AI Carbon Removal Onboarding and Compliance System
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Solution Overview
Problem
Current carbon management systems are ineffective in reducing legacy carbon in the atmosphere, as they primarily focus on limiting current CO2 emissions without addressing the existing legacy carbon, which continues to increase due to slow degradation and uniform distribution of CO2 in the atmosphere, posing environmental and existential risks, especially for water scarcity issues.
Innovation Solution
A system and method utilizing data processing apparatuses with machine learning and artificial intelligence to provide membership services, validate carbon removal and sequestration information, and enforce compliance with mandates by users, including the use of artificial neural networks to determine net carbon effects and automatically enable or disable device features based on compliance, facilitating the reduction and storage of legacy carbon.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If carbon emission limits are imposed on participating parties, then current CO2 emissions are reduced, but legacy carbon in the atmosphere continues to increase
Solution Approach 1:
The patent introduces carbon removal and sequestration technologies as intermediary mechanisms between current emissions and legacy carbon. These technologies actively remove CO2 from the atmosphere and store it in stable forms, serving as a mediator that addresses legacy carbon accumulation while emission limits address current emissions. The system uses AI/ML to match parties generating legacy carbon with those implementing removal technologies, creating a bridging mechanism that tackles both aspects of the contradiction.
Solution Approach 2:
The patent transforms the approach from passive emission limiting to active carbon removal by changing the parameter from controlling emission rates to measuring and managing actual atmospheric carbon reduction. The AI/ML system monitors and verifies carbon removal quantities, storage stability, and net carbon effects, shifting the focus from preventing addition to achieving subtraction of legacy carbon.
2Measurement precision
If AI/ML systems are used to validate carbon removal information, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where the AI/ML system automatically validates carbon removal information, verifies data integrity, and enforces compliance without requiring manual intervention. The system autonomously monitors encrypted data payloads, validates carbon removal claims, determines compliance status, and applies sanctions or incentives automatically. This automation achieves high measurement precision while managing complexity through centralized intelligent processing rather than distributed manual verification.
3Reliability
If encrypted data payloads are used for carbon removal information, then information security improves, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent replaces manual data verification mechanisms with AI/ML-based automated validation systems. The encrypted data payloads maintain security through cryptographic methods, while the AI/ML system substitutes complex manual decryption and validation processes with intelligent algorithms that can analyze encrypted information patterns, verify authenticity, and determine compliance without compromising security. This substitution maintains reliability while managing the difficulty of detection through automated intelligent processing.
Data Source
AI summary
Membership services are provided to each of a plurality of users of a plurality of user computing devices, including via software that configures the plurality of user computing devices to operate within the membership services. An encrypted data payload including information representing at least one of carbon removal and/or carbon sequestering is received by a configured user computing device, and the information is validated. As a function of machine learning and artificial intelligence, at least one of carbon removal and/or carbon sequestering associated with validated information is determined. A respective mandate is accessed and, as a function of the validated information and the respective mandate, a determination is made whether the respective user is compliant with the respective mandate. At least one operating feature of the configured user computing device is disabled or enabled, as a function of the user's compliance.