T-mobile Usa, Inc.

Samsung Electronics Co., Ltd.

T-mobile Innovations LLC

Microsoft Technology Licensing, LLC

Pei Zheng

Yu Wang

Zhiyun Li

Tor Fredericks

Dong Chen

Anqi Luo

Hui Shen

Abhishek Abhishek

Minseok Choi

Poovanpilli G Madhavan

Wei Huang

Sharad Mittal

Ankur Agiwal

Qia Wang

Xiao Zhang

Sung Hwan Lee

Pei Huang

Jae Young Lee

Jiaming Liu

Min Zhou

Daping Guo

Johnderic V Sicat

Described herein are techniques that may be used to automate testing of services on mobile devices using visual analysis. In some embodiments, a machine learning model is trained using interaction data received from a number of mobile devices by correlating user selections with visual elements (e.g., icons). During execution of a testing routine on a mobile device, screenshots are obtained of a screen of the mobile device and provided to the machine learning model. An action is generated based on the provided screenshot that simulates a user action (e.g., a user touch on the screen of the mobile device) at a location of an icon or other visual element associated with the testing routine. These steps are repeated until an end-state of the testing routine is detected.

Automated testing of mobile devices using visual analysis

The disclosure herein describes intelligently managing distribution of tests to user devices. Test definition data of a target test is obtained by a processor of a testing platform. A set of user devices is identified for executing the target test from a plurality of available user devices. Identifying the set of user devices includes, for each user device, analyzing device configuration data and predicted usage context data using test requirements of the test definition data, identifying a likelihood that the user device will enter a required device state during a test interval of the target test, and determining an average likelihood data value for the set of user devices satisfies the test requirements. Test execution control signals are sent to each user device of the set of user devices and test results associated with execution of the target test are received from the set of user devices.

Intelligently managing automatic performance of distributed tests

A data router serves User Equipment (UEs) over network connections to network slices. The data router receives a container configuration and a connection configuration from a wireless communication network. The data router exchanges user data with the UEs. The data router executes data applications in operating system containers based on the container configuration. The data applications exchange the user data with the network slices over the network connections based on the connection configuration.

Data router connectivity to wireless communication slices

To detect 'silent' crashes of communications processors in a mobile device, the mobile device analyzes signal strength icon images using a trained image processing model. During operation of a mobile device, the device captures images of a signal strength icon that is displayed by the device and that visually represents a telecommunications signal strength detected by the mobile device. The mobile device applies the captured images to the trained image processing model, which is configured to output a prediction of whether the communications processor in the mobile device crashed.

Image-based detection of modem crashes in mobile telephonic devices

To reduce the likelihood that test E911 calls will be improperly connected to a public safety access point (PSAP), while ensuring that mobile devices sold to consumers are correctly routed to a PSAP in an emergency, a system for automatically redirecting test E911 calls to an answering system is disclosed. An unstructured supplementary service data (USSD) node receives a first USSD message from a mobile device during E911 testing procedure at the mobile device. In response to identifying a first code in the first USSD message, the USSD node retrieves an identifier associated with the mobile device. The USSD node sends an instruction to a gateway mobile location center (GMLC) system to redirect E911 calls from the mobile device based on the retrieved identifier such that a subsequent E911 call placed by the mobile device is automatically rerouted by the GMLC to an answering line instead of a PSAP.

Automatic redirecting of enhanced 911 calls

A chatbot executes on a user device to gather performance data associated with an application on the user device. During a diagnostics chat with a user, the chatbot receives a plurality of answers from a user of the user device to a plurality of prompts sequentially output by the chatbot to the user regarding a performance issue of an application on the user device. The chatbot also retrieves, from the user device, contextual data of the application during the performance issue. The chatbot packages the answers received from the user and the retrieved contextual data to enable diagnosis of the performance issue of the application.

Interactive diagnosis of application performance using a chatbot

By analyzing the apps on a portable computing device, the communication modes used by the portable computing devices and the communication requirements of the apps at a given time, an ideal communication mode given a certain mix of apps operating on a portable computing device at a given point in time may be determined.

Method and system to select a communication mode

A data router serves User Equipment (UEs) over network connections to network slices. The router executes an operating system, and in response, executes router applications in containers. The router applications receive a container configuration, a connection configuration, and a slice configuration from the wireless communication network. The router applications transfer the container configuration to the operating system. The operating system controls processing qualities of the containers based on the container configuration. The router applications generate and transfer the performance information for the network connections to the wireless communication network. The router applications prioritize the network connections to the network slices based on the slice configuration. The router applications exchange user data with the UEs and exchange the user data with the network slices over the network connections using service qualities based on the connection configuration.

The disclosure herein describes intelligently managing distribution of tests to user devices. Device data is obtained from a user device via a network connection and a usage prediction model associated with the user device is generated based on the device data. Predicted usage context data of the user device is determined based on the usage prediction model. A test scheduled to be performed using distributed testing is identified based on the test including test requirements that are satisfied by the device data and the predicted usage context data of the user device. Test instructions of the identified test are sent to the user device. Based on the test instructions causing the user device to automatically execute the identified test, test results are received from the user device. As a result, the testing platform enables performance of distributed testing across user devices with minimal interaction with users of the user devices.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a network feature tester are disclosed. In one aspect, a method includes the actions of selecting computing devices that are configured to communicate with a wireless carrier network based on the computing devices having a common characteristic. The actions further include identifying a subset of the computing devices that are likely to communicate with a same base station of the wireless carrier network during a first period of time and a second period of time. The actions further include determining first monitoring data during the first period of time. The actions further include adjusting a characteristic of the same base station. The actions further include determining second monitoring data during the second period of time. The actions further include determining an effect that adjusting the characteristic has on the wireless carrier network.

Network feature tester

Described herein are techniques for implementing intelligent network slicing. Such techniques may include receiving data usage information associated with at least one user and generating, using the data usage information, a predicted usage model for the at least one user. Upon identifying an upcoming predicted usage event from the predicted usage model, the techniques may further include determining a current status of a core network and upon determining, based on the current status of the core network, that the core network is inadequate to handle the upcoming predicted usage event, instantiating a new network slice within the core network configured to handle the upcoming predicted usage event. Upon identifying network traffic associated with the upcoming predicted usage event, the techniques comprise routing that network traffic through the new network slice.

Intelligent 5G network slicing

Techniques for automatically selecting device tests for testing devices configured for operation in wireless communication networks, based upon maintenance releases (MRs) received from original equipment manufacturers. When an MR with changes for a device is received, the MR may be analyzed in order to determine what the changes pertain to with respect to the device. The changes may be clustered with respect to requirements for the changes and a knowledge base may be consulted by a recommendation engine in order to determine candidate tests for testing the MR. The candidate tests may be based upon previous tests, failed tests and, relevant tests. Based at least in part on the identified previous tests, failed tests and relevant tests, one or more tests may be selected for testing devices with respect to the newly received MR.

System for recommending tests for mobile communication devices maintenance release certification

Solutions for validating a customized user equipment (UE), prior to permitting the UE to register on a cellular network, include: receiving a set of requirement queries into a validation tool, the set of requirement queries including at least one software application identification, at least one user interface (UI) configuration, and at least one network connectivity configuration; receiving a set of requirements into the validation tool, the set of requirements corresponding with the set of requirement queries; receiving, from a customization tool on the UE, UE configuration information corresponding with the set of requirement queries; determining whether the UE configuration information meets the set of requirements; based on at least the UE configuration information meeting the set of requirements, generating a validation report for the UE; and transmitting the validation report over a network. This may require installing and configuring the customization tool on the UE to collect the configuration information.

Validating customized user equipment (UE)

A processor-implemented method includes integrating telecommunication network provider performance analytics with vendor testing automation such that specific data from vendor user equipment (UE) testing can be filtered out and the performance data results represent true service performance for the desired UEs. A network device associated with the network provider may establish an application programming interface (API) with a vendor device associated with the vendor to receive vendor testing information used to identify UEs undergoing vendor testing. The network device may identify the vendor UEs undergoing vendor testing when performing measurements on a group of UEs, such as key performance indicator (KPI) measurements.

Growing community of inventors

Sammamish, WA, United States of America

Pei Zheng