Abstract: A system logs application usage data on a mobile device, processes the data on an analysis system and outputs a current and predicted score to, e.g. third parties. The system logs application-related usage data, which is collected via, e.g. a keyboard application running in the background on the mobile device. The system then evaluates the logged usage data and the events corresponding to a particular application. The events can be analyzed to score the user engagement level with the application, e.g., more events recorded for a given application per day, the more engaged a user is with that application. The engagement level can further be predicated based on historical usage log data from which a score decay model can be generated.

Abstract: The present disclosure describes a method of detecting a drug marker in urine a urine sample using a toilet. The drug markers are fluorophores each of which emits a unique fluorescence spectra. Accordingly, the method does not detect the drug but rather, the drug marker. A user who has consumed the drug with its unique drug marker then urinates into the toilet and a urine sample is captured. The toilet includes a mechanism for fluid handling which diverts urine into a fluorescence spectrometer. The fluorescence spectrometer screens the urine for drug markers based on their unique fluorescent spectra. The toilet may include a controller which quantifies the drug marker. The fluorescent spectrometer may detect multiple drug markers in a single urine sample. This method may be used to confirm drug compliance, test for illicit drugs, identify amounts of drugs consumed, and other uses described herein.

Abstract: A system logs application usage data on a mobile device, processes the data on an analysis system and outputs a current and predicted score to, e.g. third parties. The system logs application-related usage data, which is collected via, e.g. a keyboard application running in the background on the mobile device. The system then evaluates the logged usage data and the events corresponding to a particular application. The events can be analyzed to score the user engagement level with the application, e.g., more events recorded for a given application per day, the more engaged a user is with that application. The engagement level can further be predicated based on historical usage log data from which a score decay model can be generated.