T-mobile Usa, Inc.

Intel Corporation

University of Michigan

Jie Hui

Antoine T Tran

Jong Sung Yoon

Kranthi Sontineni

Kevin Lau

Jeffery A Smith

Yunhan Jia

Doru Culiac

Himesh Bagley

Kevin Hinson

Narciso Faustino

Bryan Yang

Yasmin Karimli

Samson Kim-Sun Kwong

Zachary William McPherson

Zhuoqing Morley Mao

Peter P Myron

Daniel Howard Wilson

Ahmad Arash Obaidi

Pablo Tapia

Jun Liu

Salvador Mendoza

Michael J Mitchell

Thomas P Lucht

Carl Williams

Sayeedur Rahman

Shoeb Mohammed

Maxwell Andrew Smith

Evgene Shulga

Sanjay Perumbur

Rath Vannithamby

Jing Zhu

Ali Taha Koc

Maruti Gupta

Christopher Harvey Joul

Cristian Asandului

Warren J McNeel

Karthik Iyer

Emile Minh Tran

Boua Lor

Suchit Satpathy

Samson Kwong

Vipul M Sagare

Mathew Samuel

Kristian D Taylor

Qi Alfred Chen

Z Morley Mao

Chao Kong

Yihua Guo

Alex Yoon

Haokun Luo

Mohamad F Ezzeddine

Ryan Cyrus Khamneian

Vigneshwaran Ganesan

ChihChin Wu

Maria Huynh

Joohyung Kim

Jennifer Caldwell

Zachary Mcpherson

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.

Personalizing an interaction between a mobile device associated with a user and an application or a website associated with a wireless telecommunication network

Continuous and non-episodic user sessions in a virtual world are facilitated by providing a virtual service center with alternative virtual experiences selected to retain user presence in the virtual world. The virtual service center is associated with a mobile network operator (MNO) of a telecommunications network and is provided to enable resolution or fulfillment of network service issues indicated by users. The alternative virtual experiences are selected and users driven thereto to continuously engage users in the virtual world outside of the immediate context of their service issues and resolution thereof.

Network service center with improved virtual immersion during service durations via alternative virtual experiences

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.

Localization using internet of things devices

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.

Constrained user device location using building topology

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.

Using machine learning to locate mobile device

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.

High fidelity geolocation using machine learning

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.

Wireless communication network audio data packet loss diagnostics and visualization system

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.

Developing a fingerprint map for determining an indoor location of a wireless device

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.

Profiling location information and network traffic density from telemetry data

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.

Enhanced algorithms for improved user experience using internet of things sensor integration

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.

User equipment including spectrum analyzer, and network device

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.

Licensed assisted access based on low utilization of unlicensed channels

The techniques described herein involve determining a context-based Quality of Experience based upon client device Quality of Experience diagnostic files in combination with client device equipment dynamics. Client device Quality of Experience (QoE) diagnostic files may indicate a reduced QoE at a client device, such as reduced signal strength or an increased number of dropped packets. User behavior during a reduced QoE event may be reflected as equipment dynamics, which may be included in equipment dynamics files. A service provider may receive information from the client device and may analyze the information to determine, with an increased confidence level, that the user device experiences a reduced QoE. Network resources may be allocated in response to the reduced QoE determination, thereby increasing a functioning of the computing network and an associated device's Quality of Experience.

Contextual quality of user experience analysis using equipment dynamics

Growing community of inventors

Mercer Island, WA, United States of America

Jie Hui