Automated Expert System for Ego Network Co-prosperity Analysis
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Solution Overview
Problem
Current technologies lack comprehensive methods for analyzing and improving the dynamics and prosperity outcomes of personal and organizational ego networks, with existing tools focusing on social network analysis rather than ego network analysis, and lacking empirical validation in their advisory services.
Innovation Solution
An automated expert system employing sociometric methods for analyzing network member behaviors and providing personalized habit improvement coaching through machine learning, using a core predictive algorithm to assess and improve interdependent relationship habits for enhanced co-prosperity outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If social network analysis tools are used to map connections between individuals, then the ability to visualize network structure is improved, but the capability to analyze ego network dynamics and prosperity outcomes deteriorates
Solution Approach 1:
The patent segments network analysis into two distinct modes: social network analysis for overall structure mapping, and ego network analysis for individual-centered prosperity evaluation. This segmentation allows each analysis type to be optimized independently, resolving the contradiction between structure visualization and dynamics analysis capabilities.
Solution Approach 2:
The patent creates a universal analysis platform that can perform both social network analysis and ego network analysis through a single system. The platform adapts its functionality based on the selected analysis type, providing comprehensive versatility without compromising the precision of either analysis method.
2Ease of operation
If expert advisory services are provided to improve relationship habits, then the quality of personalized coaching is improved, but the scalability and reach of the service deteriorates
Solution Approach 1:
The patent introduces an AI-based expert system as an intermediary that bridges personalized coaching and scalability. The system uses machine learning models trained on expert knowledge to deliver personalized habit improvement recommendations at scale, maintaining coaching quality while eliminating the linear scalability limitation of human-only services.
Solution Approach 2:
The patent creates digital copies of expert advisory knowledge through AI models and algorithms. These copied expertise representations can serve multiple users simultaneously without degradation of quality, enabling the service to scale from one-on-one coaching to population-level intervention.
3Measurement precision
If comprehensive data collection on relationship habits and prosperity outcomes is implemented, then the accuracy of predictive modeling is improved, but the complexity of data processing and analysis deteriorates
Solution Approach 1:
The patent performs preliminary data processing by pre-defining standardized habit categories and prosperity metrics before data collection. This preliminary structuring of data frameworks simplifies subsequent analysis while maintaining comprehensive measurement capability, reducing processing complexity without sacrificing accuracy.
Solution Approach 2:
The patent transforms complex qualitative relationship habit data into quantifiable parameters through standardized scoring systems and categorical classifications. This parameter transformation enables accurate predictive modeling while simplifying data processing through consistent numerical representation of behavioral patterns.
Data Source
AI summary
Methods and systems for analyzing, improving, and monitoring the co-prosperity of members of a network, an ego network, a subnetwork or affiliated networks, implement the steps of: for each member, profiling the habits of the member and determining a member habit index; profiling the welfare and wellbeing of the member and determining a member success index; determining a co-prosperity index for the member reflecting the member's benefits from and contributions to the network; determining the causal relationships between the member and network relationship habit profiles and the co-prosperity index of the member; developing and delivering a habit improvement program to the member based on the predictive modelling of the impact of changes to the member habit profile and the member welfare and wellbeing outcomes. The method and system may track the effectiveness of the habit improvement program by periodically updating the habit profile of the member and the welfare and wellbeing profile of the member.