Multi-Tenant Feed Platform for AI-Prioritized Cross-Tenant Support
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current remote monitoring management (RMM) systems are inefficient for IT support staff, particularly in cross-tenant scenarios, leading to increased resource requirements and IT costs due to the complexity of managing multi-tenant environments and difficulty in prioritizing tasks across tenants.
Innovation Solution
An AI-driven multi-tenant feed and insights platform that collects event data across tenants, generates actionable insights, and delivers them via a unified communication channel, using an AI model to rank and prioritize insights based on predicted success scores, facilitating efficient task prioritization and collaboration among IT support personnel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If IT personnel manually manage multi-tenant environments and pull data from multiple tenants, then they can complete IT tasks, but the complexity of managing multi-tenant environments increases and IT costs rise
Solution Approach 1:
The patent merges data from multiple tenants into a unified feed that consolidates events and insights across all tenants. Instead of IT personnel manually pulling data from each tenant separately, the system combines all tenant data into a single integrated view, reducing management complexity while maintaining productivity.
Solution Approach 2:
The patent introduces an intermediary processing layer that automatically aggregates, filters, and prioritizes events from multiple tenants before presenting them to IT personnel. This intermediary feed acts as a mediator between the complex multi-tenant environment and the IT staff, simplifying their workload.
2Ease of operation
If IT personnel access data from multiple tenants to complete tasks, then they can provide comprehensive support, but the time required to figure out which tenant a user is operating in increases
Solution Approach 1:
The patent combines tenant identification information with user events in a unified feed. When an event occurs, the feed automatically includes contextual information about which tenant the user belongs to, eliminating the need for IT personnel to separately determine tenant affiliation and reducing time loss.
3Loss of information
If the system provides comprehensive insights from all tenants, then IT support staff can make informed decisions, but the information overload makes it difficult to prioritize tasks
Solution Approach 1:
The patent applies local quality by providing different levels of information detail and prioritization to different users or contexts. The feed can be customized to highlight specific types of events or tenants based on user roles and preferences, making task prioritization easier while maintaining access to comprehensive information when needed.
Solution Approach 2:
The patent changes parameters such as event priority, severity levels, and filtering criteria to transform comprehensive raw data into prioritized actionable insights. By adjusting these parameters, the system maintains information completeness while presenting data in a prioritized manner that facilitates easier task management.
4Productivity
If the system aggregates and delivers feeds from multiple tenants, then collaboration among IT support personnel is improved, but the resource requirements and IT costs increase
Solution Approach 1:
The patent creates a universal feed system that serves multiple functions: it provides real-time monitoring, enables collaboration, prioritizes tasks, and reduces resource requirements through intelligent filtering. This multi-functional approach delivers comprehensive collaboration benefits while optimizing resource utilization across the system.
Data Source
AI summary
A remote monitoring and management (RMM) system is configured to receive a stream of events generated in response to interactions of users from multiple tenants with one or more applications and store the events in a database. A plurality of different insight types is defined for one or more event types for the events. Insights of the different insight types are generated based on the events in the database, the event types of the events, and numbers of events of the event types. The insights are ranked using an artificial intelligence (AI) model trained to generate a predicted success score for each of the insights. A predetermined number of top insights are selected based on the ranking of the insights and aggregated into a feed. The feed is to at least one computing device associated with the RMM system.