behavioural data during an interaction between a computer user and web-based content accessible via a computing device. A behavioural data collection activation script is provided, which may be provided within executable code of a webpage or media player executable on a webpage. The script runs during loading of the webpage or media player or upon execution of a specific operation or action within the webpage or media player. The script triggers activation of a data recording component on the computing device via a browser-based application programming interface (API), and triggers initiation of a behavioural data collection application on the computing device, which receives information from the data recording component as an input.

Abstract: Embodiments of the invention provide analytics in regard to behavioural data for improved computer-resource utilization, more particularly, in connection with a computer-implemented method of using behavioural data collected for a user (and in particular emotional response data obtained from facial images of the user) to generate or otherwise control ad inventory or ad display on the fly.

Abstract: Embodiments of the present method comprise activating a data recording component on the computing device to receive information relating to the environment of the computer user and executing a quality check module in the computing device operative to analyse the environment of the computer user. The quality check module performs the steps of: assessing a received signal from the data recording component against a predetermined quality metric to ascertain if an informational content of the received signal meets a predetermined minimum quality to permit computer user behavioural data to be collected therefrom, determining and executing a responsive action where the received signal does not satisfy the quality metric, and initiating a computer user behavioural data collection process to computer user collect behavioural data during the interaction between the computer user and the computing device where the received signal satisfies the quality metric.

Abstract: A method of initiating and controlling computer user behavioural data (e.g., emotional state information) collection and processing within the structure of a video ad response provides an analysis of video ad effectiveness as a result of interaction with the computer user. A behavioural data collection and processing module is caused to run by the execution of a Video Ad Standard Template (VAST) compliant video ad response in a video player. The data collection and processing module may be obtained by calling a resource identifier contained in the video ad response, or by providing an executable application within the served video ad response itself. In more particular methods according to the invention, processing of collected data is managed so as to improve the efficiency of media playback at the client computer and communications with remote servers.