Jpmorgan Chase Bank, N.a.

Sudhir Upadhyay

Tulasi Movva

Suresh Shetty

John Corwin Hunter

Palka S Patel

Ramesh Babu Anandhan

Thomas Eapen

Samer Falah

Tiffany Ashley Wan

Elizabeth Polanco Aquino

Luciane Sant'Anna

Leticia Pui Sze Lim

Vinay Somashekar

Chang Yang Jiao

Rob Otter

Sergul Aydore

David Voell

Jimmy C Mark

Monik Raj Behera

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 associated with a distributed application consumer.

System and method for decentralized marketplaces

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.

Systems and methods for federated learning using distributed messaging with entitlements for anonymous computation and secure delivery of model

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.

System and method for decentralized notification services

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.

System and method for implementing an interbank information network

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.

Systems and methods for machine-learning augmented application monitoring

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.

Methods for automatically generating structured pricing models from unstructured multi-channel communications and devices thereof

Growing community of inventors

Edison, NJ, United States of America

Sudhir Upadhyay