Abstract: Trace information representing a program object is automatically logged on a client computing system. It is uploaded to a service computing system where it is configured to be searchable on a property-by-property basis.

Abstract: An assistance service through its local client application or agent at a user's device (or devices) may collect diagnostic related information associated with the user's operating environment (physical and software operation parameters and configurations) and monitor a health of one or more applications. Upon detecting an issue or being activated by the user, the local client application or agent may perform diagnostic and/or recovery actions. In some cases, the diagnostic related signals may be sent directly by the application being monitored or diagnosed to its hosting service or the assistance service. Upon detecting a disconnect of the application being monitored or diagnosed, the local client application or agent may determine an alternative server and transmit the diagnostic related signals to the alternative server. An alert indicating the disconnect of the application may also be sent.

Abstract: An assistance service through its local client application or agent at a user's device (or devices) may collect diagnostic related information associated with the user's operating environment (physical and software operation parameters and configurations) and monitor a health of one or more applications. Upon detecting an issue or being activated by the user, the local client application or agent may perform diagnostic and/or recovery actions. In some cases, the diagnostic related signals may be sent directly by the application being monitored or diagnosed to its hosting service or the assistance service. Upon detecting a disconnect of the application being monitored or diagnosed, the local client application or agent may determine an alternative server and transmit the diagnostic related signals to the alternative server. An alert indicating the disconnect of the application may also be sent.

Abstract: Historical telemetry data can be used to generate predictions for various classes of data at various aggregates of a system that implements an online service. An anomaly detection process can then be utilized to detect anomalies for a class of data at a selected aggregate. An example anomaly detection process includes receiving telemetry data originating from a plurality of client devices, selecting a class of data from the telemetry data, converting the class of data to a set of metrics, aggregating the set of metrics according to a component of interest to obtain values of aggregated metrics over time for the component of interest, determining a prediction error by comparing the values of the aggregated metrics to a prediction, detecting an anomaly based at least in part on the prediction error, and transmitting an alert message of the anomaly to a receiving entity.

Abstract: An assistance service through its local client application or agent at a user's device for devices) may collect diagnostic related information associated with the user's operating, environment (physical and software operation parameters and configurations) and monitor a health of one or more applications. Upon detecting an issue or being activated b the user, the local client application or agent may perform diagnostic and/or recovery actions. In some cases, the diagnostic related signals may be sent directly by the application being monitored or diagnosed to its hosting service or the assistance service. Upon detecting a disconnect of the application being monitored or diagnosed, the local client application or agent may determine an alternative server and transmit the diagnostic related signals to the alternative server.

Abstract: An issue is detected in the operation of a set of integrated applications. Assistive logic is launched and an application, causing the issue, is identified. Application running logic is controlled to roll the application back to a last known good state.

Abstract: A problem with a computing system is detected, a root cause is identified and a solution is also identified. Diagnostic data is obtained and an issue signature is generated that maps the issue to failed components and product functionality.

Abstract: An assistance service through its local client application or agent at a user's device hardware and software environment information and monitor a health of one or more applications. Upon detecting an issue or being activated by the user, the assistance service may perform diagnostic and/or recovery actions. If predefined or dynamically determined recovery actions are inadequate to address the issue, the assistance service may engage a support service providing collected user device environment information and received server environment information to the support service. A history of attempted recovery actions and diagnostic results may also be provided. The assistance service may, through a user interface of its local, client or agent, facilitate communication with the supports service and perform recovery actions, suggested by the support service providing feedback to the support service.

Abstract: Trace information representing a program object is automatically logged on a client computing system. It is uploaded to a service computing system where it is configured to be searchable on a property-by-property basis.

Abstract: Trace information representing a program object is automatically logged on a client computing system. It is uploaded to a service computing system where it is configured to be searchable on a property-by-property basis.

Abstract: Historical telemetry data can be used to generate predictions for various classes of data at various aggregates of a system that implements an online service. An anomaly detection process can then be utilized to detect anomalies for a class of data at a selected aggregate. An example anomaly detection process includes receiving telemetry data originating from a plurality of client devices, selecting a class of data from the telemetry data, converting the class of data to a set of metrics, aggregating the set of metrics according to a component of interest to obtain values of aggregated metrics over time for the component of interest, determining a prediction error by comparing the values of the aggregated metrics to a prediction, detecting an anomaly based at least in part on the prediction error, and transmitting an alert message of the anomaly to a receiving entity.

Abstract: Personalized diagnostics, troubleshooting, recovery, and notification based on application state is provided. In some examples, system, application, and device level configuration and usage data may be collected as telemetry data. Upon detection of a crash or similar problem, or upon user activation, an assistance service and/or a local assistance application component may execute diagnostics on the crashed application based on the telemetry data, user credentials, known problems, and other factors which may be recorded by the assistance application and/or other system elements. Suitable recovery actions may be taken. If recovery actions are unsuccessful, diagnostic information may be provided to a support system and the issue elevated. Moreover, collected information may be provided to a system database for generating data insights and determining error patterns.

Abstract: Historical telemetry data can be used to generate predictions for various classes of data at various aggregates of a system that implements an online service. An anomaly detection process can then be utilized to detect anomalies for a class of data at a selected aggregate. An example anomaly detection process includes receiving telemetry data originating from a plurality of client devices, selecting a class of data from the telemetry data, converting the class of data to a set of metrics, aggregating the set of metrics according to a component of interest to obtain values of aggregated metrics over time for the component of interest, determining a prediction error by comparing the values of the aggregated metrics to a prediction, detecting an anomaly based at least in part on the prediction error, and transmitting an alert message of the anomaly to a receiving entity.

Abstract: A problem with a computing system is detected, a root cause is identified and a solution is also identified. Diagnostic data is obtained and an issue signature is generated that maps the issue to failed components and product functionality.

Abstract: Historical telemetry data can be used to generate predictions for various classes of data at various aggregates of a system that implements an online service. An anomaly detection process can then be utilized to detect anomalies for a class of data at a selected aggregate. An example anomaly detection process includes receiving telemetry data originating from a plurality of client devices, selecting a class of data from the telemetry data, converting the class of data to a set of metrics, aggregating the set of metrics according to a component of interest to obtain values of aggregated metrics over time for the component of interest, determining a prediction error by comparing the values of the aggregated metrics to a prediction, detecting an anomaly based at least in part on the prediction error, and transmitting an alert message of the anomaly to a receiving entity.

Abstract: Trace information representing a program object is automatically logged on a client computing system. It is uploaded to a service computing system where it is configured to be searchable on a property-by-property basis.

Abstract: An issue is detected in the operation of a set of integrated applications. Assistive logic is launched and an application, causing the issue, is identified. Application running logic is controlled to roll the application back to a last known good state.

Abstract: Historical telemetry data can be used to generate predictions for various classes of data at various aggregates of a system that implements an online service. An anomaly detection process can then be utilized to detect anomalies for a class of data at a selected aggregate. An example anomaly detection process includes receiving telemetry data originating from a plurality of client devices, selecting a class of data from the telemetry data, converting the class of data to a set of metrics, aggregating the set of metrics according to a component of interest to obtain values of aggregated metrics over time for the component of interest, determining a prediction error by comparing the values of the aggregated metrics to a prediction, detecting an anomaly based at least in part on the prediction error, and transmitting an alert message of the anomaly to a receiving entity.

Abstract: An assistance service through its local client application or agent at a user's device hardware and software environment information and monitor a health of one or more applications. Upon detecting an issue or being activated by the user, the assistance service may perform diagnostic and/or recovery actions. If predefined or dynamically determined recovery actions are inadequate to address the issue, the assistance service may engage a support service providing collected user device environment information and received server environment information to the support service. A history of attempted recovery actions and diagnostic results may also be provided. The assistance service may, through a user interface of its local, client or agent, facilitate communication with the supports service and perform recovery actions, suggested by the support service providing feedback to the support service.

Abstract: Personalized diagnostics, troubleshooting, recovery, and notification based on application state is provided. In some examples, system, application, and device level configuration and usage data may be collected as telemetry data. Upon detection of a crash or similar problem, or upon user activation, an assistance service and/or a local assistance application component may execute diagnostics on the crashed application based on the telemetry data, user credentials, known problems, and other factors which may be recorded by the assistance application and/or other system elements. Suitable recovery actions may be taken. If recovery actions are unsuccessful, diagnostic information may be provided to a support system and the issue elevated. Moreover, collected information may be provided to a system database for generating data insights and determining error patterns.