Abstract: A method for automatically identifying a root cause of customer dissatisfaction with a software product and creating feedback items to improve the software product includes collecting engagement data pertaining to interactions between a customer and a flow of visual elements presented by the software product and detecting a trigger event indicating that the customer is dissatisfied with the software product. In response to the trigger event and based at least in part on the engagement data, a potential deficiency of the software product is automatically identified and a repair ticket is generated for a development team. The repair ticket identifies the potential deficiency of the software product.

Abstract: A computing system in a server environment receives a diagnostic data package, corresponding to a problem, from a client computing system and runs a problem-specific analyzer to generate additional problem-specific data. A root cause for the problem is identified along with a recovery action, based on the diagnostic data package and the additional problem-specific data. The root cause and recovery action are communicated to the client computing system.

Abstract: A computing system in a server environment receives a diagnostic data package, corresponding to a problem, from a client computing system and runs a problem-specific analyzer to generate additional problem-specific data. A root cause for the problem is identified along with a recovery action, based on the diagnostic data package and the additional problem-specific data. The root cause and recovery action are communicated to the client computing system.

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: A method, performed by a computing system deployed in a server environment, comprises receiving, from a client computing device that is remote from the server environment, a problem scenario identifier that identifies a problem scenario indicative of a problem associated with the client computing device, identifying a problem-specific diagnostic analyzer, that is specific to the problem associated with the client computing device, based on mapping information that maps the problem scenario to the problem-specific diagnostic analyzer, running the problem-specific diagnostic analyzer to obtain problem-specific diagnostic data that is specific to the problem associated with the client computing device, the problem-specific diagnostic data including first data associated with the client computing device and second data associated with the server environment, identifying a suggested recovery action based on the problem-specific diagnostic data, and communicating the suggested recovery action to the client computin

Abstract: A client computing system detects when a problem is encountered with an application and identified problem-specific diagnostic data that is to be collected, given the detected problem. It also identifies one or more problem-specific test or diagnostic analyzers and executes those analyzers to generate additional data. The data that is collected and the data that is generated by the analyzers is packaged and sent to a service computing system for identifying a remedial action that can be performed to address the problem. The remedial action is received and surfaced for the user.

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 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: A method, performed by a computing system deployed in a server environment, comprises receiving, from a client computing device that is remote from the server environment, a problem scenario identifier that identifies a problem scenario indicative of a problem associated with the client computing device, identifying a problem-specific diagnostic analyzer, that is specific to the problem associated with the client computing device, based on mapping information that maps the problem scenario to the problem-specific diagnostic analyzer, running the problem-specific diagnostic analyzer to obtain problem-specific diagnostic data that is specific to the problem associated with the client computing device, the problem-specific diagnostic data including first data associated with the client computing device and second data associated with the server environment, identifying a suggested recovery action based on the problem-specific diagnostic data, and communicating the suggested recovery action to the client computin

Abstract: A computing system in a server environment receives a diagnostic data package, corresponding to a problem, from a client computing system and runs a problem-specific analyzer to generate additional problem-specific data. A root cause for the problem is identified along with a recovery action, based on the diagnostic data package and the additional problem-specific data. The root cause and recovery action are communicated to the client computing system.

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 client computing system detects when a problem is encountered with an application and identified problem-specific diagnostic data that is to be collected, given the detected problem. It also identifies one or more problem-specific test or diagnostic analyzers and executes those analyzers to generate additional data. The data that is collected and the data that is generated by the analyzers is packaged and sent to a service computing system for identifying a remedial action that can be performed to address the problem. The remedial action is received and surfaced for the user.

Abstract: A computing system in a server environment receives a diagnostic data package, corresponding to a problem, from a client computing system and runs a problem-specific analyzer to generate additional problem-specific data. A root cause for the problem is identified along with a recovery action, based on the diagnostic data package and the additional problem-specific data. The root cause and recovery action are communicated to the client computing system.

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: 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.