T-mobile Usa, Inc.

Peter P Myron

Michael J Mitchell

Samson Kim-Sun Kwong

Jie Hui

Jeffery A Smith

Kranthi Sontineni

Himesh Bagley

Samson Kwong

Zachary William McPherson

Michael Mitchell

Sarvesh Kaushal

Rohit Iyer

Evan Fitzpatrick

A suitability analysis platform may receive one or more environmental parameter values of one or more environmental parameters collected for a plurality of sites by multiple sensor devices. The platform may aggregate the one or more environmental parameter values collected for each site of the plurality of sites by each site. Subsequently, the platform may receive queries for suitable sites for performing activities from user devices, in which each of the queries includes one or more environmental parameter value criteria for at least one corresponding environmental parameter of a suitable site for performing a specific activity. The platform may provide responses to the queries in which each response includes information on one or more sites of the plurality of sites with one or more environmental parameter values that match or are closest to the one or more environmental parameter value criteria of the corresponding query.

Suitability metrics based on environmental sensor data

Systems and methods for generating encoded text representations of spoken utterances are disclosed. Audio data is received for a spoken utterance and analyzed to identify a nonverbal characteristic, such as a sentiment, a speaking rate, or a volume. An encoded text representation of the spoken utterance is generated, comprising a text transcription and a visual representation of the nonverbal characteristic. The visual representation comprises a geometric element, such as a graph or shape, or a variation in a text attribute, such as font, font size, or color. Analysis of the audio data and/or generation of the encoded text representation can be performed using machine learning.

Generating encoded text based on spoken utterances using machine learning systems and methods

This disclosure describes techniques that enable a Meeting Expense and Verification (MEV) controller to infer the meeting expense of a proposed meeting. The MEV controller may analyze a meeting request to identify meeting attendees, and in doing so, determine the meeting expense. In response to the meeting expense being greater than a predetermined meeting expense threshold, the MEV controller may suspend the meeting request.

Inferring meeting expense via a meeting expense and verification controller

Systems and methods for remotely resetting networked devices is disclosed. The system can comprise a primary network connection and a secondary network connection to provide communications redundancy. The system can also include physical connections between networked devices to enable devices to be reset regardless of the type of error. The method can include monitoring one or more parameters of a device to determine what type of error is occurring and what action to take. The system can enable a first computer (or other electronic device) to restart applications, adapters, and services on a second computer (or other electronic device). The system can also enable the first computer to reset or restart the second computer. The system can enable networked computers to monitor and restore operation to other computers on the same network without human intervention.

Systems and methods for improved uptime for network devices

Adaptive automated testing methods and systems are disclosed. In an embodiment, an automatic test controller of an automated test system may collect a log of a plurality of actions executed by a plurality of devices of the automated test system and apply a heuristic to the log as the automated test system operates to determine whether to execute a next action by a device of the automated test system. When the next action is to be executed, the automatic test controller automatically directs the device to execute the next action. Otherwise, the automatic test controller indicates the next action was intentionally skipped by recording such omission in the log and automatically skipping the next action.

Adaptive automated testing methods and systems

Described herein are techniques, devices, and systems for using a machine learning model(s) and/or artificial intelligence algorithm(s) to optimize testing of components of a system operated by a wireless carrier. For example, data generated as a result of executing a first test of a suite of tests may be provided as input to a trained machine learning model(s) to classify one or more tests of the suite of tests as having a particular characteristic. A to-be-executed test may be classified as likely to pass or likely to fail when executed, for example. An already-executed test may be classified as reliable or unreliable, as another example. Based on the classification of the test(s), the suite of tests may be modified to optimize testing of the wireless carrier's system.

Using machine learning to optimize wireless carrier system testing

Systems may comprise a connectivity manager application operating on a User Equipment (UE) for optimal and secure performance of local applications and improving user experience by optimizing application performance (e.g., by prioritizing file downloads). The systems may modify data transmission of other local applications on the UE in a multi-network environment. The connectivity manager application may detect one or more network characteristics of one or more networks providing service to the UE and determine a connectivity rule based at least in part on the one or more network characteristics and/or particular data types and indicating that data transmission for the one or more transmission paths is to be adjusted, modified, paused, or prohibited in response to particular network conditions, etc. The connectivity manager application may cause modifications to the transmission paths and present indications of the modifications at a graphical user interface (GUI) or via application programming interfaces and call-backs.

Managing local application connectivity in a multi-network environment

Systems and methods for monitoring mobile devices in a controlled environment are provided. One or more mobile devices may be accessible through a communication port of the mobile device. One or more monitoring devices may be configured to monitor the one or more mobile devices. A processor in communication with the one or more monitoring devices may control the monitoring devices and receive signals from the monitoring devices. A server in communication with the mobile devices may create a communication channel with each of the mobile devices, with each communication channel accessible through a network for use by software developers and testers. Thus, monitoring and detection of malfunctions or other abnormalities of the mobile devices is achieved, and remediation may be performed.

System and method of monitoring resilience and safety of electronic devices during testing

Modulating and optimizing operation parameters such as power usage of wireless networks include determining a baseline reference level for a network demand at a base station in a network during a first time interval. The transmission power of the base station corresponding to the baseline reference level is determined, and the network demand at the base station in the network is forecasted during a second time interval. A difference between the projected network demand and the baseline reference level at the second time is determined. The transmission power of the base station is adjusted by a predetermined increment during the second time interval based at least on the difference.

Dynamic operation parameter optimization and modulation of a wireless network

Systems and methods are provided for using automated mobile computing device testing to ensure that a given targeted message is actually appearing in an intended manner for various types of intended recipients. The intended content or format of a target message to be presented by each of a plurality of mobile computing devices may be based on characteristics of each device. The targeted message may be sent to each device, and an indication of the actual content and/or format of the targeted message as presented by each device may be received and compared to the intended content or format of the targeted message for each device. Errors associated with the targeted message may be detected based on any discrepancies between the intended content and/or format of the targeted message for each device and the actual content or format of the targeted message as presented by each device.

Targeted message validation for mobile devices

Systems and methods are provided for testing features of a mobile device application based on their likely use by various user classes or personas. Each of a plurality of mobile computing devices may be associated with respective user classes that each have a respective set of characteristics. A particular mobile computing device application may be installed on each of the plurality of mobile computing devices, and each of the mobile computing devices may attempt to execute the same feature of the application. When any of the mobile computing devices fails to execute the feature, an indication of the failure may be logged, along with an indication of the user class associated with the mobile computing device that failed to execute the feature, and an error associated with the feature for one or more user classes may be detected based on the logged indication(s).

Mobile device testing based on user class

A Honeypot Adaptive Security (HAS) system is described that determines whether a pre-loaded partner or third-party (PP-TP) application executed on a client device has gained over-privileged access to confidential client or network (CCN) data, or over-privileged use of client account features or information provided by a telecommunications service provider. The HAS system is configured to retrieve client data associated with a PP-TP application executed on the client device, retrieve policy rules associated with the PP-TP application, generate a PP-TP application data model to determine a probability of the PP-TP application gaining over-privileged access to CCN data or over-privileged use of client account features on a client device. Responsive to the determined probability, the HAS system is configured to deploy a solution data package to the client device that resolves the instance of over-privileged access or over-privileged use.

Honeypot adaptive security system

The techniques described herein involve analysis of communication data included in trace file(s) of device(s) involved in a communication. These trace file(s) may each include data associated with multiple layers of a communication protocol stack of a respective device or data associated with a single such layer. The techniques may further involve one or more of determination of performance metrics associated with data at a specific layer of a specific device, correlation of the data between layers of a device, or correlation of data across multiple device(s) involved in the communication. The performance metrics or correlated data may then be analyzed based on thresholds or models to determine whether the performance metrics or correlated data exhibits a degraded quality of user experience. Also or instead, graphic or textual representations of the performance metrics or correlated data may be generated.

Quality of user experience analysis

In a software testing environment, a test script may be designed to search among outputs of an in-test target application for an expected output element having a specified property. A test execution engine executes the test script, and if such an output is not found, and the test script returns a 'fail' result, the test execution engine revises the test script so that rather than searching for the originally specified property, the test script searches for a different property, where the different property is a property that the expected output element was observed to have during a previous execution of the test script. The test execution engine then executes the revised test script and reports its results.

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Renton, WA, United States of America

Peter P Myron