Abstract: A message generating system disseminates to each of multiple user devices first information items each associated with at least a respective one of multiple services made available by a first entity. When access indicative signals are received via a network connection, each prompted by a respective one of the user devices accessing at least one of the first information items, the system stores a respective awareness-stage record for each received access indicative signal, thereby recording that the respective user entity accessed at least one of the first information items. For each user entity for which a respective awareness-stage record is stored, a second item is generated and sent including information of a particular service associated with at least one first information item. User entities for which a second item was not generated are excluded as having not accessed a first item. The exclusion reduces data traffic on the network connection.

Abstract: A system for reducing input data size for use in an artificial intelligence (AI) engine for predicting a subsequent event. The system includes a computer configured to implement instructions to receive input data and time data indicative of previous events associated with users. The instructions configure the system to determine interface channels associated with modes of interface with the users, previous event characteristics, or both. The system implements instructions associating previous event data with time windows. The instructions configure the system to generate user window values for the combinations of users and time windows. The user window values indicate the interface channels and previous event characteristics of data within the respective time windows. Implementing the instructions configures the system to form a first portion of the raw input data having an association value below a threshold with respect to preceding the subsequent event and generate condensed input data.

Abstract: A message generating system disseminates to each of multiple user devices first information items each associated with at least a respective one of multiple services made available by a first entity. When access indicative signals are received via a network connection, each prompted by a respective one of the user devices accessing at least one of the first information items, the system stores a respective awareness-stage record for each received access indicative signal, thereby recording that the respective user entity accessed at least one of the first information items. For each user entity for which a respective awareness-stage record is stored, a second item is generated and sent including information of a particular service associated with at least one first information item. User entities for which a second item was not generated are excluded as having not accessed a first item. The exclusion reduces data traffic on the network connection.

Abstract: A system includes a computer to implement a front-end input condensation program and a back-end machine learning program. Steps of the front-end program include receive input data and time data indicative of previous events associated with users; determine interface channels associated with modes of interface with the users and/or previous event characteristics; associate previous event data with time windows; generate user window values for the combinations of users and time windows indicating the interface channels and previous event characteristics of data within the respective time windows; and form condensed input data without raw input data having a low association with respect to preceding the subsequent event. Steps of the back-end program include receive the condensed input data and use the condensed data to generate an inference related to the subsequent event such that a time required by the machine learning algorithm to generate the inference is reduced.

Abstract: A system for guiding interactions with a user device requests a response from a plurality of users, stores the response as response data forming a subset of a personal data set of each of the responding users, and generates a predictive model during training of a machine learning program utilizing at least one neural network with a training data set including the personal data set of each of the plurality of users. The predictive model predicts a probability of a first one of the users associated with the user device interacting with a first product and/or service by correlating a personal data set of the first one of the users to the personal data set of at least a second one of the users and sends a communication relating to the first product and/or service to the user device when the predicted probability meets or exceeds a threshold value.

Abstract: A message generating system disseminates to each of multiple user devices first information items each associated with at least a respective one of multiple services made available by a first entity. When access indicative signals are received via a network connection, each prompted by a respective one of the user devices accessing at least one of the first information items, the system stores a respective awareness-stage record for each received access indicative signal, thereby recording that the respective user entity accessed at least one of the first information items. For each user entity for which a respective awareness-stage record is stored, a second item is generated and sent including information of a particular service associated with at least one first information item. User entities for which a second item was not generated are excluded as having not accessed a first item. The exclusion reduces data traffic on the network connection.

Abstract: A system for guiding interactions with a user device requests a response from first users, stores the response as response data forming a subset of a personal data set of each of the responding first users, and generates a predictive model during training of a machine learning program with a training data set including the personal data set of each of the first users. The predictive model predicts a predicted response of a second user associated with the user device by correlating a personal data set of the second user to the personal data set of at least one of the first users. The computer sends a communication to the user device of the second user having content relating to the first product and/or service when it is determined that the predicted response indicates an interest in or preference of the second user for the first product and/or service.

Abstract: A system for guiding interactions with a user device requests a response from a plurality of users, stores the response as response data forming a subset of a personal data set of each of the responding users, and generates a predictive model during training of a machine learning program utilizing at least one neural network with a training data set including the personal data set of each of the plurality of users. The predictive model predicts a probability of a first one of the users associated with the user device interacting with a first product and/or service by correlating a personal data set of the first one of the users to the personal data set of at least a second one of the users and sends a communication relating to the first product and/or service to the user device when the predicted probability meets or exceeds a threshold value.

Abstract: A system for reducing the time to train a machine learning program for predicting a subsequent event. The system includes a computer configured to implement instructions to receive training data and time data indicative of training events associated with users. The instructions configure the system to determine interface channels associated with modes of interface with the users, training event characteristics, or both. The system implements instructions associating training event data with time windows. The instructions configure the system to generate user window values for the combinations of users and time windows. The user window values indicate the interface channels and training event characteristics of data within the respective time windows. Implementing the instructions configures the system to form a first portion of the raw input data having an association value below a threshold with respect to preceding the subsequent event and generate condensed training data.

Abstract: A system and method for training an artificial intelligence engine for real-time monitoring to eliminate false positives is disclosed. The system includes at least one processor, a communication interface coupled to the processor, and a memory device storing executable code. Executing the executable code causes the processor to receive data from an AI security model, receive data from a false positive database, and correlate both sets of data. The correlated data is used to generate a training dataset and a test dataset used to train a false positive identification model. After evaluating the false positive identification model, an AI engine is applied to user registration. The AI engine includes an AI security model and the false positive identification model. Additionally, a system for evaluating the security of user registration utilizing the false positive identification model is disclosed.