Abstract: The system obtains an indication of an attribute of a UE operating on a network. The system obtains an NFT based on the attribute of the UE and an ID of the NFT. The system creates a digital wallet based on a public cryptographic key associated with the UE, where the digital wallet can store the NFT and a private cryptographic key associated with the UE. The system stores the NFT in the digital wallet. Storing the NFT indicates that the attribute included in the NFT belongs to the user of the UE. The system receives, from a third party independent of the network, a request to authenticate the user based on the attribute, and provides, to the third party, the ID of the NFT and an address of the digital wallet, where the third party authenticates the user upon verifying that the ID of the NFT is stored in the digital wallet.

Abstract: Systems and methods are provided for determining that the device is not reporting precise location information, based on output from one or more sensors determining that the device is located indoors and determining the altitude of the device. Based on the determination that the device is located indoors, suspending precise location services until it is determined that the device is back outdoors.

Abstract: Systems and methods are provided for determining that the device is not reporting precise location information, based on output from one or more sensors determining that the device is located indoors and determining the altitude of the device. Based on the determination that the device is located indoors, suspending precise location services until it is determined that the device is back outdoors.

Abstract: Systems and methods are provided for determining that the device is not reporting precise location information, based on output from one or more sensors determining that the device is located indoors and determining the altitude of the device. Based on the determination that the device is located indoors, suspending precise location services until it is determined that the device is back outdoors.

Abstract: Techniques are disclosed for locating a user device in an indoor environment such as a building based at least on a building topology model using one or more Internet of Things (IoT) devices. Certain aspects of the techniques include detecting a presence of a user device in communication with an IoT device in a building, wherein the IoT device is associated with device attributes. The building is identified based at least on a building topology model that is associated with the building and the device attributes. The location of the IoT device is determined based at least on the building topology model. The user device is located relative to the location of the IoT device.

Abstract: Described herein are techniques, devices, and systems for training a machine learning model(s) and/or artificial intelligence algorithm(s) to determine where a mobile device (and, hence, a user of the mobile device) is located based on audio data associated with the mobile device and/or contextual data associated with the mobile device. The machine learning techniques may be used to determine contextual information about users, such as determining that a particular location is likely to be a user's home, office, or the like, based on movement patterns exhibited in the data associated with a user's mobile device. Once trained, the machine learning model(s) is usable to classify a mobile device as having been located at one of multiple candidate locations, such as indoors or outdoors, at a particular time. The described techniques can improve the accuracy of determining a mobile device's location, among other technical benefits.

Abstract: The disclosed system obtains data entries associated with a user including segmentation, preferences, behavior elements, contextual events, and trust expectations. The system obtains data entries associated with the wireless telecommunication network including available products, available promotions, and selling channels. The system receives an indication to initiate an interaction between the user and a channel associated with the wireless telecommunication network, where the interaction is aimed at completing a task. Based on the indication to initiate the interaction, the data entries associated with the user, and the data entries associated with the wireless telecommunication network, the system generates a first question to present to the user. The system receives a response to the question from the user. Based on the response, the system generates a second question to present to the user, where the second question furthers completion of the task.

Abstract: Techniques are described for locating user devices in an indoor environment based at least on a building topology model. The techniques include detecting a presence of a user device in a building. The building may include ingress and egress points connecting defined spaces within the building. The locations of the individual ingress and egress points are identified based at least on the movement data of the user device. A building topology model may be generated using the locations of the individual ingress and egress points. To locate the user device at a given timestamp, one or more of the ingress and egress points passed through by the user device may be identified. The location of the user device may be determined based at least on the locations of the one or more ingress and egress points passed through by the user device mapped to the building topology model.

Abstract: Methods of the present invention comprise photoinactivation of catalase in combination with low-concentration peroxide solutions and/or ROS generating agents to provide antibacterial effects.

Abstract: Described herein are techniques, devices, and systems for training a machine learning model(s) and/or artificial intelligence algorithm(s) to determine where a mobile device (and, hence, a user of the mobile device) is located based on audio data associated with the mobile device and/or contextual data associated with the mobile device. The machine learning techniques may be used to determine contextual information about users, such as determining that a particular location is likely to be a user's home, office, or the like, based on movement patterns exhibited in the data associated with a user's mobile device. Once trained, the machine learning model(s) is usable to classify a mobile device as having been located at one of multiple candidate locations, such as indoors or outdoors, at a particular time. The described techniques can improve the accuracy of determining a mobile device's location, among other technical benefits.

Abstract: A system for adjusting steering damping based on sound frequency analysis includes: an acoustic main body configured to play music and determine whether the music is being played; a sound collection device configured to collect a sound signal of the music being played; a sound frequency analysis device configured to receive the sound signal of the music collected by the sound collection device and determine whether the collected music has a strong beat; and a power steering electronic control unit electrically connected to the sound frequency analysis device and configured to receive a determination result of the sound frequency analysis device and adjust the steering damping according to the received determination result of the sound frequency analysis device.

Abstract: Techniques are described for filtering location data in a dataset based at least on its representation of the actual location of a user device. In some embodiments, sensor data including location data associated with a user device located in an environment may be used to identify one or more prediction factors. The one or more prediction factors may be associated to embed a user behavior vector of a user based at least on the associations. The user behavior vector may be a representation of an environment class in which the environment may be classified. The location data is associated with the environment class.

Abstract: A processor-implemented method includes receiving, from a mobile device, transmission information corresponding to an audio data transmission associated with an audio data packet loss of one or more audio data packets. The processor determines, based at least in part on the transmission information, a geographic region associated with a wireless communication network transceiver. The processor further determines an average audio data packet loss rate associated with the geographic region and a correlation between the audio data packet loss and at least one key performance indicator (KPI). The KPI is indicative of the audio data transmission. Based at least in part on (i) the average audio data packet loss rate and (ii) the correlation between the audio data packet loss and the at least one KPI, the processor generates a handover instruction comprising at least one threshold value for transitioning control from a first cell channel to a second cell channel.

Abstract: Techniques are described herein for developing a fingerprint map that may be used for 3D indoor localization. In one example, a network server may leverage a building footprint from an open source database with signal measurements taken by probing user devices from signal sources such as access point (AP) devices. The network server may use the signal measurements to remotely calculate corresponding 3D positions of the AP devices in a particular building. Further, the network server may use the building footprint and the calculated 3D positions of the AP devices as references for developing the fingerprint map for 3D indoor localization.

Abstract: A telemetry data computing engine may receive the telemetry data of a user device, via a network, and it may apply a machine learning algorithm to at least one telemetry data and generate a predicted location for the user device. The predicted location may be used to generate a location pattern for the user device and a user device profile for the user device. The user device profile may be routed to the wireless carrier core network.

Abstract: Techniques directed to utilizing networked devices to enhance user experience are described. In an example, sensor data received from sensor(s) associated with a facility can be used to determine a presence of a user equipment (UE) within or proximate to the facility. Based at least in part on the sensor data, an account associated with the UE can be accessed. Based at least in part on account data associated with the account, a distribution of network technology can be modified to temporarily enable the UE to access a service that is not otherwise available to at least one other UE within or proximate to the facility.

Abstract: User equipment (UE) can include a spectrum analyzer to monitor characteristics of transmission channels. The user equipment can monitor a 600 MHz spectrum and associated channels, for example, to determine if the spectrum is free of interference or is currently occupied. The UE can analyze a received signal strength indication (RSSI), a reference signal received power (RSRP), a reference signal received quality (RSRQ), and signal-to-interference-plus-noise ratio (SINR), for example, to distinguish between types of interference if a channel is occupied. User equipment (UE) can aggregate data and report such data to a network device further aggregate the data and to generate reports. Network components can be deployed or optimized based at least in part on network metrics provided by individual UEs or aggregated data provided by a plurality of UEs. In some instances, the UE can be a mobile phone of a customer to gather metrics in a distributed manner.

Abstract: Methods of the present invention comprise photoinactivation of catalase in combination with low-concentration peroxide solutions and/or ROS generating agents to provide antibacterial effects.

Abstract: A computing device of a telecommunication network can receive crowd-sourced Wi-Fi reports submitted by user equipment based on scans for beacon signals broadcast by Wi-Fi access points. From the crowd-sourced Wi-Fi reports, the computing device can determine when Licensed Assisted Access (LAA) transmissions over channels of an unlicensed spectrum are safe and are not expected to interfere with Wi-Fi transmissions.

Abstract: A processor-implemented method includes receiving, from a mobile device, transmission information corresponding to an audio data transmission associated with an audio data packet loss of one or more audio data packets. The processor determines, based at least in part on the transmission information, a geographic region associated with a wireless communication network transceiver. The processor further determines an average audio data packet loss rate associated with the geographic region and a correlation between the audio data packet loss and at least one key performance indicator (KPI). The KPI is indicative of the audio data transmission. Based at least in part on (i) the average audio data packet loss rate and (ii) the correlation between the audio data packet loss and the at least one KPI, the processor generates a handover instruction comprising at least one threshold value for transitioning control from a first cell channel to a second cell channel.