Impact-Scored Recurring Payments for Social-Commercial Network Growth
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Solution Overview
Problem
Existing social and commercial networks lack advanced payment and financial architectures that incentivize and reward long-term engagement and activity, relying on one-time payments that result in short-term effects, failing to leverage network effects and viral effects for sustained user retention and network growth.
Innovation Solution
A multi-layered social-commercial network utilizing network effects and viral effects, with a financial payment architecture that rewards user-members based on their ongoing engagement and the activity of their sub-networks, calculated through a data analytics engine and impact scores, fostering generative connectivity and long-term engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If one-time payments are provided to user-members, then initial joining and referral activity is encouraged, but long-term engagement and sustained network growth are not incentivized
Solution Approach 1:
The patent implements periodic recurring payments distributed at regular intervals (e.g., monthly) based on accumulated network effects and user engagement metrics. This periodic distribution mechanism transforms one-time incentives into sustained long-term engagement drivers, as user-members continuously work to maintain and grow their impact scores across payment cycles.
Solution Approach 2:
The system calculates and displays impact scores that provide continuous feedback to user-members about their network effects, referral activity, and engagement levels. This feedback loop enables user-members to understand how their actions translate into payment entitlements, motivating sustained effort to improve their scores and maintain recurring payment eligibility.
2Duration of action of stationary object
If advanced payment architectures are implemented to reward long-term engagement, then sustained user activity is incentivized, but system complexity increases
Solution Approach 1:
The system automatically calculates impact scores, determines payment entitlements, and distributes recurring payments without requiring manual intervention. The automated calculation engine continuously monitors network effects, referral activity, and engagement metrics, then autonomously computes and distributes payments based on pre-established rules, reducing operational complexity despite the sophisticated payment architecture.
Solution Approach 2:
The patent segments the payment system into distinct functional modules: impact score calculation, network effect tracking, referral activity monitoring, and payment distribution. This modular segmentation allows each component to be independently developed, maintained, and optimized, managing overall system complexity while enabling sophisticated long-term engagement incentives.
3Quantity of substance
If network effects and viral effects are leveraged for growth, then network value and membership expansion are enhanced, but difficulty in measuring and managing user contributions increases
Solution Approach 1:
The patent replaces manual measurement and assessment of user contributions with an automated computational system that calculates impact scores based on objective metrics. The system automatically tracks network effects (value provided to other users), viral effects (referral-driven growth), and engagement levels, transforming the complex task of measuring user impact into an automated data processing function that scales with network size.
Solution Approach 2:
The system defines and tracks multiple quantifiable parameters including network effects metrics, viral referral counts, user engagement frequency, and impact score values. By transforming qualitative user contributions into measurable numerical parameters, the system enables precise tracking and comparison of user impacts across the entire network, facilitating fair and transparent payment distributions.
Data Source
AI summary
A computer-implemented method for determining a periodic payment to users of a social network including. The method includes associating users of a cloud-based social network into a Merkle tree structure based on an order of membership referrals as to which user referred which to the cloud-based social network. The method includes quantifying an amount of digital engagement that a first user has with the cloud-based social network through an individual impact score. The method includes attributing all users that were referred to the cloud-based social network by the first user into the first user's sub-network. The method includes quantifying an amount of digital engagement for all users within the first user's sub-network has with the cloud-based social network through a sub-network impact score. The method includes making a recurring electronic payment to the first user on a periodic basis utilizing the individual impact score and the sub-network impact score.