Abstract: The present disclosure is directed to a system and associated methods for assessing, evaluating a target business, and leveraging insights from the assessment and evaluation to provide strategy recommendations to optimize the performance of the target business. In some embodiments, the system may identify a benchmark competitor and determine a performance score for the benchmark competitor. In some embodiments, the system may determine a domain score of an identified benchmark competitor. In some embodiments, the system determines an updated performance score based on provided capability process data. In some embodiments, the system recommends solutions and/or key performance indicators (KPI) to solve a provided problem. In some embodiments, the system provides a platform to enable stakeholders, (e.g., users) in the target business to perform a design thinking process.

Abstract: The present disclosure is directed to a system and associated methods for assessing, evaluating a target business, and leveraging insights from the assessment and evaluation to provide strategy recommendations to optimize the performance of the target business. In some embodiments, the system may identify a benchmark competitor and determine a performance score for the benchmark competitor. In some embodiments, the system may determine a domain score of an identified benchmark competitor. In some embodiments, the system determines an updated performance score based on provided capability process data. In some embodiments, the system recommends solutions and/or key performance indicators (KPI) to solve a provided problem. In some embodiments, the system provides a platform to enable stakeholders, (e.g., users) in the target business to perform a design thinking process.

Abstract: Implementations described and claimed herein provide systems and methods for a cloud-based seizure management platform for personalized management of seizures while providing assured connectedness across multiple stakeholders. The systems and methods address the needs of a patient in the area of seizure management, through such functionalities as seizure detection, seizure histories and other health-related data management, stakeholder connectedness, tachyphylaxis detection, drug titration guidance, and/or treatment aggressiveness management. The system provides for access for multiple parties, including allowing neurologists and/or caregivers to monitor progression of neurological conditions on a continuous basis while at home or otherwise remote from the patient. The unique methodology of the seizure management system includes wearable technologies coupled with artificial intelligence, machine-learning algorithms, and data modeling techniques to enable personalization of care.

Abstract: The present disclosure is directed to a system and associated methods for assessing, evaluating a target business, and leveraging insights from the assessment and evaluation to provide strategy recommendations to optimize the performance of the target business. In some embodiments, the system may identify a benchmark competitor and determine a performance score for the benchmark competitor. In some embodiments, the system may determine a domain score of an identified benchmark competitor. In some embodiments, the system determines an updated performance score based on provided capability process data. In some embodiments, the system recommends solutions and/or key performance indicators (KPI) to solve a provided problem. In some embodiments, the system provides a platform to enable stakeholders, (e.g., users) in the target business to perform a design thinking process.

Abstract: A system and method for the adaptive scheduling of hourly labor cost through the use of an electronic device to predict a preferred schedule of a workforce. In particular, the computer implemented method of the present disclosure allows for the input of data in the form of employees and demand and utilizes computer processing and machine learning to model a predictive schedule based upon the input data in the form of a generated schedule. By implementing machine learning in the comparison of the generated schedule and a manager generated manager schedule, the computer processing system and method steps can predict a more efficient generated schedule over time.

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 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: 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.

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 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: 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: 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.