Abstract: Systems and methods are disclosed that provide smart alerts to users, e.g., alerts to users about diabetic states that are only provided when it makes sense to do so, e.g., when the system can predict or estimate that the user is not already cognitively aware of their current condition, e.g., particularly where the current condition is a diabetic state warranting attention. In this way, the alert or alarm is personalized and made particularly effective for that user. Such systems and methods still alert the user when action is necessary, e.g., a bolus or temporary basal rate change, or provide a response to a missed bolus or a need for correction, but do not alert when action is unnecessary, e.g., if the user is already estimated or predicted to be cognitively aware of the diabetic state warranting attention, or if corrective action was already taken.

Abstract: A method of intelligently degrading the quality of content, such as video clips and music, stored on a mobile device over time. The method takes into account the probability of a user watching each item of content and either encodes the content at a lower bit-rate, or under certain circumstances, restores the content to its original quality by side-loading the content from a network content store. The bit-rate at which the content is to be encoded is linked to the probability of a piece of content being watched. If the probability of an item of content being watched is high, then the item is stored at a high bit-rate, and conversely, for low probabilities of watching, a low bit-rate is used. If required, in order to restore an item to a higher bit rate, the original high bit rate item can be requested from a network content store. The aim being to store as much content as possible in a given finite memory, but to ensure that content which is likely to be watched is not degraded.