Abstract: Aspects of the subject disclosure may include, for example, a system or method that determines, based on consumption data and location information, groups of access portals, where the consumption data and the location information correspond to a time period in which the access portals are used to access a service using an account. The groups can be adjusted according to an intended benefit unit for the service and a sharing structure can be determined including benefit unit partitions for the account based on the adjusting. Factors can be determined, based on the benefit unit partitions, that are indicative of account sharing, as well as weights for the factors by applying a learning model to the consumption data. An intervention strategy can be selected from a group of intervention strategies according to the factors and the weights for the factors. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a system or method that collects or otherwise accesses information indicating connection patterns such as IP addresses being utilized by communication devices for communication services over a time period, where the communication devices use a same credential of a single account for accessing the communication services. Hubs can be identified according to groups of the communication devices that exhibit a particular sharing pattern such as having used the one or more common IP addresses. A prediction or estimation that the single account is engaging in sharing activity can be made based on an analysis of the hubs, such as based on a number of the hubs. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, identifying content consumption data associated with media content consumption at a customer device, and generating a content selection recommendation for the customer device. Some embodiments can include determining a habit-based content selection vector for the customer device. Various embodiments can include determining the habit-based content selection vector based on a habit profile for the customer device. Some embodiments can include adjusting a content selection vector for the customer device based on the habit-based content selection vector for the customer device. Various embodiments can include generating the content selection recommendation for the customer device based on the adjusted content selection vector. Other embodiments are disclosed.

Abstract: Facilitating separation of intended and non-intended browsing traffic in browsing history advanced networks (e.g., 4G, 5G, and beyond) is provided herein. Operations of a system can comprise determining respective contradiction values for second-level domains of a group of second-level domains in observed browsing history traffic. The operations can also comprise separating intended network traffic from non-intended network traffic based on the respective contradiction values. The respective contradiction values can indicate levels of inconsistency between the observed browsing history traffic and a determined popularity ranking.

Abstract: Aspects of the subject disclosure may include, for example, identifying content consumption data associated with media content consumption at a customer device, and generating a content selection recommendation for the customer device. Some embodiments can include determining a habit-based content selection vector for the customer device. Various embodiments can include determining the habit-based content selection vector based on a habit profile for the customer device. Some embodiments can include adjusting a content selection vector for the customer device based on the habit-based content selection vector for the customer device. Various embodiments can include generating the content selection recommendation for the customer device based on the adjusted content selection vector. Other embodiments are disclosed.

Abstract: Facilitating identification of background browsing traffic in browsing history data in advanced networks (e.g., 5G, 6G, and beyond) is provided herein. Operations of a system can include, based on browsing history traffic observed with respect to a group of user equipment, constructing labeling functions for a group of domains associated with the browsing history traffic, producing a group of labels based on the labeling functions, and transforming the group of labels into a single consolidated label for a first domain. Further, the operations can include, based on a determination that a first numeric value of the single consolidated label is nearer to a first value than a second value, scheduling first traffic for the first domain prior to scheduling traffic for a second domain determined to include the second numeric value that is nearer the second value than the first value.

Abstract: Aspects of the subject disclosure may include, for example, a system or method that collects or otherwise accesses information indicating connection patterns such as IP addresses being utilized by communication devices for communication services over a time period, where the communication devices use a same credential of a single account for accessing the communication services. Hubs can be identified according to groups of the communication devices that exhibit a particular sharing pattern such as having used the one or more common IP addresses. A prediction or estimation that the single account is engaging in sharing activity can be made based on an analysis of the hubs, such as based on a number of the hubs. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a system or method that collects or otherwise accesses information indicating connection patterns such as IP addresses being utilized by communication devices for communication services over a time period, where the communication devices use a same credential of a single account for accessing the communication services. Hubs can be identified according to groups of the communication devices that exhibit a particular sharing pattern such as having used the one or more common IP addresses. A prediction or estimation that the single account is engaging in sharing activity can be made based on an analysis of the hubs, such as based on a number of the hubs. Other embodiments are disclosed.

Abstract: Facilitating separation of intended and non-intended browsing traffic in browsing history advanced networks (e.g., 4G, 5G, and beyond) is provided herein. Operations of a system can comprise determining respective contradiction values for second-level domains of a group of second-level domains in observed browsing history traffic. The operations can also comprise separating intended network traffic from non-intended network traffic based on the respective contradiction values. The respective contradiction values can indicate levels of inconsistency between the observed browsing history traffic and a determined popularity ranking.

Abstract: Aspects of the subject disclosure may include, for example, a method comprising receiving one or more media content items; creating one or more leveraged content items from the media content item(s) by extracting one or more leverage points from the media content item(s); obtaining user information, such as user preferences and user context; selecting a selected content item from the leveraged content item(s) based on the user information; creating a custom content item by further extracting a subset of the leverage points from the selected content item based on the user information; and presenting the custom content item to the user's equipment. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, identifying content consumption data associated with media content consumption at a customer device, and generating a content selection recommendation for the customer device. Some embodiments can include determining a habit-based content selection vector for the customer device. Various embodiments can include determining the habit-based content selection vector based on a habit profile for the customer device. Some embodiments can include adjusting a content selection vector for the customer device based on the habit-based content selection vector for the customer device. Various embodiments can include generating the content selection recommendation for the customer device based on the adjusted content selection vector. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a method comprising receiving one or more media content items; creating one or more leveraged content items from the media content item(s) by extracting one or more leverage points from the media content item(s); obtaining user information, such as user preferences and user context; selecting a selected content item from the leveraged content item(s) based on the user information; creating a custom content item by further extracting a subset of the leverage points from the selected content item based on the user information; and presenting the custom content item to the user's equipment. Other embodiments are disclosed.

Abstract: Facilitating separation of intended and non-intended browsing traffic in browsing history advanced networks (e.g., 4G, 5G, and beyond) is provided herein. Operations of a system can comprise determining respective contradiction values for second-level domains of a group of second-level domains in observed browsing history traffic. The operations can also comprise separating intended network traffic from non-intended network traffic based on the respective contradiction values. The respective contradiction values can indicate levels of inconsistency between the observed browsing history traffic and a determined popularity ranking.

Abstract: Aspects of the subject disclosure may include, for example, identifying content consumption data associated with media content consumption at a customer device, and generating a content selection recommendation for the customer device. Some embodiments can include determining a habit-based content selection vector for the customer device. Various embodiments can include determining the habit-based content selection vector based on a habit profile for the customer device. Some embodiments can include adjusting a content selection vector for the customer device based on the habit-based content selection vector for the customer device. Various embodiments can include generating the content selection recommendation for the customer device based on the adjusted content selection vector. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, identifying content consumption data associated with media content consumption at a customer device, and generating a content selection recommendation for the customer device. Some embodiments can include determining a habit-based content selection vector for the customer device. Various embodiments can include determining the habit-based content selection vector based on a habit profile for the customer device. Some embodiments can include adjusting a content selection vector for the customer device based on the habit-based content selection vector for the customer device. Various embodiments can include generating the content selection recommendation for the customer device based on the adjusted content selection vector. Other embodiments are disclosed.

Abstract: A system for dynamic derivation of and sending to a contact group. The system may recognize an emergency situation (natural disaster, stranded, lost cell connection) and send a confirmation that may be sent to different contact groups from a network entity. The system may also recognize anomalous user behaviors and send a message indicating the anomalous user behaviors to different contact groups from the network entity.

Abstract: Software downloads to Internet of things (IoT) devices are facilitated over a constrained network. In one embodiment a method comprises monitoring, by a network device comprising a processor, data determined to have been sent to a device for transmission to the device via a radio access network device of a wireless communication network, and determining, by the network device, a type of traffic associated with the data. The method further includes based on a determination that the data comprises firmware and that the type of traffic is of a traffic priority that is lower than a defined traffic priority, applying, by the network device, a low priority transport protocol to the data, wherein the applying comprises associating protocol information with the data representative of the low priority transport protocol.

Abstract: Facilitating separation of intended and non-intended browsing traffic in browsing history advanced networks (e.g., 4G, 5G, and beyond) is provided herein. Operations of a system can comprise determining respective contradiction values for second-level domains of a group of second-level domains in observed browsing history traffic. The operations can also comprise separating intended network traffic from non-intended network traffic based on the respective contradiction values. The respective contradiction values can indicate levels of inconsistency between the observed browsing history traffic and a determined popularity ranking.

Abstract: Facilitating separation of intended and non-intended browsing traffic in browsing history advanced networks (e.g., 4G, 5G, and beyond) is provided herein. Operations of a system can comprise determining respective contradiction values for second-level domains of a group of second-level domains in observed browsing history traffic. The operations can also comprise separating intended network traffic from non-intended network traffic based on the respective contradiction values. The respective contradiction values can indicate levels of inconsistency between the observed browsing history traffic and a determined popularity ranking.

Abstract: Aspects of the subject disclosure may include, for example, a method comprising receiving one or more media content items; creating one or more leveraged content items from the media content item(s) by extracting one or more leverage points from the media content item(s); obtaining user information, such as user preferences and user context; selecting a selected content item from the leveraged content item(s) based on the user information; creating a custom content item by further extracting a subset of the leverage points from the selected content item based on the user information; and presenting the custom content item to the user's equipment. Other embodiments are disclosed.