Abstract: A method, device and system for the determination of the attention level of an user of a communication device. The proposed invention detects situations when users are more likely to pay attention to content presented to it (e.g. when they combat boredom) or in other words, situations when users are more receptive to content, without requiring the user to perform certain specific activities or requiring any interventions or specific sensors. In order to do so, user data such as demographics, user context, and usage patterns of the user's communication device (e.g. a mobile phone) is collected.

Abstract: The method comprising: presenting a textual information on a screen of at least one user computing device therefore providing a bitmapped textual image; limiting the time in which said bitmapped textual image is presented on said screen; generating at least two background bitmaps of said bitmapped textual image by using a colour for background pixels of said background bitmaps different from colour for text pixels; and displaying said bitmapped textual image on top of one of said at least two background, wherein a percentage of the background pixels of any of said at least two background bitmaps, that are randomly selected, is altered into said colour of text pixels so that obtaining a time-changing combined image cannot be done by a screenshot. The system is configured to implement the method.

Abstract: A method for predicting reactiveness of MMI users comprises: reacting to a message with a mobile user device which is a receiver of the message, collecting ground-truth data (11) for a machine-learning classifier, extracting from the collected ground-truth data (11) a list of features (12) which determines a current or past context of the user, and each feature having a feature's prediction strength calculated as fraction of classes misclassified when removing the feature; selecting the list of features (12) based on each feature's prediction strength; defining a plurality of reactiveness classes (101); both the extracted list of features (12) and the reactiveness classes (101) being input to the machine-learning classifier; classifying (102) the user according to the defined reactiveness classes (101); predicting the user's reactiveness for the given current or past context of the user by determining the most likely reactiveness class via the machine-learning classifier.