US-20260127620-A1 - Intelligent Apparatus for Next-GEN Carbon Credits Platform Leveraging Synthetic Data Token Generation Powered by Responsible AI

US20260127620A1US 20260127620 A1US20260127620 A1US 20260127620A1US-20260127620-A1

Abstract

Intelligent methods, processes, and systems are disclosed for generating synthetic eco-crypto tokens for carbon credits and may include the steps of identifying the amount of carbon credits saved for a product or service purchased by a customer by using data centric artificial intelligence (AI) working with trained data sets. The trained data sets may be loaded to knowledge graphs and a unique green reward identification for a customer may be combined to then generate a new synthetic data which may be used to generate an eco-crypto token. Responsible AI may be used to generate the eco-crypto tokens in a safe, trustworthy, and ethical fashion for use in other transactions for products and services.

Inventors

Gowri Sundar Suriyanarayanan
Maneesh Kumar Sethia
Madala Rajasekhar

Assignees

BANK OF AMERICA CORPORATION

Dates

Publication Date: 20260507
Application Date: 20241106

Claims (20)

1 . A process for generating synthetic eco-crypto tokens for carbon credits, comprising: identifying an energy-efficient transaction or an energy-efficient service by collecting relevant data using a data-centric artificial intelligence (AI) module; analyzing an energy savings associated with each product or service and calculating corresponding carbon credits; storing information on energy savings and carbon credits in a knowledge graph, wherein the knowledge graph is trained using a hypergraph neural network to encode high-order data correlations; generating an eco-crypto token based on analyzed data provided by the data-centric AI module and the knowledge graph; onboarding a customer onto an eco-crypto rewards platform and generating a unique eco-crypto token based on customer data and a timestamp; collecting energy-saving data using a-the data-centric AI module and training the knowledge graph to reflect energy efficiency metrics; encrypting the eco-crypto token with homomorphic encryption to ensure secure transaction processing; validating the transaction or service using a responsible AI module based on data from the knowledge graph; providing, via a platform that supports eco-friendly product or service purchases, the purchased product or service to the customer; and recording the generated eco-crypto token in a rewards ledger.
2 . The process of claim 1 further comprising: collecting energy-saving data from industries and products using an AI data collector integrated into the data-centric AI module, where this data is used to continuously train the knowledge graph.
3 . The process of claim, 1 wherein the responsible AI module ensures that eco-crypto token generation is carried out in a resource-efficient and ethical manner, preventing unnecessary energy consumption.
4 . The process of claim 1 , further comprising generating dynamic smart contracts to automatically adjust the eco-crypto token in the rewards ledger according to a purchase history.
5 . The process of claim 1 , wherein the eco-crypto token generated is unique to each customer and can only be decoded by the eco-crypto rewards ledger to ensure secure and accurate carbon credit transactions.
6 . The process of claim 1 , further comprising the step of generating a dynamic smart contract via a dynamic smart contract engine, wherein the dynamic smart contract is configured to manage validation and claim processing of the eco-crypto token.
7 . The process of claim 1 , wherein homomorphic encryption of an eco-crypto ID of the customer allows secure processing of the customer data.
8 . The process of claim 1 , further comprising the step of using the responsible AI module to monitor and ensure resource-efficient eco-crypto token generation and compliance with ethical standards, thereby minimizing unnecessary energy consumption.
9 . The process of claim 1 , further comprising verifying a customer eco-token balance prior to initiating the transaction or service, and ensuring the customer has sufficient eco-crypto tokens to complete the transaction.
10 . The process of claim 1 , wherein the eco-token is configured for use with another cryptocurrency or another cryptocurrency platform.
11 . The process of claim 1 , wherein the knowledge graph is trained continuously using a Hypergraph Neural Network (HGNN) framework to adapt to new energy-saving data, enabling dynamic and accurate allocation of carbon credits.
12 . The process of claim 1 , wherein the generated eco-crypto token reflects a customer contribution to reducing carbon emissions.
13 . The process of claim 1 , wherein the transaction is a purchase of an eco-friendly product.
14 . The process of claim 1 , wherein the service is designing, developing, testing, updating, or maintaining software.
15 . A process of purchasing eco-friendly products using an eco-friendly cryptocurrency-based system comprising: onboarding a customer on a platform that supports eco-friendly product purchases; allowing the customer to purchase eco-friendly products using a cryptocurrency token specific to the platform; completing a cryptocurrency transaction for the purchased eco-friendly product; providing, via the platform that supports eco-friendly product purchases, the purchased product to the customer; associating the purchase with an amount of carbon savings linked to the eco-friendly product purchased; adding a corresponding reward value to a reward ledger based on a cryptocurrency value used for purchasing carbon-saving products; and updating the reward ledger.
16 . The process of claim 15 , further comprising: receiving identity data for a customer; verifying the customer identity through a Know Your Customer (KYC) verification; wherein the KYC process checks the customer data for compliance with identity verification regulations; onboarding the customer after successful KYC verification, or returning the customer to a verification process if the KYC verification is unsuccessful; generating an eco-crypto token for the customer via an eco-crypto token generator upon successful onboarding of the customer; recording the generated eco-crypto token in a carbon crypto rewards ledger, wherein the carbon crypto rewards ledger maintains a record of customer eco-crypto tokens and associated rewards; completing the customer onboarding process, wherein the customer is successfully integrated into the platform after the eco-crypto token generation and logging processes.
17 . The process of claim 15 , wherein the eco-friendly products available for purchase are filtered based on a predefined sustainability criteria, such as carbon footprint reduction, biodegradability, or renewable resource usage.
18 . The process of claim 17 , wherein a calculated carbon savings score for each eco-friendly product based on its lifecycle carbon impact is displayed to the customer.
19 . The process of claim 18 , further comprising generating a dynamic smart contract comprising instructions for validation and claiming of the customer associated rewards.
20 . A non-transitory machine-readable medium storing instructions for generating synthetic eco-crypto tokens for carbon credits that, when executed by one or more processors, cause the one or more processors to perform steps comprising: identifying an energy-efficient transaction or an energy-efficient service by collecting relevant data using a data-centric artificial intelligence (AI) module; analyzing an energy savings associated with each product or service and calculating corresponding carbon credits; storing information on energy savings and carbon credits in a knowledge graph, wherein the knowledge graph is trained using a hypergraph neural network to encode high-order data correlations; generating an eco-crypto token based on analyzed data provided by the data-centric AI module and knowledge graph; onboarding a customer onto an eco-crypto rewards platform and generating a unique eco-crypto token based on customer-specific data and a timestamp; collecting energy-saving data using the data-centric AI module and training the knowledge graph to reflect energy efficiency metrics; encrypting the eco-crypto token with homomorphic encryption to ensure secure transaction processing; validating the transaction using a responsible AI module based on data from the knowledge graph; providing, via a platform that supports eco-friendly product or service purchases, the product or service to the customer; and recording the generated eco-crypto token in a rewards ledger.

Description

TECHNICAL FIELD The present disclosure relates to the fields of environmental sustainability, artificial intelligence, synthetic data generation, and blockchain-based carbon credit reward systems. Specifically, it relates to an intelligent platform and process that utilizes responsible artificial intelligence (AI) for generating synthetic data tokens tied to carbon credits, facilitating secure, resource-efficient transactions in carbon credit marketplaces as disclosed herein. BACKGROUND At present, there is no carbon credits payment gateway that supports seamless transactions or allows the purchase of environment sustainable products, which are rewarded in return for eco-friendly choices. The generation of unique tokens for such systems heavily relies on traditional crypto-mining or minting processes, which are resource-intensive and environmentally damaging. Moreover, earned and existing carbon credits cannot be utilized across different platforms due to interoperability issues. This limits the growth of carbon credits systems and discourages adoption. Additionally, the need for a secure, responsible method of generating, managing, and validating carbon credit tokens across platforms remains unmet. SUMMARY In light of the foregoing background, the following presents a simplified summary of the present disclosure in order to provide a basic understanding of various aspects of the disclosure. This summary is not limiting with respect to the exemplary aspects of the inventions described herein and is not an extensive overview of the disclosure. It is not intended to identify key or critical elements of the disclosure or to delineate the scope of the disclosure. Instead, as would be understood by a personal of ordinary skill in the art, the following summary merely presents some concepts of the disclosure in a simplified form as a prelude to the more detailed description provided below. In one aspect of the disclosure, a process for generating synthetic eco-crypto tokens for carbon credits is disclosed in accordance with one or more aspects described herein and may include the steps of identifying an energy-efficient transaction or an energy-efficient service by collecting relevant data using a data-centric artificial intelligence (AI) module, analyzing an energy savings associated with each product or service and calculating corresponding carbon credits, storing information on energy savings and carbon credits in a knowledge graph in which the knowledge graph may be trained using a hypergraph neural network to encode high-order data correlations, generating an eco-crypto token based on analyzed data provided by the data-centric AI module and knowledge graph, onboarding a customer onto an eco-crypto rewards platform and generating a unique eco-crypto token based on customer data and a timestamp, collecting energy-saving data using a data-centric AI module and training a knowledge graph to reflect energy efficiency metrics, encrypting the eco-crypto token with homomorphic encryption to ensure secure transaction processing, validating the transaction or service using a responsible AI module based on data from the knowledge graph, and recording the generated eco-crypto token in a rewards ledger. In some examples, energy-saving data from industries and products may be collected using an AI data collector integrated into the data-centric AI module, and this data may be used to continuously train the knowledge graph. In other examples, the responsible AI module may ensure that eco-token generation may be carried out in a resource-efficient and ethical manner, preventing unnecessary energy consumption. In some aspects, the process may further include generating dynamic smart contracts to automatically adjust the eco-crypto token in the rewards ledger according to a customer purchase history. In one example, the generated eco-crypto token may be unique to each customer and may only be decoded by the eco-crypto rewards ledger to ensure secure and accurate carbon credit transactions. In yet another example, a dynamic smart contract engine may be used to generate a dynamic smart contract configured to manage validation and claim processing of the eco-crypto token. In still another example, homomorphic encryption of an eco-crypto ID for the customer may be used to secure processing of the customer data. In some examples, the responsible AI module may be configured to monitor and ensure resource-efficient eco-token generation and compliance with ethical standards, thereby minimizing unnecessary energy consumption. In some examples, a customer cryptocurrency balance may be verified prior to initiating the transaction or service to ensure that the customer has sufficient eco-tokens to complete the transaction or service. In yet another example, the service may be designing, developing, testing, updating, or maintaining software. In one example, the knowledge graph may be trained continuously using a Hypergraph Neural Network