AI Micro-Community System for Secure User Connections
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Solution Overview
Problem
Large organizations face challenges in securely and reliably connecting users with similar interests or needs across internal and external computer systems to provide effective product and service offerings, as current mechanisms lack verification of user input reliability.
Innovation Solution
An AI/ML model is trained to identify patterns in user activities, forming micro-communities based on common interests and needs, and managing these communities by connecting users, providing targeted recommendations, and implementing fraud protections within a secure network framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current mechanisms are used to connect users across internal and external computer systems, then user connections can be established, but the reliability of user input cannot be verified
Solution Approach 1:
The patent introduces a micro-community system as an intermediary layer between users and the organization. This micro-community assigns credibility scores to users based on their contributions and interactions within the community. The credibility score acts as a mediator that verifies user input reliability without requiring complex direct verification mechanisms between all user pairs.
Solution Approach 2:
The system implements feedback loops where user contributions are evaluated by the micro-community, generating credibility scores that are fed back to users. This feedback mechanism continuously adjusts and verifies user reliability based on their ongoing participation and contribution quality within the micro-community structure.
2Reliability
If a secure network is created to verify user reliability, then user input reliability improves, but the system complexity increases
Solution Approach 1:
The patent segments the large organization into multiple micro-communities, each handling specific user groups and interests. This segmentation reduces the complexity of the overall secure network by breaking it into smaller, more manageable units. Each micro-community operates semi-independently, verifying users within its own context rather than requiring a single complex verification system for all users.
Solution Approach 2:
The micro-community system is dynamic, allowing users to move between different micro-communities based on their interests and contributions. The credibility scores and community memberships are not static but evolve over time, enabling the system to adapt to changing user behaviors and needs without requiring complete restructuring of the secure network.
3Productivity
If micro-communities are formed based on user interests and activities, then user engagement improves, but the time and resources required for community formation and management increase
Solution Approach 1:
The micro-community system operates largely autonomously, with users self-organizing into communities based on their interests and activities. The system automatically tracks user contributions, calculates credibility scores, and manages community memberships without requiring extensive manual intervention. This self-service approach reduces the time and resources needed for community formation and ongoing management.
Solution Approach 2:
The system pre-establishes the framework and rules for micro-communities in advance, including credibility scoring criteria and community formation protocols. By preparing these structures beforehand, the system enables rapid community formation when users need to connect, reducing the time required for ad-hoc community creation and management.
Data Source
AI summary
A computing platform to form creative micro-communities of users aggregates electronic data records about electronic activities performed via one or more application computing systems providing products or services to users. An artificial intelligence/machine learning (AI/ML) model is continually trained to identify users with similar interests and group users into micro-communities based on the identified common interests. The computing platform generates micro-communities based on AI determined features or identifiers of a user, or a user may self-identify in a particular category to join the micro-community. Each micro-community may have tiers of participants that may be identified via the AI/ML models. Each micro-community may have customized rules, parameters, or the like, such as protections from certain types of communications.