Aspects of the subject disclosure may include, for example, embodiments that comprise obtaining a data budget associated with a communication session for streaming video content over a communication network from a video content server, determining a first portion of the data budget that is associated with a first segment of the video content, and obtaining quality information associated with the video content from the video content server over the communication network. Further embodiments can include identifying a first group of tracks for the first segment, and determining a first target quality for the first segment based on the first portion of the data budget and the quality information. Other embodiments are disclosed.

An electronic device is provided. The electronic device includes a display, a communication module, a memory, and a processor configured to receive external device information by establishing a communication connection with a server using the communication module, display a graphic object indicating an external device corresponding to the received external device information on the display, provide a configuration user interface (UI) capable of changing configuration information of the external device based on a user input for the graphic object, store the changed configuration information of the external device based on a user input for the configuration UI, obtain a plurality of device information by searching the external device, establish the communication connection by selecting the device corresponding to the external device information among the plurality of device information, and transmit the stored configuration information to the external device through the communication connection.

A call recommendation system based on artificial intelligence is provided. The call recommendation system includes a data collecting unit, a matching time predicting unit, a price determining unit, and a final ranking determining unit. When a service is requested from a service user, the data collecting unit collects first past data indicating a past location of the service user, first present data indicating a present location of the service user, second past data indicating a past location of a service provider, and second present data indicating a present location of the service provider. The matching time predicting unit inputs the first and second past data and the first and second present data to a recurrent neutral network (RNN) leaning model to predict a future location of the service user and a future location of the service provider and inputs first prediction data regarding the future location of the service user and second prediction data regarding the future location of the service provider to a prediction learning model to predict, when the service provider selects a service, a matching time required until the service provider is matched with a next service user after the service provider completes the service. The price determining unit determines a price for the service such that the price increases as the matching time increases. The final ranking determining unit determines a recommendation rating (or a recommendation priority) of a service among services required for the service provider based on preference data indicating preference of the service provider regarding a service and a price. The RNN learning model and the prediction learning model are based on a deep learning algorithm.

One or more computing devices, systems, and/or methods for identifying anomalous behavior of users are provided. In an example, users of a telecommunication service provider may be segmented into a plurality of user segments based upon telecommunication service metrics associated with the users. A machine learning model may be trained using telecommunication service information associated with users of the first user segment to generate a trained machine learning model. Using the trained machine learning model, a forecast of telecommunication service usage associated with a first user segment of the plurality of user segments. A telecommunication service usage metric, associated with a user belonging to the first user segment, may be compared with a range indicated by the forecast. The user may be flagged as having anomalous behavior based upon a determination that one or more telecommunication usage metrics, associated with the user, are outside one or more ranges indicated by the forecast.

In order to appropriately perform, in a mobile communication system including a plurality of mobile communication networks respectively operated by a plurality of mobile communication operators, cooperation among respective mobile communication networks, a system 2 includes a control apparatus including a second control unit that performs, via a first mobile communication network 1a operated by a mobile communication operator A, for a second mobile communication network 1b operated by a mobile communication operator B different from the first mobile communication operator A, control related to mobility for a terminal apparatus connected to the second mobile communication network 1b, and a control apparatus including a first control unit that performs, not via another mobile communication network different from the second mobile communication network 1b, for the second mobile communication network 1b, control related to mobility for the terminal apparatus connected to the second mobile communication network 1b.

A 5G network may support path switch mechanisms from a direct path to an indirect path to the network, and vice versa. Methods and systems are disclosed for performing the path switching process.

Disclosed here is a method to determine a user intent when a user device initiates an interactive voice response (IVR) call with a wireless telecommunication network. A processor can detect the IVR call initiated with the network and determine whether the user device is a member of the network. Upon determining that the user device is a member of the network, the processor can obtain user history including interaction history between the user and the network. Based on the user history, the processor can predict the user intent when the user initiates the IVR call. The processor can detect whether user device is a 5G capable device. Upon the determining that the device is 5G capable and based on the predicted user intent, the processor can suggest to the user an application configured to execute on the user device and configured to address the predicted user intent.

Aspects of the subject disclosure may include, for example, identifying a first user associated with a first mobile device, and determining a first user type associated with the first user. Further embodiments can include identifying a first identity associated with the first user based on the first user type and identifying a second identity associated with the first user based on the first user type. Additional embodiments can include providing first alternate content to the first mobile device to mask the first identity and providing second alternate content to the first mobile device associated with the second identity. Other embodiments are disclosed.

A method, a device, and a non-transitory storage medium are described in which a mapping table is transmitted to a user device. The mapping table includes, for each entry, an index, a frequency band, and wireless services available on the frequency band. Index information corresponding to a frequency band and wireless service available in a location associated with the user device is communicated to the user device. An icon associated with the wireless service is displayed on a display of the user device. A network device determines that the user device is using the wireless service to communicate via a wireless network and transmits, to a core network, an indication that the user device is using the wireless service on the frequency band.

Systems, methods, and software of managing bandwidth allocated to User Equipment (UE). In one embodiment, a bandwidth management system collects cell load information for a plurality of cells within a Radio Access Network (RAN), and processes the cell load information to determine a cell load status for each of the cells. The system performs bandwidth throttling for the UE by determining a location of the UE, identifying the cell load status for one or more cells in a region of the RAN corresponding with the location of the UE, determining whether the region of the RAN is overloaded based on the cell load status, and controlling a downgrade of bandwidth allocated to the UE in the RAN responsive to a determination that the region is overloaded.