Abstract: A modeling problem can be received. A database of prior calibrated models can be searched to identify a similar problem having features similar to the received modeling problem. The modeling problem can be calibrated using information of the identified similar problem. The accuracy of calibrated modeling problem can be monitored. The modeling problem can be recalibrated until a performance criterion is met. Calibrated modeling problem can be stored in the database.

Abstract: A method and system for predicting vehicle wash services for a human-driven or self-driven vehicle. The method determines anticipated routes of travel for the vehicle based on context of a user associated with the vehicle, implements machine learning neural network models for predicting the road conditions in the anticipated routes of travel, and, then, auto-suggests one or more candidate car wash locations and associated Time of Days (to wash the car) while optimizing a user value.

Abstract: A method of operating a support system includes enabling electronic communications with at least one senor of user conditions and at least one database of user activity, collecting, via the electronic communications, a plurality of data points from the sensor and the database, determining a context of the user, calculating at least one affinity score of the user for each of at least one patient, predicting a risk of passive illness associated with the user, identifying, using the risk of passive illness, an amelioration action, and outputting a signal reducing the risk of the passive illness.

Abstract: A method and system for predicting vehicle wash services for a human-driven or self-driven vehicle. The method determines anticipated routes of travel for the vehicle based on context of a user associated with the vehicle, implements machine learning neural network models for predicting the road conditions in the anticipated routes of travel, and, then, auto-suggests one or more candidate car wash locations and associated Time of Days (to wash the car) while optimizing a user value.

Abstract: Intelligent management of user interface and device sensed events is provided. Discrete events generated through interactions between a user and a machine are monitored, the interaction performed via a user interface associated with an application running on the machine, the discrete events stored by sessions in a storage device local to the machine as event logs. Network connection pattern of the machine with a server device is determined. Storage constraints of the storage device are determined based on the monitoring and the network connection pattern. The event logs are compacted by progressively summarizing the discrete events on dynamically adjusted segments of the sessions. The compacted event logs are transmitted to the server device responsive to determining that a network connection between the machine and the server device is available.

Abstract: Intelligent management of user interface and device sensed events is provided. Discrete events generated through interactions between a user and a machine are monitored, the interaction performed via a user interface associated with an application running on the machine, the discrete events stored by sessions in a storage device local to the machine as event logs. Network connection pattern of the machine with a server device is determined. Storage constraints of the storage device are determined based on the monitoring and the network connection pattern. The event logs are compacted by progressively summarizing the discrete events on dynamically adjusted segments of the sessions. The compacted event logs are transmitted to the server device responsive to determining that a network connection between the machine and the server device is available.