Abstract: Systems and methods for guided user actions are described, including detecting a first action performed by a user, gathering information associated with the first action; retrieving a predictive model based on the information, determining an applicability level of the predictive model to the first action, the predictive model suggests a second action; providing the second action in a user interface when the applicability level meets a threshold level; and receiving input from the user selecting the second action or a third action.

Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Abstract: Systems and methods for guided user actions are described, including gathering information associated with a detected first action; applying a predictive model to suggest a second action based on an application of machine learning to the first action and the information; calculating a first confidence level associated with the application of the machine learning to the first action and the information; for the first confidence level exceeding a threshold, providing the second action, and performing the second action or a third action based on user input.

Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Abstract: Systems and methods for guided user actions are described, including detecting a first action performed by a user; gathering information associated with the first action; retrieving a predictive model based on the information; determining an applicability level of the predictive model to the first action, the predictive model suggests a second action; providing the second action in a user interface when the applicability level meets a threshold level; and receiving input from the user selecting the second action or a third action.

Abstract: Identity protection and management for electronic communication is described, including receiving a request to provide data from a first address to a second address, the request including an attribute associated with the second address, determining a risk value of the second address based on the attribute associated with the second address and a risk score, based on the risk value of the second address generating a third address that is associated with the first address, and providing the data and the third address to the second address without providing the first address to the second address.

Abstract: User settings management using external sources is described, including providing a user interface for a user to configure one or more settings that affect functions of an application; the user interface allows the one or more settings to be manipulated by the user, and allows the one or more settings to be configured based on setting data from a third-party entity; receiving input from the user to configure at least a portion of the one or more settings based on the setting data from the entity; identifying that the setting data from the entity includes the at least the portion of the one or more settings; and configuring the at least the portion of the one or more settings based on the setting data from the entity.

Abstract: User settings management using external sources is described, including providing a user interface for a user to configure one or more settings that affect functions of an application; the user interface allows the one or more settings to be manipulated by the user, and allows the one or more settings to be configured based on setting data from a third-party entity; receiving input from the user to configure at least a portion of the one or more settings based on the setting data from the entity; identifying that the setting data from the entity includes the at least the portion of the one or more settings; and configuring the at least the portion of the one or more settings based on the setting data from the entity.

Abstract: Systems and methods for guided user actions are described, including detecting a first action performed by a user; gathering information associated with the first action; retrieving a predictive model based on the information; determining an applicability level of the predictive model to the first action, the predictive model suggests a second action; providing the second action in a user interface when the applicability level meets a threshold level; and receiving input from the user selecting the second action or a third action.

Abstract: A post end of life management system and method comprising, registering and setting up directives to manage online accounts, digital data, and perform other functions by providers after the death of the registrant. The system is integrated with the social security administration to receive death notifications from the death master file as well as with the deceased individual registry counterparts of other countries. Registrant accounts are encrypted and stored in the system and triggered to decrypt in order to carry out directives left by the deceased after notification of death is received from the social security administration or the deceased individual registry counterpart of another country.