Network Graph for Appreciation Messaging
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Solution Overview
Problem
Current social media platforms like LinkedIn and Facebook only provide a scalar metric of the number of connections, failing to assess the quality or strength of relationships, which is influenced by communication frequency, content, and appreciation between users.
Innovation Solution
A host platform that builds an interconnected graph of user relationships based on message content and gifts exchanged, using a scoring mechanism to quantify appreciation capabilities, recommending actions to enhance relationship quality through personalized messaging and gift suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the platform only provides a scalar metric of connection count, then the system complexity is low and easy to implement, but the measurement precision of relationship quality is insufficient
Solution Approach 1:
The patent segments the single scalar connection count metric into multiple dimensional attributes including communication frequency, communication content analysis, and appreciation behavior. Each dimension is measured and weighted separately to create a comprehensive relationship quality score, transforming one coarse metric into multiple precise measurements.
Solution Approach 2:
The patent transitions from a one-dimensional connection count metric to a multi-dimensional relationship quality assessment space. By adding dimensions such as communication frequency, message content semantics, and appreciation actions, the system creates a higher-dimensional measurement space that captures relationship quality more accurately.
2Measurement precision
If the platform analyzes message content and communication patterns to assess relationship quality, then the measurement precision improves, but the loss of user privacy and data security increases
Solution Approach 1:
The patent introduces an intermediary processing layer that analyzes message metadata and content patterns without exposing raw user data. The system uses natural language processing to extract relationship indicators from communications while maintaining data anonymization and access control, allowing quality assessment without direct exposure of private user information.
Solution Approach 2:
The patent implements feedback mechanisms where users can control the level of data sharing and review how their communication patterns are being analyzed. The system provides transparency about what data is collected and allows users to adjust privacy settings, creating a feedback loop that balances measurement precision with user privacy concerns.
3Loss of information
If the platform builds a comprehensive network graph with multiple relationship attributes, then the information completeness about user relationships improves, but the device complexity and computational resources required increase
Solution Approach 1:
The patent applies local quality by computing relationship attributes selectively based on user needs and interaction contexts. Rather than calculating all possible relationship metrics for all user pairs continuously, the system computes specific attributes (communication frequency, appreciation score, etc.) only when relevant to particular user actions or queries, reducing overall computational complexity while maintaining information completeness where needed.
Solution Approach 2:
The patent performs preliminary actions by pre-computing and caching relationship attributes during user interactions. Communication patterns and appreciation behaviors are analyzed and stored as pre-processed relationship metrics, allowing the network graph to be built efficiently without real-time computation of all relationship qualities during user queries.
Data Source
AI summary
The example embodiments are directed to a system and method that can determine an appreciation capability of an individual based on their interactions via a messaging platform with other users. The appreciation capability can also be based off of organizations that the individual is involved with. Furthermore, the appreciation capability can be used to make recommendations to the individual as well as recommendations to other users about improving their appreciation capabilities.