Abstract: Systems, methods, and devices for decentralized notification services are disclosed. In some aspects, the techniques described herein relate to: providing a distributed notification service configured to execute on each node of a plurality of nodes of a decentralized peer-to-peer platform; determining, by the distributed notification service of a first node, a notification definition, where the notification definition is from a catalog of notification definitions and included in a distributed application executing on the first node; sending, by the distributed notification service of a second node, a notification message, where the notification message is associated with the notification definition; receiving, by the distributed notification service of the first node, the notification message; and delivering, by the distributed notification service of the first node, the notification message in the distributed application executing on the first node.

Abstract: In some aspects, the techniques described herein relate to a method including: providing, on a peer-to-peer distributed network, an aggregation server; implementing, at a first node of the peer-to-peer distributed network, a machine learning (ML) model, wherein the ML model is trained on a first private data set; sending the ML model to the aggregation server; replicating the ML model to a second node of the peer-to-peer distributed network, wherein the ML model is retrained on a second private data set to generate a retrained model; sending the retrained model to the aggregation server; aggregating the ML model and the retrained model to generate an aggregated model; and replicating the aggregated model to the first node of the peer-to-peer distributed network, wherein the ML model is promoted to a production model at the first node of the peer-to-peer distributed network.

Abstract: The invention relates to creating a secure, decentralized, cloud-based network or physical/virtual infrastructure that enables the payments industry to redefine payment processing and information sharing. The innovative network addresses key pain points by reducing payment delays and touch points, realizing faster and comprehensive payment tracking, real-time sanctions, AML and fraud management tools.

Abstract: A method for distributed application distribution may include: (1) receiving, at a first decentralized marketplace instance in a distributed ledger network and from a first node of the plurality of nodes, the first node associated with a distributed application creator, a distributed application; (2) making available, by the first decentralized marketplace instance, the distributed application to decentralized marketplace instances, wherein the first node is configured to provide the distributed application to a second node, the second node associated with a distributed application collaborator; (3) receiving, at a second decentralized marketplace instance and from the second node, a modified version of the distributed application; and (4) making available, by the second decentralized marketplace instance, the modified version of the distributed application to the decentralized marketplace instances, wherein the second node is configured to provide the distributed application to a third node, the third node a

Abstract: A method for tokenizing assets via a blockchain-to-blockchain bridge may include: receiving a first instruction from a client to mint first tokens for a client asset; encumbering the client asset on a custody ledger; creating a balance of the first tokens on a first distributed ledger; receiving a second instruction to lock and mint the first tokens to a second distributed ledger as second tokens; receiving verification from a custodian that the first tokens are locked on a lock ledger for the custodian; instructing a blockchain bridge computer program to mint second tokens to a client address on the second distributed ledger; receiving a third instruction to return the second tokens to the first distributed ledger; executing a public burn of the second tokens on the second distributed ledger; and instructing the custodian to unlock the first tokens and move the first tokens to the first distributed ledger.

Abstract: The invention relates to creating a secure, decentralized, cloud-based network or physical/virtual infrastructure that enables the payments industry to redefine payment processing and information sharing. The innovative network addresses key pain points by reducing payment delays and touch points, realizing faster and comprehensive payment tracking, real-time sanctions, AML and fraud management tools.

Abstract: The invention relates to creating a secure, decentralized, cloud-based network or physical/virtual infrastructure that enables the payments industry to redefine payment processing and information sharing. The innovative network addresses key pain points by reducing payment delays and touch points, realizing faster and comprehensive payment tracking, real-time sanctions, AML and fraud management tools.

Abstract: A method for distributed application distribution may include: (1) receiving, at a first decentralized marketplace instance in a distributed ledger network and from a first node of the plurality of nodes, the first node associated with a distributed application creator, a distributed application; (2) making available, by the first decentralized marketplace instance, the distributed application to decentralized marketplace instances, wherein the first node is configured to provide the distributed application to a second node, the second node associated with a distributed application collaborator; (3) receiving, at a second decentralized marketplace instance and from the second node, a modified version of the distributed application; and (4) making available, by the second decentralized marketplace instance, the modified version of the distributed application to the decentralized marketplace instances, wherein the second node is configured to provide the distributed application to a third node, the third node a

Abstract: Systems, methods, and devices for decentralized notification services are disclosed. In some aspects, the techniques described herein relate to: providing a distributed notification service configured to execute on each node of a plurality of nodes of a decentralized peer-to-peer platform; determining, by the distributed notification service of a first node, a notification definition, where the notification definition is from a catalog of notification definitions and included in a distributed application executing on the first node; sending, by the distributed notification service of a second node, a notification message, where the notification message is associated with the notification definition; receiving, by the distributed notification service of the first node, the notification message; and delivering, by the distributed notification service of the first node, the notification message in the distributed application executing on the first node.

Abstract: Systems, methods, and devices for decentralized notification services are disclosed. In some aspects, the techniques described herein relate to: providing a distributed notification service configured to execute on each node of a plurality of nodes of a decentralized peer-to-peer platform; determining, by the distributed notification service of a first node, a notification definition, where the notification definition is from a catalog of notification definitions and included in a distributed application executing on the first node; sending, by the distributed notification service of a second node, a notification message, where the notification message is associated with the notification definition; receiving, by the distributed notification service of the first node, the notification message; and delivering, by the distributed notification service of the first node, the notification message in the distributed application executing on the first node.

Abstract: A method for privacy preserving machine learning model sharing may include a computer program for a first institution of a plurality of institutions in a distributed ledger network: receiving transaction data for a transaction; training a local machine learning model using the transaction data; submitting parameters for the local machine learning model to the distributed ledger network as a private transaction with a trusted entity, wherein the trusted entity receives parameters for a plurality of local machine learning models from the distributed ledger network for the plurality of institutions in the distributed ledger network and aggregates the parameters into an aggregated machine learning model and submits the aggregated parameters to the distributed ledger network as one or more transactions; receiving, from the distributed ledger network, the aggregated parameters for the aggregated machine learning model; and updating the local machine learning model with the aggregated parameters.

Abstract: Systems and methods for systems and methods for cryptocurrency checkout are disclosed. According to one embodiment, a method may include a financial institution backend: receiving, from a merchant backend, control of a checkout flow for a transaction comprising an identification of a good or service to purchase, wherein the merchant backend accepts payment in cryptocurrency; receiving login credentials for the customer from the computer program; retrieving a plurality of customer accounts for the customer; receiving selection of one or more of the plurality of customer accounts to fund the cryptocurrency for the transaction; deducting a payment amount for the transaction from the one or more selected customer accounts; confirming payment to the merchant backend; returning control of the checkout flow to the merchant backend, wherein the merchant backend completes the transaction with the customer; and settling the transaction by providing a settlement amount in cryptocurrency to the merchant backend.

Abstract: Systems, methods, and devices for decentralized notification services are disclosed. In some aspects, the techniques described herein relate to: providing a distributed notification service configured to execute on each node of a plurality of nodes of a decentralized peer-to-peer platform; determining, by the distributed notification service of a first node, a notification definition, where the notification definition is from a catalog of notification definitions and included in a distributed application executing on the first node; sending, by the distributed notification service of a second node, a notification message, where the notification message is associated with the notification definition; receiving, by the distributed notification service of the first node, the notification message; and delivering, by the distributed notification service of the first node, the notification message in the distributed application executing on the first node.

Abstract: Systems and methods for generating synthetic data using federated, collaborative, privacy preserving learning models are disclosed. In one embodiment, a method for generating synthetic data from real data for use in a federated learning network may include: (1) conducting, by a backend for a first institution of a plurality of institutions in a federated learning network, a transaction comprising transaction data; (2) generating, by the backend for the first institution, local synthetic data for the transaction data using a local synthetic data generating model; (3) sharing, by the backend for the first institution, the local synthetic data with a plurality of backends for other institutions; (4) receiving, by the backend for the first institution, global synthetic data from the plurality of backends for the other institutions; and (5) training, by the backend for the first institution, a local machine learning model with the global synthetic data.

Abstract: Systems and methods for federated learning based on a reward-driven approach are disclosed. In one embodiment, a method may include: (1) receiving, by a federated contribution computer program executed by a federated node in a distributed ledger network, a plurality of local machine learning model updates from a plurality of clients in the distributed ledger networks; (2) retrieving, by the federated contribution computer program, a prior global machine learning model; (3) calculating, by the federated contribution computer program, a current global machine learning model based on the prior global machine learning model and the plurality of local machine learning model updates; (4) determining, by the federated contribution computer program, a federated contribution for each client based on each client's federated contribution to the current global machine learning model; and (5) issuing, by the federated contribution computer program, rewards to each client based on the client's federated contribution.

Abstract: Systems and methods for federated learning using peer-to-peer networks are disclosed. A method may include: electing a participant node as a collaborator node using a consensus algorithm; the collaborator node generating and broadcasting a public/private key pair; the participant nodes generating public/private key pairs for each communication with the collaborator node, encrypting and broadcasting a message comprising a parameter for a local machine learning model for the participant node and its public key with the collaborator node's public key, the collaborator node decrypting the encrypted messages, updating an aggregated machine learning model with the decrypted parameters, encrypting and broadcasting update messages each comprising an update with each participant node's public key; the participant nodes decrypting one of the messages with their private keys, and the participant nodes updating their local machine learning models with the update.

Abstract: A method may include an aggregator node in a distributed computer network: generating an aggregator node public/private key pair; communicating the aggregator node public key to participant nodes; receiving, from each participant node, a message comprising a local machine learning (ML) model encrypted with a participant node private key and the aggregator node public key, and a participant node public key encrypted with the aggregator node public key; decrypting the local ML models and the participant node public keys using the aggregator node public key; decrypting the local ML models using the participant node public keys; generating an aggregated ML model based on the local ML models; encrypting, with each participant node public key, the aggregated ML model; and communicating the encrypted ML models to all participant nodes. Each participant node decrypts one of the encrypted ML models and modifies its local ML model with the aggregated ML model.

Abstract: A method for distributed application distribution may include: (1) receiving, at a first decentralized marketplace instance in a distributed ledger network and from a first node of the plurality of nodes, the first node associated with a distributed application creator, a distributed application; (2) making available, by the first decentralized marketplace instance, the distributed application to decentralized marketplace instances, wherein the first node is configured to provide the distributed application to a second node, the second node associated with a distributed application collaborator; (3) receiving, at a second decentralized marketplace instance and from the second node, a modified version of the distributed application; and (4) making available, by the second decentralized marketplace instance, the modified version of the distributed application to the decentralized marketplace instances, wherein the second node is configured to provide the distributed application to a third node, the third node a

Abstract: Systems and methods for machine-learning augmented application monitoring are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor and a memory, a method for machine-learning augmented application monitoring may include: (1) receiving data from a plurality of data sources; (2) storing the data in a data store; (3) transforming the data; (4) extracting one or more feature and metric from the transformed data; (5) feeding the feature and metric into a machine learning model to identify at least one contributing metric; and (6) associating the contributing metric with at least one incident.

Abstract: Methods, non-transitory computer readable media, and natural language understanding (NLU) service device are disclosed that receive raw requests for price quotes for financial instruments from two or more different types of communication channels and via one or more communication networks. The raw requests are in two or more different formats and comprise unstructured data. The unstructured data of the raw requests is analyzed to identify key value(s) associated with the raw requests. The raw requests are then automatically transformed based on the identified key value(s) into price quote request(s) comprising structured data in particular format(s) required by pricing application(s) hosted by pricing server device(s). The price quote request(s) are output to one or more of the pricing application(s) via another one or more communication networks in order to facilitate automated pricing of the financial instruments.