Microsoft Technology Licensing, LLC

T-mobile Usa, Inc.

Vvc Holding Corporation

Wei Huang

Patrick John Baumgartner

Drew Voegele

Joseph Milan

John Hancock

Zhongyuan Li

Eric Wu

Pei Zheng

Bin Wang

Robert Zhu

Ying Chin

Pengxiang Zhao

Vikas Ranjan

Dejun Zhang

Lance Paul Lukens

Satyendra Bahadur

Xiao Zhang

Anandajothi Muttayane

Anselmo Myungsup Shim

Christian A Hernandez Sanchez

Brian A Pokrifka

Carla M Difranco

William P Donkervoet

Johnderic V Sicat

Jau-Huei Lin

Aftab Hassan

Joshua B Bruno

Rajeswari Rajagopalan

Xuejun Chen

Systems and methods for selectively allowing or blocking operating system upgrades based on probabilities that the upgraded operating system may result in a fault condition. In an example, the technology may perform a method that includes receiving usage data from a client device to evaluate providing an operating system upgrade for the client device; accessing a root-cause factor that is likely to lead to a fault condition with the operating system upgrade; based on the usage data, identifying a fault probability for the root-cause factor; comparing the fault probability to a fault threshold for the root-cause factor; based on the comparison of the fault probability and the fault threshold, performing one of: blocking the operating system upgrade from being installed on the client device; or providing the operating system upgrade for installation on the client device.

Controlling operating system upgrades based on usage data of client device

A machine learning system automatically diagnoses and resolves issues in a telecommunications network. When a customer reports a network issue using a mobile application, the device performs a diagnostic test, such as a speed test. In addition, network logs or performance metrics during occurrence of the network issue are collected. The results of the diagnostic are used as inputs to a machine learning model in combination with the network logs or metrics to predict the cause of the network issue or to perform a corrective action.

Machine learning system for predicting network abnormalities

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

Methods and systems are provided for mapping clinical terminology with natural language processing. In one embodiment, an example method includes generating a word relationship graph for a plurality of mappings between a first code set and a second code set, receiving a first code of the first code set, and automatically mapping a second code of the second code set to the first code based on the word relationship graph. In this way, seemingly different code descriptions from different medical vocabularies may be automatically mapped to each other with reduced processing and reduced human intervention.

Clinical terminology mapping with natural language processing

File exploration is facilitated by enabling zoom with respect to a thumbnail as a function of an identified point of interest. More particularly, a scaled thumbnail of the same size as a thumbnail can be presented as a function of an identified point of interest. Furthermore, navigation, among other things, is enabled to allow panning with respect to a scaled thumbnail, for instance.

Thumbnail zoom

Apparatus, systems, devices, other articles of manufacture and associated methods are disclosed and described herein to process medical data to generate a classification of the medical data using artificial intelligence. An example apparatus includes a processor to execute instructions to implement a history of past illness (HPI) receiver to receive an HPI formatted as a string, the string including one or more words, the words organized in sentences, a natural language processor to tokenize the one or more words into tokens based on a context associated with at least one of the one or more words and a tensor generator to convert the tokens into hashes, each of the hashes forming a dimension of a tensor based on the context. The apparatus further includes a neural network to embed each of the hashes into vectors, process the vectors to classify the HPI as extended or brief based on a similarity to a set of classified HPIs and output a classification for the HPI. The apparatus further includes an electronic medical record modifier to modify an electronic medical record with the HPI and the classification and to trigger an action with respect to the electronic medical record based on the classification.

Medical system interface apparatus and methods to classify and provide medical data using artificial intelligence

A communications link between a computing device and an external device is monitored. A driver for the communications link is executed on the computing device. The driver is configured to monitor data traffic over the communications link. Data indicative of the monitored data traffic is received from the driver. The data is sent to an analysis function configured to identify a condition of the communications link based on accumulated data indicative of the data traffic. Data indicative of an identified condition of the communications link is received from the analytics function. An indication of the identified condition is rendered on a display device of the computing device.

Monitoring peripheral transactions

A system generates a command to cause a computing machine to collect information for an issue that occurred with an application. The command is stored in a command list and associated with a computing machine identifier for the computing machine. The system receives a query from the computing machine for the command list. The query includes the computing machine identifier and the system locates the command in the command list based on the command being associated with the computing machine identifier. The system then sends the command to the computing machine where the command dynamically causes the computing machine to collect the information for the issue with the application.

Dynamic command generation for automated control of computing machines

File exploration is facilitated by enabling zoom with respect to a thumbnail based on an identified point of interest. More particularly, a scaled thumbnail of the same size as a thumbnail can be generated and presented based on an identified point of interest. In one instance, a scaled thumbnail can be generated automatically by repositioning an image based on a point of interest and applying a scaling factor to the image behind a fixed clipping mask of the same size as the thumbnail. Furthermore, navigation, among other things, is enabled to allow panning with respect to a scaled thumbnail.

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Bellevue, WA, United States of America

Wei Huang