# US 12,761,828 B2 · Distributed network providing certificate rights for intelligent modeling outputs > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,761,828 B2 - Document ID: US12761828B2 - Status: Granted - Kind: B2 - Application number: 17/825,141 - Filed: 2022-05-26 - Published: 2023-11-30 - Granted: 2026-10-13 - Earliest claimed: 2022-05-26 - Assignees: Bank of America Corporation - Applicants: Bank of America Corporation - Inventors: Shailendra Singh - Web page: https://patentgrep.com/patent/US12761828B2 ## CPC - G06F 3/0655: {Vertical data movement, i.e. input-output transfer; data movement between one or more hosts and one or more storage devices} - G06F 3/0604: {Improving or facilitating administration, e.g. storage management} - G06F 3/067: {Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract The present invention is generally related to systems and methods for providing an improved authentication and verification system for artificial intelligence (AI) and machine learning (ML) model output. The invention immutably stores AI and ML decisioning data, resource data, and metadata in a non-fungible token format such that this data can be traced, relocated, and validated at a later time. Decisioning data of AI and ML model output is incorruptible and more reliable as a result. ## Claims ### Claim 1 A system for secure storage of artificial intelligence and machine learning data, the system comprising: at least one non-transitory storage device; and at least one processing device coupled to the at least one non-transitory storage device, wherein the at least one processing device is configured to: initiate an application portal on a user device; transmit instructions to the application to collect data and metadata related to an artificial intelligence or machine learning (AI/ML) output generated by an AI/ML program; provide a metadata mapping engine, wherein the metadata mapping engine extracts and categorizes the data and the metadata into a plurality of categories; generate, for each categorization corresponding to the AI/ML output, a non-fungible token (NFT) comprising the data or the metadata, wherein each NFT corresponds to a specific category; assign each NFT to a respective container; assign each of the respective containers to a container package, wherein the container package comprises each respective container related to the AI/ML output; generate an index file for the container package; generate a non-fungible token (NFT) for the container package, wherein the NFT is linked to the container package; store the index file and store address information for the container package in a decision repository; validate the data and the metadata stored in the container package by comparing the stored data and the stored metadata for the AI/ML output with original data associated with the AI/ML program upon each access request; implement a smart contract engine within the application for data validation and permission management for accessing stored AI/ML data; and enable audit logging and tracking of data access and modification events, storing the data access and modification events in the decision repository along with the index file and NFT details. ### Claim 2 (depends on 1) The system of claim 1, wherein the application portal further comprises a smart contract engine. ### Claim 3 (depends on 1) The system of claim 1, wherein collecting the data and the metadata related to the AI/ML output further comprises using a deep learning engine to extract data from one or more servers. ### Claim 4 (depends on 1) The system of claim 1, wherein the data and the metadata related to the AI/ML output further comprise infrastructure resource data including one or more of an IP address, device information, server information, host environment details, or geolocation data. ### Claim 5 (depends on 1) The system of claim 1, wherein the data and the metadata related to the AI/ML output further comprise algorithm features including one or more of a model version, data semantics, algorithm type, data capture files, data formats, or data logs. ### Claim 6 A computer program product for secure storage of artificial intelligence and machine learning data, the computer program product comprising at least one non-transitory computer-readable medium having computer-readable program code portions embodied therein, the computer-readable program code portions comprising: an executable portion configured to initiate an application portal on a user device; an executable portion configured to transmit instructions to the application to collect data and metadata related to an artificial intelligence or machine learning (AI/ML) output generated by an AI/ML program; an executable portion configured to provide a metadata mapping engine, wherein the metadata mapping engine extracts and categorizes the data and the metadata into a plurality of categories; an executable portion configured to generate, for each categorization corresponding to the AI/ML output, a non-fungible token (NFT) comprising the data or the metadata, wherein each NFT corresponds to a specific category; an executable portion configured to assign each NFT to a respective container; an executable portion configured to assign each of the respective containers to container package, wherein the container package comprises each respective container related to the AI/ML output; an executable portion configured to generate an index file for the container package; an executable portion configured to generate a non-fungible token (NFT) for the container package, wherein the NFT is linked to the container package; an executable portion configured to store the index file and store address information for the container package in a decision repository; an executable portion configured to validate of the data and the metadata stored in the container package by comparing the stored data and the stored metadata for the AI/ML output with original data associated with the AI/ML program upon each access request; an executable portion configured to implement a smart contract engine within the application for data validation and permission management for accessing stored AI/ML data; and an executable portion configured to enable audit logging and tracking of data access and modification events, storing the data access and modification events in the decision repository along with the index file and NFT details. ### Claim 7 (depends on 6) The computer program product of claim 6, wherein the application portal further comprises a smart contract engine. ### Claim 8 (depends on 6) The computer program product of claim 6, wherein collecting the data and the metadata related to the AI/ML output further comprises using a deep learning engine to extract data from one or more servers. ### Claim 9 (depends on 6) The computer program product of claim 6, wherein the data and the metadata related to the AI/ML output further comprise infrastructure resource data including one or more of an IP address, device information, server information, host environment details, or geolocation data. ### Claim 10 (depends on 6) The computer program product of claim 6, wherein the data and the metadata related to the AI/ML output further comprise algorithm features including one or more of a model version, data semantics, algorithm type, data capture files, data formats, or data logs. ### Claim 11 A computer-implemented method for secure storage of artificial intelligence and machine learning data, the method comprising: providing a computing system comprising a computer processing device and a non-transitory computer readable medium, wherein the computer readable medium comprises configured computer program instruction code, such that when said instruction code is operated by said computer processing device, said computer processing device performs the following operations: initiate an application portal on a user device; transmit instructions to the application to collect data and metadata related to an artificial intelligence or machine learning (AI/ML) output generated by an AI/ML program; provide a metadata mapping engine, wherein the metadata mapping engine extracts and categorizes the data and the metadata into a plurality of categories; generate, for each categorization corresponding to the AI/ML output, a non-fungible token (NFT) comprising the data or the metadata, wherein each NFT corresponds to a specific category; assign each NFT to a respective container; assign each of the respective containers to a container package, wherein the container package comprises each respective container related to the AI/ML output; generate an index file for the container package; generate a non-fungible token (NFT) for the container package, wherein the NFT is linked to the container package; store the index file and store address information for the container package in a decision repository; validate the data and the metadata stored in the container package by comparing the stored data and the stored metadata for the AI/ML output with original data associated with the AI/ML program upon each access request; implement a smart contract engine within the application for data validation and permission management for accessing stored AI/ML data; and enable audit logging and tracking of data access and modification events, storing the data access and modification events in the decision repository along with the index file and NFT details. ### Claim 12 (depends on 11) The computer-implemented method of claim 11, wherein the application portal further comprises a smart contract engine. ### Claim 13 (depends on 11) The computer-implemented method of claim 11, wherein collecting the data and the metadata related to the AI/ML output further comprises using a deep learning engine to extract data from one or more servers. ### Claim 14 (depends on 11) The computer-implemented method of claim 11, wherein the data and the metadata related to the AI/ML output further comprise infrastructure resource data including one or more of an IP address, device information, server information, host environment details, or geolocation data. ### Claim 15 (depends on 11) The computer-implemented method of claim 11, wherein the data and the metadata related to the AI/ML output further comprise algorithm features including one or more of a model version, data semantics, algorithm type, data capture files, data formats, or data logs. ### Claim 16 (depends on 1) The system of claim 1, further comprising a feature extraction engine configured to determine, in real-time and based on the collected data and the collected metadata, features of the AI/ML output. ### Claim 17 (depends on 16) The system of claim 16, wherein the metadata mapping engine and the feature extraction engine are configured to jointly track all actions or decisions associated with the AI/ML program, and provide, in real-time, feedback to the application. ### Claim 18 (depends on 6) The computer program product of claim 6, further comprising a feature extraction engine configured to determine, in real-time and based on the collected data and the collected metadata, features of the AI/ML output. ### Claim 19 (depends on 18) The computer program product of claim 18, wherein the metadata mapping engine and the feature extraction engine are configured to jointly track all actions or decisions associated with the AI/ML program, and provide, in real-time, feedback to the application. ### Claim 20 (depends on 11) The computer-implemented method of claim 11, further comprising a feature extraction engine configured to determine, in real-time and based on the collected data and the collected metadata, features of the AI/ML output, wherein the metadata mapping engine and the feature extraction engine are configured to jointly track all actions or decisions associated with AI/ML program, and provide, in real-time, feedback to the application.