Abstract: Provided are methods, kits and compositions related to toxicity associated with administration of cell therapy for the treatment of diseases or conditions, e.g., cancer, including methods for use in predicting and treating a toxicity. In some embodiments, the toxicity is a neurotoxicity or cytokine release syndrome (CRS), such as a severe neurotoxicity or a severe CRS. The methods generally involve detecting a parameter of a biomarker or individually a parameter of each biomarker in a panel of biomarkers, such as a concentration, amount or activity, and comparing the detected parameter to a reference value for the parameter to determine if the subject is at risk for developing the toxicity, such as neurotoxicity or CRS or severe neurotoxicity or severe CRS.

Abstract: A system for generating proportional diagrams. The system includes a user interface, a memory for storing executable program code, and one or more electronic processors. The electronic processors are functionally coupled to the memory and the user interface. The electronic processors are configured to receive a dataset from one or more servers, wherein the dataset comprises software service health data. The electronic processors are further configured to remove extraneous data from the dataset, extract relevant data points from the dataset, and determine one or more relational data strings within the extracted relevant data points. The electronic processors are also configured to determine a frequency of occurrence for each of the extracted relevant data points within the dataset, and generate the proportional relationship diagram by plotting the relevant data points, wherein the relevant data points are plotted based on the determined relational data string and the determine frequency of occurrence.

Abstract: Systems and methods for automatically detecting and mitigating errors in a cloud computing environment. One example method includes receiving, from a telemetry server, telemetry data for the cloud computing environment, detecting an error within the cloud computing environment based on the telemetry data, determining an error type for the error based on the telemetry data, determining an impact severity for the error based on the telemetry data, and when the error type is a reportable error type and the impact severity exceeds a predetermined threshold, performing a mitigation action.

Abstract: Systems and methods for automatically recovering from software regression in a cloud computing environment. One example method includes determining, with an electronic processor, that a batch software update has been applied to the cloud computing environment. The method includes, in response to determining that a batch software update has been applied, transmitting a problem request to an event listener server. The method includes receiving, from the event listener server, a problem statement including a stack trace. The method includes determining, based on the stack trace, a software feature indicator. The method includes transmitting the software feature indicator to a root cause analyzer. The method includes receiving, from the root cause analyzer, a change list indicator and a relevancy score associated with the change list indicator. The method includes performing a mitigation action based on the change list indicator when the relevancy score exceeds a relevancy threshold.

Abstract: A system for detecting service issues within multi-sourced service data. The system includes a memory and one or more electronic processors coupled to the memory. The electronic processors are configured to receive one or more data sets in near real time, and to enrich the dataset with one or more metadata parameters, pre-clean the data within the dataset, and determine one or more data points within the dataset that are semantically similar to each other. The electronic processors are also configured to generate a similarity score for each of the semantically similar data points, and determine one or more significant clusters within the dataset within a predefined lookback window. The electronic processors are also configured to analyze the determined significant clusters to determine the existence of one or more service issues, and generate a service alert based on the analysis determining that one or more service issues are present.

Abstract: Disclosed herein is a system for automating the causality detection process when upgrades are deployed to different resources that provide a service. The resources can include physical and/or virtual resources (e.g., processing, storage, and/or networking resources) that are divided into different, geographically dispersed, resource units. To determine whether a root cause of a problem is associated with an upgrade event that has recently been deployed, a system is configured to use telemetry data to compute an upgrade-to-upgrade score that represents differences between two different upgrade events that are deployed to the same resource unit. The system is further configured to use telemetry data to compute an upgrade unit-to-unit score that represents differences between the same upgrade event being deployed to two different resource units. The scores can be used to output an alert, for an analyst, that signals whether a recently deployed upgrade event is the cause of a problem.

Abstract: Systems and methods for automatically detecting and mitigating errors in a cloud computing environment. One example method includes receiving, from a telemetry server, telemetry data for the cloud computing environment, detecting an error within the cloud computing environment based on the telemetry data, determining an error type for the error based on the telemetry data, determining an impact severity for the error based on the telemetry data, and when the error type is a reportable error type and the impact severity exceeds a predetermined threshold, performing a mitigation action.

Abstract: Systems and methods for automatically reducing regression for a software payload applied to a plurality of computing platforms by a software updater. One example method includes receiving a health request associated with the payload, and retrieving, from an escalation engine, a plurality of identifiers identifying a subset of the plurality of computing platforms that have completed deployment of the payload, and determining a plurality of ULS tags associated with the payload. The method includes querying an anomaly detector for failure data, including pre and post-deployment data, for the subset corresponding to the ULS tags, detecting a potential software regression associated with the payload by comparing the pre and post-deployment data, and querying a root cause analyzer based on the potential regression. The method includes receiving an identifier identifying a potential root cause for the potential regression, and transmitting an event based on the potential regression and the potential root cause.

Abstract: Systems and methods for automatically detecting and mitigating errors in a cloud computing environment. One example method includes receiving, from a telemetry server, telemetry data for the cloud computing environment, detecting an error within the cloud computing environment based on the telemetry data, determining an error type for the error based on the telemetry data, determining an impact severity for the error based on the telemetry data, and when the error type is a reportable error type and the impact severity exceeds a predetermined threshold, performing a mitigation action.

Abstract: Disclosed herein is a system for automating the causality detection process when upgrades are deployed to different resources that provide a service. The resources can include physical and/or virtual resources (e.g., processing, storage, and/or networking resources) that are divided into different, geographically dispersed, resource units. To determine whether a root cause of a problem is associated with an upgrade event that has recently been deployed, a system is configured to use telemetry data to compute an upgrade-to-upgrade score that represents differences between two different upgrade events that are deployed to the same resource unit. The system is further configured to use telemetry data to compute an upgrade unit-to-unit score that represents differences between the same upgrade event being deployed to two different resource units. The scores can be used to output an alert, for an analyst, that signals whether a recently deployed upgrade event is the cause of a problem.

Abstract: Systems and methods relate to determining whether a particular failure of a web page feature is related to a recently implemented modification, and, when applicable, automatically taking one or more actions to return the web page to a functioning state (revert the modification). One embodiment provides a system for web page recovery. The system includes at least one server including an electronic processor and memory. The memory stores instructions that, when executed by the electronic processor, cause the system to obtain a web page log, detect a failure of a feature of a web page related to the web page log, identify one or more change packages related to the web page, determine a set of possible root causes for the failure of the feature based on the one or more change packages, select one possible root cause, and revert the one possible root cause.

Abstract: Systems and methods for determining a severity level of a computing service. One system includes an electronic processor that is configured to receive telemetry data associated with one or more tenants of an online service providing services through a plurality of computing services and calculate, based on the telemetry data, a number of accesses of each of the plurality of computing services during a predetermined time period. The electronic processor is also configured to, for each of the plurality of computing services, assign a severity level to each computing service based on the number of accesses of each computing service during the predetermined time period. The electronic processor is further configured to, in response to detecting a failure of one of the plurality of computing services, initiate a response to the failure based on the severity level assigned to the one of the plurality of computing services.

Abstract: Technologies are disclosed for supervised reimaging of vulnerable computing devices with prioritization, auto healing, and pattern detection. A system for re-imaging computing devices includes a scheduler that implements a workflow for reimaging computing devices using software agents. The system also includes a supervisor that monitors state data to determine if the reimaging workflow has failed for any computing devices and for initiating an auto heal job for remediating failure of the re-imaging workflow. The system also includes a vulnerability manager that can perform various operations with respect to computing devices for which a reimaging workflow has failed a predetermined number of times. The vulnerability manager might also, or alternately, identify failure patterns (e.g. multiple instances of a reimaging workflow failing for the same reason) and initiate various actions based upon the identified patterns.

Abstract: A system for detecting service issues within multi-sourced service data. The system includes a memory and one or more electronic processors coupled to the memory. The electronic processors are configured to receive one or more data sets in near real time, and to enrich the dataset with one or more metadata parameters, pre-clean the data within the dataset, and determine one or more data points within the dataset that are semantically similar to each other. The electronic processors are also configured to generate a similarity score for each of the semantically similar data points, and determine one or more significant clusters within the dataset within a predefined lookback window. The electronic processors are also configured to analyze the determined significant clusters to determine the existence of one or more service issues, and generate a service alert based on the analysis determining that one or more service issues are present.

Abstract: Systems and methods for automatically detecting and mitigating errors in a cloud computing environment. One example method includes receiving, from a telemetry server, telemetry data for the cloud computing environment, detecting an error within the cloud computing environment based on the telemetry data, determining an error type for the error based on the telemetry data, determining an impact severity for the error based on the telemetry data, and when the error type is a reportable error type and the impact severity exceeds a predetermined threshold, performing a mitigation action.

Abstract: Systems and methods for determining a severity level of a computing service. One system includes an electronic processor that is configured to receive telemetry data associated with one or more tenants of an online service providing services through a plurality of computing services and calculate, based on the telemetry data, a number of accesses of each of the plurality of computing services during a predetermined time period. The electronic processor is also configured to, for each of the plurality of computing services, assign a severity level to each computing service based on the number of accesses of each computing service during the predetermined time period. The electronic processor is further configured to, in response to detecting a failure of one of the plurality of computing services, initiate a response to the failure based on the severity level assigned to the one of the plurality of computing services.

Abstract: Systems and methods for automatically reducing regression for a software payload applied to a plurality of computing platforms by a software updater. One example method includes receiving a health request associated with the payload, and retrieving, from an escalation engine, a plurality of identifiers identifying a subset of the plurality of computing platforms that have completed deployment of the payload, and determining a plurality of ULS tags associated with the payload. The method includes querying an anomaly detector for failure data, including pre and post-deployment data, for the subset corresponding to the ULS tags, detecting a potential software regression associated with the payload by comparing the pre and post-deployment data, and querying a root cause analyzer based on the potential regression. The method includes receiving an identifier identifying a potential root cause for the potential regression, and transmitting an event based on the potential regression and the potential root cause.

Abstract: Systems and methods relate to determining whether a particular failure of a web page feature is related to a recently implemented modification, and, when applicable, automatically taking one or more actions to return the web page to a functioning state (revert the modification). One embodiment provides a system for web page recovery. The system includes at least one server including an electronic processor and memory. The memory stores instructions that, when executed by the electronic processor, cause the system to obtain a web page log, detect a failure of a feature of a web page related to the web page log, identify one or more change packages related to the web page, determine a set of possible root causes for the failure of the feature based on the one or more change packages, select one possible root cause, and revert the one possible root cause.

Abstract: Systems and methods for automatically recovering from software regression in a cloud computing environment. One example method includes determining, with an electronic processor, that a batch software update has been applied to the cloud computing environment. The method includes, in response to determining that a batch software update has been applied, transmitting a problem request to an event listener server. The method includes receiving, from the event listener server, a problem statement including a stack trace. The method includes determining, based on the stack trace, a software feature indicator. The method includes transmitting the software feature indicator to a root cause analyzer. The method includes receiving, from the root cause analyzer, a change list indicator and a relevancy score associated with the change list indicator. The method includes performing a mitigation action based on the change list indicator when the relevancy score exceeds a relevancy threshold.

Abstract: A system for generating proportional diagrams. The system includes a user interface, a memory for storing executable program code, and one or more electronic processors. The electronic processors are functionally coupled to the memory and the user interface. The electronic processors are configured to receive a dataset from one or more servers, wherein the dataset comprises software service health data. The electronic processors are further configured to remove extraneous data from the dataset, extract relevant data points from the dataset, and determine one or more relational data strings within the extracted relevant data points. The electronic processors are also configured to determine a frequency of occurrence for each of the extracted relevant data points within the dataset, and generate the proportional relationship diagram by plotting the relevant data points, wherein the relevant data points are plotted based on the determined relational data string and the determine frequency of occurrence.