Dynamic Prompting Data Session Using Relationship Graphs
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Solution Overview
Problem
Existing systems fail to effectively present dynamic and personalized prompting data to users, particularly in adapting to individual user preferences and engagement levels during conversations, leading to suboptimal user interaction and engagement.
Innovation Solution
A method and system that establish and iteratively update a relationship graph based on user data, using natural language processing and machine learning to adapt the graph in real-time, presenting different versions of the graph to users based on their demographics, knowledge levels, and engagement patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static prompting data is presented to users, then system complexity is reduced, but user engagement and personalization are deteriorated
Solution Approach 1:
The patent implements dynamic prompting data that automatically updates based on user interactions, conversation context, and engagement levels. The system transitions from static to dynamic content delivery, where prompting data evolves in real-time to match user needs, thereby achieving personalization without requiring complete system redesign
Solution Approach 2:
The system pre-establishes relationship graphs and content structures that can be quickly adapted during user interactions. By preparing foundational data structures in advance and enabling incremental updates, the system achieves personalization while maintaining manageable complexity through structured evolution rather than complete regeneration
2Productivity
If generic prompting data is used, then ease of operation is improved, but user engagement and contribution are deteriorated
Solution Approach 1:
The patent applies local quality by customizing prompting data based on individual user characteristics, conversation context, and engagement patterns. Different users receive tailored content while the overall system maintains standardized operational frameworks, achieving personalization without sacrificing ease of operation through centralized management of adaptive algorithms
3Adaptability or versatility
If prompting data is not updated in real-time, then system stability is maintained, but adaptability to user preferences is deteriorated
Solution Approach 1:
The system implements controlled dynamics by enabling real-time updates to prompting data while maintaining stable core structures. The relationship graphs and content frameworks evolve incrementally based on user interactions, allowing adaptability without complete system reconfiguration, thus preserving stability while achieving responsiveness
Solution Approach 2:
The patent incorporates feedback mechanisms that monitor user engagement, conversation context, and preference signals to trigger selective updates of prompting data. This feedback-driven approach ensures system stability by updating only when and where changes are warranted, balancing adaptability with compositional stability
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining user data of at least one user to determine whether a criterion has been satisfied for running a prompting data session for prompting the at least one user; responsively to determining that the criterion has been satisfied for running the prompting data session for prompting the at least one user, running a prompting data session, wherein the running the prompting data session includes (a) establishing and iteratively updating a relationship graph and (b) presenting the iteratively updated relationship graph to one or more user.