Persona-Based Task Recommendations for Enterprise Network Configuration
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Solution Overview
Problem
Managing large enterprise networks is time-consuming and requires significant support due to the complexity of tracking performance, troubleshooting, and integrating updates, which is challenging without proper understanding of the IT specialist's persona.
Innovation Solution
A task recommendation system using machine learning to analyze user and network data to determine the identity of IT specialists, generating customized task recommendations tailored to their roles and responsibilities, and providing guidance through an enterprise service cloud portal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional network management methods are used, then IT specialists can manage enterprise networks, but the process becomes time-consuming and requires significant support due to complexity
Solution Approach 1:
The system enables IT specialists to receive automated task recommendations and guidance without requiring extensive external support. The AI-powered platform analyzes user identity, device context, and task history to provide self-service capabilities that reduce dependency on provider support while improving management efficiency
Solution Approach 2:
The patent replaces traditional manual network management processes with an AI-powered automated system. Machine learning models analyze user behavior patterns, device states, and task requirements to automatically generate personalized task recommendations, substituting mechanical manual processes with intelligent automation
2Adaptability or versatility
If generic task recommendations are provided, then all users receive standard guidance, but the recommendations do not align with specific user roles and responsibilities
Solution Approach 1:
The system segments users into different identity groups based on their roles, responsibilities, and behavior patterns. By analyzing user identity characteristics and dividing the user base into distinct segments, the system can provide customized task recommendations tailored to each segment's specific needs rather than applying generic guidance to all users
Solution Approach 2:
The system performs preliminary analysis of user identity, device context, and task history before generating recommendations. By pre-processing and analyzing user characteristics in advance, the system prepares personalized recommendation profiles that align with specific user roles, eliminating the need for complex real-time analysis during task execution
Data Source
AI summary
Methods are provided in which a computing device obtains user data and network data associated with one or more assets used in an enterprise network of a user. The computing device further determines an identity of the user based on the user data and the network data and generates a task recommendation based on the identity of the user. The task recommendation includes one or more tasks having a plurality of operations that are to be performed within a predetermined time interval. The computing device further provides the task recommendation for performing one or more actions associated with configuring the enterprise network.