Abstract: A network system to allow global usage of data while allowing regional jurisdictions control over sensitive data. Different jurisdictions may declare different types of data as sensitive data that is not to be discoverable by a third party. The system allows the data to be shared across jurisdiction boundaries with complete auditability, traceability, and compliance. The system allows a first jurisdiction computing device to control the usage of the data that is stored outside of the jurisdiction. The technology allows the first jurisdiction to propagate rules, tokenization protocols, and updates to the system to manage the sensitive data. The system detokenizes the data when the data is to be used for an approved purpose by an approved party. If the original jurisdiction has a change in permissions for sensitive data, the jurisdiction can propagate a tokenization to all data stored in the data management system outside of the jurisdiction.

Abstract: Systems and methods for providing on-demand access to resources across global or cloud computer networks are described herein. In particular, the systems and methods can use transformer models to estimate proxy resource capacities. These proxy resource capacities can be leveraged to satisfy certain conditions for executing blockchain actions, enabling resources to be used even when a current capacity of those resources does not satisfy the conditions.

Abstract: Systems and methods for securely accessing resources across global or cloud computer networks. In particular, systems and methods for using token-specific self-executing programs to securely access resources. For example, the use of token-specific self-executing programs, such as smart contracts designed to exchange a single NPT representing an off-chain product, enhances security in accessing resources by automating trustless transactions and enforcing predefined rules without intermediaries.

Abstract: Systems and methods are for managing resources across a global and/or cloud network. In particular, systems and methods are described for mitigating issues related to providing services using unstable resources (e.g., resources that may be off-line or potentially go off-line). For example, the systems and methods may mitigate issues related to providing services despite instability in resources by monitoring tokenized availability of a given resource using a decentralized blockchain network and performing actions based on that availability based on conditions specified within the code of action-specific self-executing programs themselves (as opposed to a centralized coordination device).

Abstract: Systems and methods are described herein for providing seamless service route updates for service routing platforms. When a request for a new service route is received, the system may use the data within the request to transform the service route into a format supported by one or more platforms that will have the route setup. Once the transformation has been completed, the system may store the transformed service route or service routes in a route repository. In addition, the system may store the service route in a standardized format separately. The system may then determine that a new service route has been added and match the new service route with one or more service routing platforms. Once the match has been made, for each service route, the system may transmit the transformed service routes to the corresponding service routing platforms.

Abstract: Presented herein are system and methods for controlling access to services for processing requests. A server maintains rule sets defined for risk levels to control access to second services. Each of the risk levels defines a respective group of rule sets from the rule sets to apply. The server receives a request including authentication information of a transaction type for an end user device to access a second service. The server determines risk parameters and a challenge threshold. The server identifies a risk level for the request based on the risk parameters. The server selects a group of rule sets to apply for the identified risk level and applies the group of rule sets to the authentication information to perform at least one of a denial, allowance, or challenge of the request of the transaction type using the challenge threshold, for the end user device to access the second service.

Abstract: Methods and systems herein identify and redact personally identifiable information. A PII sensitivity detection framework includes multiple layers where each layer corresponds to a computer model. The framework analyzes data stored within different data tables and predicts whether a data column includes PII. The first layer corresponds to an artificial intelligence model that analyzes each column metadata and predicts a first score indicative of a likelihood of PII. The second layer corresponds to a rule-based computer model that uses various rules to determine a second score indicative of a likelihood of PII for each column. The third layer corresponds to a column content model that analyzes content of each column using various natural language processing techniques to generate a third score indicative of a likelihood of PII. The framework masks data being presented to a user based on the scores generated via execution of one or more of the layers.

Abstract: Systems and methods for novel uses and/or improvements to blockchains and blockchain technology. As one example, systems and methods are described herein for self-validating digital records that may be transferred in real-time through a cross-network registry. For example, in a conventional system, minting a token (e.g., a digital record) involves writing a self-executing program that defines the transfer rules of the digital record. Once the self-executing program is written, it is deployed on a blockchain, and the digital record is minted by publishing it to a blockchain.

Abstract: The technology evaluates the compliance of an AI application with predefined vector constraints. The technology employs multiple specialized models trained to identify specific types of non-compliance with the vector constraints within AI-generated responses. One or more models evaluate the existence of certain patterns within responses generated by an AI model by analyzing the representation of the attributes within the responses. Additionally, one or more models can identify vector representations of alphanumeric characters in the AI model's response by assessing the alphanumeric character's proximate locations, frequency, and/or associations with other alphanumeric characters. Moreover, one or more models can determine indicators of vector alignment between the vector representations of the AI model's response and the vector representations of the predetermined characters by measuring differences in the direction or magnitude of the vector representations.

Abstract: Systems and methods for providing network validations for cloud-based network architectures are described herein. For example, the system may receive a network requirement for a first cloud-based network architecture. The system may receive a first network action that corresponds to the network requirement when facilitated by the first cloud-based network architecture. The system may process the first network action through the first cloud-based network architecture. The system may receive a first indicium of security components used to process the first network action through the first cloud-based network architecture. The system may compare the first indicium to a known indicium for processing the first network action through an approved cloud-based network architecture. The system may generate a first network validation based on comparing the first indicium to the known indicium.

Abstract: The technology evaluates the compliance of an AI application with predefined vector constraints. The technology employs multiple specialized models trained to identify specific types of non-compliance with the vector constraints within AI-generated responses. One or more models evaluate the existence of certain patterns within responses generated by an AI model by analyzing the representation of the attributes within the responses. Additionally, one or more models can identify vector representations of alphanumeric characters in the AI model's response by assessing the alphanumeric character's proximate locations, frequency, and/or associations with other alphanumeric characters. Moreover, one or more models can determine indicators of vector alignment between the vector representations of the AI model's response and the vector representations of the predetermined characters by measuring differences in the direction or magnitude of the vector representations.

Abstract: A system facilitates a process for automatically deploying artificial intelligence (AI) models. The system receives, for a first artificial intelligence (AI) model used by an entity, a first request to deploy the first AI model to make the first AI model available for use in a production environment to process input data and generate corresponding outputs. A first model deployment location for the first model is selected based on a model deployment engine. The system generates scripts to deploy the first AI model to the selected location, then monitors operations parameters associated with the deployment of the first AI model. Based on the values of the operations parameters, the system updates the model deployment engine. In response to a second request to deploy a second AI model, the system uses the updated model deployment engine to select a second model deployment location for the second model.

Abstract: Systems and methods for reducing usage of computational resources when accessing siloed data across disparate locations via a unified metadata graph are disclosed. The system receives a user-specified query indicating a request to access a set of data objects. The system then performs natural language processing on the user-specified query to determine a set of phrases corresponding to the user-specified query. The system then accesses a metadata graph to determine a node corresponding to the set of phrases. Using a location identifier corresponding to the determined node, the system determines a data silo storing at least one data object of the set of data objects. The system then generates for display, on a graphical user interface, a visual representation of the at least one data object.

Abstract: Systems and methods for novel uses and/or improvements to data migration. In particular, systems and methods for data migration of metadata stored in object storage from one container to another, especially in instances when the metadata is destined for ingestion by an artificial intelligence application. The systems and methods ensure that all metadata (e.g., metadata stored in object storage) is preserved during data migration, including metadata such as content type, object lock mode, object lock retain until date, and/or custom metadata.

Abstract: A distributed private ledger function of a server of a first consortium member receives data representing an alias for one of its customers from the customer and also receives data that represents an alias for a customer of a second member replicated by a distributed private ledger function of a server of the second member to all members of the consortium. Thereafter, the distributed private ledger function of the first member's server identifies a recipient account of the second member's customer based on an account pointer associated with the alias of the second member's customer and initiates a transfer of funds from a source account of the first member's customer corresponding to an account pointer associated with the alias for the first member's customer to the identified recipient account of the second member's customer.

Abstract: A computer can connect with a middleware computing system. The middleware computing system may use application programming interfaces (APIs), webhooks, file-based integration, database replication, message queues, websockets, or direct integration to establish connections with different computing devices. The computer may request a data structure classification for an external data structure stored in a remote computing device. The middleware computing system can receive the request, identify the connection that the middleware computing device has with the remote computing device, and retrieve records for transactions performed by the external data structure from the remote computing device. The middleware computing system can use metadata in the records to automatically determine a data structure type of the data structure. The middleware computing system can generate instructions that cause the computer to link the external data structure with the profile.

Abstract: Systems and methods for generating code for artificial intelligence models without requiring training data that is specific to model components and objectives. For example, the system may receive an original version of a rule engine. The system may input the original version, using a first input condition, into a regeneration model to generate a first regenerated version of the rule engine. The system may determine whether the first regenerated version includes a first hallucination based on comparing the first regenerated version to alternative versions of the rule engine, wherein each of the alternative versions were generated using a respective alternative input condition. The system may, in response to determining that the first regenerated version includes the first hallucination, determining whether the first hallucination comprises a positive mutation.

Abstract: A computer-implemented method for seamlessly processing transactions using distributed ledger technology. The method may comprise: linking one or more conventional accounts hosted in a conventional banking infrastructure to one or more DLT-based client accounts hosted on a distributed ledger, wherein the DLT application comprises a routing address configured to be used in conventional transaction infrastructure using conventional communication protocols; storing one or more wallet identifications for the one or more DLT-based client accounts and a mapping of the one or more wallet identifications to the one or more conventional accounts hosted in the conventional banking infrastructure; exchanging a sequence of messages to execute an asset transfer and complete a transaction lifecycle, the sequence of messages based on the first asset type; updating the distributed ledger based on the asset transfer; and sending appropriate messages to clients.

Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining video data comprising a plurality of frames including a document of a document type. The method may include executing an object recognition engine of a machine-learning architecture using image data of the plurality of frames, the object recognition engine trained to detect edges of documents. The method may include identifying, based on the edge detection, a plurality of boundaries for the document. The method may include validating, based on the plurality of boundaries, the document as the document type. The method may include transmitting via one or more networks, to a computer remote from the computing device, responsive to the validation of the type of document, the image data for the plurality of frames depicting the document.

Abstract: Systems and methods are for mitigating hindsight bias related to designing and using artificial intelligence models for outlier events. More specifically, systems and methods for the use of synthetic data in the training and/or validation of model predictions in order to prevent overfitting and generate predictions that attempt to predict the unpredictable.