Abstract: According to an aspect of one or more embodiments, a system for performing a real-time predictive modeling at a client device may include an ANN model in the client device configured to generate an inference performance associated with the trainable ANN model associated with an action performed at the client device based on data comprising a plurality of features associated with the client device, wherein the inference performance is transmitted. The trainable ANN model is configured to receive a loss calculation generated based on the inference performance, indicating a requirement to correct the trainable ANN model, wherein the trainable ANN model generates one or more corrections for coefficients of the trainable ANN model based on the loss calculation. The trainable ANN model is configured to generate one or more coefficients associated with the trainable ANN model and the data, by convoluting the data, and the trainable model.

Abstract: The present disclosure provides a computer system (112). The computer system (112) performs a method for encoding user count with a low memory footprint. The method includes a first step of receiving real-time and adaptive frequency of user visibility. Further, the method includes another step of receiving a user device (106) id associated with one or more users (104). Furthermore, the method includes yet another step of encoding the user visibility count. The frequency of the user visibility is the number of times the computer system receives a request from a user device (106). The user device (106) id is a unique string of numbers and letters. The unique string of numbers and letters identifies the user device (106) associated with one or more user (104). The user visibility count is encoded by using one or more data structures and one or more algorithms.

Abstract: A method for performing at least one action on a user's computing-device, according to at least one user-moment, including: collecting data comprising at least one signal from the computing-device; analyzing the collected data in real time, to determine occurrence of at least one user-moment; analyzing by the processor a plurality of user-moments, to predict at least one future user-moment, related to what a user is expected to do, need or want; receiving a list comprising at least one action that may be applied on the user's computing-device; receiving at least one rule, associating the at least one action with the at least one predicted user-moment; receiving at least one rule-condition, associated with the rule and the at least one predicted user-moment; and performing at least one applied-action on the user's computing-device, according to the applied-rule, if the at least one rule-condition is met.