Dynamic At-Risk System Report Delivery via Remote Intermediary
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Solution Overview
Problem
Existing methods for assessing at-risk system health are either static, requiring software updates and limited by hardware constraints, or require users to manually retrieve risk assessments from separate support servers, leading to delayed preventative action.
Innovation Solution
Implementing a dynamic system where at-risk systems send health data to a support entity for real-time risk assessment and solution updates, which are then pushed back to the system's native interface, eliminating the need for manual retrieval and separate interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If risk assessment is performed locally at the at-risk system, then the system can provide static risk profiles and solutions, but the system is limited by hardware constraints and requires software updates to be taken offline
Solution Approach 1:
A support entity acts as an intermediary between the at-risk system and the risk assessment database. The support entity receives health data from the at-risk system, performs risk assessment remotely, and pushes assessments back to the system. This mediator approach allows dynamic updates without requiring the at-risk system to have local storage capabilities or go offline for software updates.
Solution Approach 2:
The at-risk system automatically sends health data to the support entity and receives risk assessments without manual intervention. The system integrates the risk assessment into its native interface automatically, eliminating the need for users to manually retrieve assessments from separate support servers.
2Loss of information
If users manually retrieve risk assessments from separate support servers, then the assessments can be obtained, but the process takes users out of their workflow and delays preventative action
Solution Approach 1:
The support entity serves as a mediator that automatically delivers risk assessments to the at-risk system's native interface. This eliminates the need for users to manually navigate to separate support servers, keeping them within their existing workflow and enabling timely preventative action.
Solution Approach 2:
The system implements automatic feedback loops where the at-risk system sends health data to the support entity, which then pushes risk assessments back to the system. This automated feedback mechanism ensures continuous monitoring and immediate delivery of risk information without user intervention.
3Difficulty of detecting and measuring
If a separate support server interface is used for risk assessment, then remote diagnostics can be performed, but additional system resources are required and user workflow is disrupted
Solution Approach 1:
The support entity acts as a remote intermediary that performs diagnostics externally. This allows comprehensive system diagnostics to be conducted without requiring additional local resources at the at-risk system, as all processing occurs at the support entity.
Solution Approach 2:
The risk assessment and diagnostic information are pushed back to the at-risk system's native interface as data copies. This allows users to access complete diagnostic information within their existing interface without requiring duplicate diagnostic capabilities or additional system resources.
Data Source
AI summary
The present application describes techniques for providing dynamic at-risk system reports at the site of the at-risk system. The ARS may send information about system health (e.g., values for system health parameters) to a support entity, and may receive a risk assessment from the support entity. The ARS may, alternatively or in addition, receive support information from the support entity. Risk assessments and recommendations may be surfaced on a user interface of the ARS. New risk profiles, risk assessments, and support recommendations may be developed and loaded onto the support entity to provide dynamically updatable support solutions.