Cryptographic Charity Impact Rating With Automated Fund Release

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Solution Overview

Problem

Existing charity donation systems face inefficiencies in processing matching gifts, lack real-time impact tracking, and rely on static, biased human evaluations, leading to mismanagement and fraud risks.

Innovation Solution

A cryptographically attested, continuously self-learning system using AI for objective charity ratings, real-time impact tracking, and secure fund disbursement, incorporating a hardware-accelerated cryptographic attestation layer, causal-inference modules, and a permissioned distributed ledger for automated fund releases.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual review processes are used for charity evaluations and matching gift approvals, then donors can assess risk and make informed decisions, but the process is time-consuming and inefficient

Engineering Contradiction:

Improvedonor confidence in charity evaluationVSAvoidtime for manual review and processing

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical review processes with an automated AI-based evaluation system. The system uses machine learning algorithms to automatically assess charities, analyze risk factors, and generate ratings, substituting human reviewers with computational processes that operate continuously without time constraints while maintaining consistent evaluation criteria.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables charities to self-evaluate and update their own information through the platform. Charities can input their data, receive automated feedback, and maintain their profiles, reducing the need for manual intervention while ensuring data accuracy and up-to-date information for donor decision-making.

Inventive Principle:

Principle #25Self-service

2Productivity

If static, one-time charity ratings are used, then evaluation costs are reduced, but the ratings become outdated and unreliable over time

Engineering Contradiction:

Improveevaluation efficiencyVSAvoidcurrent accuracy of charity ratings

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent implements continuous monitoring and re-evaluation of charities through automated systems. Rather than static periodic reviews, the system continuously tracks charity performance, updates ratings in real-time, and notifies donors of changes, ensuring ratings remain current and reliable without requiring repeated manual evaluation campaigns.

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The system incorporates feedback loops where charity outcomes, donor experiences, and performance metrics are continuously fed back into the evaluation algorithm. This allows the AI system to learn from new data, adjust ratings dynamically, and maintain accuracy over time while operating efficiently at scale.

Inventive Principle:

Principle #23Feedback

3Ease of operation

If charities provide banking information to multiple donors and third parties, then fund disbursement is facilitated, but the risk of banking fraud and hacked accounts increases

Engineering Contradiction:

Improvefund disbursement processVSAvoidbanking fraud risk

Core Design Contradiction:

Ease of operationVSObject-affected harmful factors

Solution Approach 1:

The patent introduces a trusted intermediary platform that acts as a secure middle layer between donors and charities. The platform holds and verifies charity banking information centrally, using cryptographic attestation to ensure authenticity. Funds are disbursed through this secure intermediary channel, eliminating the need for charities to share banking details with multiple parties while maintaining efficient fund transfer capabilities.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Reliability

If manual tracking of matching gift funds is performed by charity staff, then fund allocation can be monitored, but limited staff resources lead to errors and lost funds

Engineering Contradiction:

Improvefund tracking accuracyVSAvoidstaff workload and resources

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces manual staff tracking processes with automated digital systems. The platform automatically tracks matching gift allocations, monitors fund flows, and maintains audit trails through cryptographic logging. This substitution eliminates human error in tracking while reducing staff workload, as the system handles monitoring autonomously with minimal human intervention required.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12561720B1Cryptographically attested, continuously self learning impact rating system for charitable organizations

Publication Date: 2026.02.24 HASSEN DAVID

AI summary

A cryptographically attested, continuously self-learning system rates the present and predicted impact of nonprofit organizations. A ZKP verifier authenticates private operational metrics. A causal-graph module and neuro-symbolic engine compute present-day causal-impact and strategic-integrity scores. A Bayesian forecasting engine and Monte-Carlo simulator generate an Impact-Predictability Index. Ratings are hashed and written to a permissioned ledger, and smart contracts release donor funds only when objective thresholds are met. Continuous-learning, federated-privacy, and financial-resilience subsystems ensure robust, tamper-evident evaluation and funding.