Spanning Content Trees for Personalized Network Configuration

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Solution Overview

Problem

Existing support content for network configurations is manually created, often outdated, and lacks personalization, leading to inefficiencies and high abandonment rates due to mismatched user skills and enterprise asset variations.

Innovation Solution

A spanning content tree generator utilizing generative artificial intelligence to create personalized, up-to-date support content tailored to user personas and enterprise network configurations, incorporating user feedback and network information to automate configuration tasks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual content creation is used, then content can be reviewed for accuracy, but content creation is time-consuming and lacks personalization

Engineering Contradiction:

Improvecontent accuracyVSAvoidcontent creation time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables self-service content generation where the AI model automatically creates personalized support content based on user profiles and network information without requiring manual intervention for each content item. The system serves itself by automatically retrieving relevant information, generating appropriate content, and delivering it to users.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual content creation process with an AI-based automated system. The AI model substitutes human writers by automatically generating support content through machine learning, replacing the mechanical process of manual writing and review with automated information processing and content generation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of manufacture

If generic support content is created, then content creation is simplified, but content relevance to individual users decreases

Engineering Contradiction:

Improvecontent creation simplicityVSAvoidcontent personalization

Core Design Contradiction:

Ease of manufactureVSAdaptability or versatility

Solution Approach 1:

The system applies local quality by tailoring support content to specific users based on their individual profiles, skill levels, and network configurations. Instead of uniform generic content, the system generates localized personalized content for each user, adapting the support information to their specific needs and context.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The support content system becomes dynamic by automatically adapting to changing user profiles, network configurations, and skill levels. The content generation process is continuous and responsive, adjusting support information in real-time based on user interactions and system state changes rather than remaining static.

Inventive Principle:

Principle #15Dynamics

3Reliability

If manual content updates are performed, then content accuracy is maintained, but system complexity increases

Engineering Contradiction:

Improvecontent up-to-datenessVSAvoidcontent maintenance complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system maintains continuous useful action by continuously monitoring network information and user profiles to generate updated support content automatically. Rather than periodic manual updates, the system operates continuously to ensure content remains current with network changes and user needs without interruption.

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The system implements feedback mechanisms where user interactions with support content and network status information are continuously monitored and fed back into the AI model. This feedback loop enables the system to automatically adjust and update content based on real-time data, maintaining accuracy without manual intervention.

Inventive Principle:

Principle #23Feedback

4Ease of operation

If personalized content is generated for each user, then user engagement increases, but content generation time increases

Engineering Contradiction:

Improveuser engagementVSAvoidcontent generation time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system performs preliminary action by pre-processing and storing network information, user profiles, and support content templates before actual content generation requests. This pre-preparation enables the AI model to quickly generate personalized content without time-consuming real-time analysis, reducing generation time while maintaining personalization.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250286791A1Spanning content tree for intellectual capital creation and configuration completion function through generative artificial intelligence prompt pipeline

Publication Date: 2025.09.11 CISCO TECHNOLOGY INC

AI summary

Methods for providing spanning content tree for generating on-demand, persona-based, and journey-aware support content using machine learning. The methods involve obtaining input data related to a configuration or an operation of one or more assets in an enterprise network and based on the input data, obtaining network information about the one or more assets of the enterprise network and base support content that includes information about configuring or operating the one or more assets in the enterprise network. The methods further involve performing generative artificial intelligence learning on the base support content using the network information to generate targeted support content specific to the input data and the one or more assets of the enterprise network. The methods further involve providing the targeted support content for changing the configuration or the operation of the one or more assets in the enterprise network.