Virtual assistant in phone conversation

This disclosure describes techniques to provide a virtual assistant in a regular telephone call. The technique provides access to a virtual assistant to phone calls without the calling user device having a data connection. A user interface is provided that enables a user to selectively add a virtual assistant to a telephone call at call initiation or during an ongoing call. Upon user selection, the virtual assistant is added to the call along with the user selected call recipient, thus providing a conference call. With user permission, the virtual assistant accesses call audio and responds to user commands. The techniques are suitable for use by users that do not have access to a data plan or a data connection, e.g., users in remote areas, users of feature phones, etc

Mechanism for Reducing the Cost Paid by End-users for the Data Consumed to View Ads

A technique is provided for separating ad streams from other services provided to a mobile device, e.g., using a separate socket for the ad stream. This can enable the ability to charge the end-user differently for the ad stream data consumed on the mobile device when the device is on a mobile wireless network that charges for the data consumed, and use lesser bandwidth to stream the ads than that used to provide the other services

Dormancy prediction and user equipment driven active-dormant transitions

The decision to move a mobile device to dormant radio resource state is typically driven by the mobile network. For example, after a period of observed inactivity, the network may move the mobile device to dormancy. However, mobile applications may send data shortly after the mobile device is moved to dormancy which results in sub-optimal power and bandwidth use. This disclosure provides techniques that enable the mobile device to predict immediate data transmission requirements. The predictions are used by the mobile device to drive active-dormant transitions at the radio resource control (RRC) layer

Voice Service Availability Indication for Data Centric Networks on a Mixed-Use Wireless Device

A wireless device, such as a smartphone, includes voice and data capabilities for a user. However, a user interface (UI) on the wireless device shows the signal quality of only the voice service signal. The user does not have information about the signal quality of data service which may be available to the user, creating a frustrating user experience. To improve the user wireless device experience, the availability of data service and the availability of voice service can be displayed in the UI

Opportunistic Data Network Selection For User Equipment With Multiple Subscriber Identification Module Cards

A user equipment (UE) includes multiple subscriber identification module (SIM) cards to connect to different networks, including any available opportunistic data network. To support connection to 5G opportunistic data networks, the UE employs the primary network stack (the network stack associated with the primary SIM card) to connect to a 5G communications network (e.g., a 5G cellular network), and further employs the primary network stack to periodically scan for opportunistic data networks. In response to detecting a 5G opportunistic data network, the primary network stack transfers the 5G connection capability to a secondary network stack (a network stack associated with a secondary SIM card), and the UE connects to the 5G opportunistic data network via the secondary network stack, while connecting the primary network stack to another communications network (e.g., a Long-Term Evolution (LTE) or 4G communications network). The UE is thereby able to detect and connect to 5G opportunistic data networks, even when only one of the multiple SIM cards is able to have 5G capability at one time

Automatic translation in voice calls

Participants in a voice call do not always have a common language. This can pose problems since participants may not be able to understand each other during the call. Some current techniques to address this problem involve incorporating a machine translator that can perform translation. However, for machine translation to be provided to all participants, such techniques require a machine translator mechanism to be provided at each participant’s end, which is not always possible. The techniques of this disclosure incorporates machine translation techniques on a participant device such that the device can perform local translation of speech in a call. Machine translation is performed to automatically translate speech in the call, e.g., between the languages spoken by participants at the near and the far ends. In this manner, automatic translation of voice calls is enabled for all participants in a voice call

Self-Optimizing Geofence for Opportunistic Wireless Networks

A method is provided for optimizing a geofence boundary in order to reduce the amount of time between a mobile device entering the geofence and the mobile device connecting to the network. Optimizing a geofence boundary can also reduce battery power used by the mobile device in scanning for network coverage. Once a mobile device is within an area that is covered by a cellular network and has connected to the network, an algorithm is employed that adjusts an edge of a geofence that encompasses some or all of the cellular coverage area. The algorithm moves the edge of the geofence based on the location of the last unsuccessful scan by the mobile device. The algorithm iteratively adjusts the edges of multiple zones of a geofence as the mobile device user moves within the geofence, resulting in a geofence that more closely matches the boundary of actual cellular coverage

Method For Cell Selection And System Determination During Network Connection

A system and method are disclosed that incorporate a machine learning algorithm for optimizing mobile system and cell selection. The system collects data from a large number of devices or from network operators spread over a geographic region to update the machine learning algorithm. The data collected by the algorithm includes location of the device, location of the cell, RAT (radio access technology) or band acquired by the device. The algorithm recommends the mobile system to connect to the best possible network from among alternatives. The device logs particulars of the connected network if connection is successful, or failure to connect, which is used to update and improve the algorithm. This method of cell selection is much faster and more efficient than the complex algorithms currently used by networks. The method eliminates the problems such as slow connectivity and getting stuck in a lower speed network encountered by devices

Optimize CBRS Geofence Switching by Merging Nearby CBSD Cells

To reduce the processing, memory, and data transfer overhead for a user equipment (UE) related to maintaining, monitoring, and receiving updates for a record of a large number of Citizens Broadband Radio Systems (CBRS) geofences (i.e., a CBRS geofence record) corresponding to individual Citizens Broadband Service Device (CBSD) coverage areas, a system redefines the coverage area of multiple nearby CBSDs into a single cluster and then updates the CBRS geofence record stored at a UE based on the coverage areas of such CBSD clusters rather than individual CBSD coverage areas. Each time the system receives an update to a carrier’s coverage file representing CBSD coverage areas, the CBSD clusters are redefined, with a new cluster being created from the initial CBSD listed in the coverage file, and subsequent CBSDs being either added to existing CBSD clusters or being used to create new CBSD clusters

INTELLIGENT SIM SWITCHING IN A MULTI-SIM COMPUTING DEVICE

A system is described that enables a computing device (e.g., a mobile phone, a tablet computer, a laptop computer, etc.) to intelligently determine whether to move a data connection from a default data subscriber identity module (DDS) to a non-default data subscriber identity module (non-DDS) when initiating a voice call or during an on-going voice call on the non-DDS in a multiple subscriber identity module (multi-SIM) computing device based on a current state of the computing device. The current state of the computing device may be determined based on various contextual signals, including data usage for each application on the device, the number of foreground applications, the number of background applications, data tethering state, voice call characteristics, connected peripherals, computing device screen state, and/or sensor data generated by one or more sensors (e.g., proximity sensors, near-field microwave sensors, radar, capacitive sensors, etc.) of the computing device. The computing device may analyze these contextual signals to determine a current state of the device and may selectively move the data connection from the DDS to the non-DDS based on this state information