Patents by Inventor Amod Jha

Amod Jha has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20240305609
    Abstract: Systems, computer program products, and methods are described herein for resource transfer mode evaluation in distributed network using semi-dynamic tokenized graph node processing. The present disclosure is configured to receive resource data from one or more resource transfer channels; extract metadata from the resource data and determine one or more resource transfer processing requests; generate a dynamic hash value for the one or more resource transfer processing requests; tokenize the dynamic hash value to generate a semi-dynamic token; select a resource gateway and a resource mode for the one or more resource transfer processing requests; generate a key value pair for the selected resource gateway and the resource mode; tokenize the key value pair and store the tokenized key value pair on a distributed ledger; and flag one or more non-selected resource gateways and resource nodes.
    Type: Application
    Filed: March 10, 2023
    Publication date: September 12, 2024
    Applicant: BANK OF AMERICA CORPORATION
    Inventors: Sakshi Bakshi, Amod Jha, Anup Kumar Kedia, Siva Kumar Paini, Sachin Juneja, Amit Agarwal
  • Publication number: 20240054413
    Abstract: Systems, computer program products, and methods are described herein for implementing parametric optimization analysis for resource selection. The present invention is configured to determine a first set of requirements associated with a resource exchange agreement; identify one or more non-fungible tokens (NFTs) for one or more categories of past resource exchange agreements based on at least the first set of requirements; extract, from the one or more NFTs, one or more resource descriptors associated with one or more past resource exchange agreements in the one or more categories; predict, using a machine learning subsystem, an optimal resource valuation model for one or more resources that meet the first set of requirements using the one or more resource descriptors and the first set of requirements; and transmit control signals configured to cause a first end-point device to display the optimal resource valuation model.
    Type: Application
    Filed: August 15, 2022
    Publication date: February 15, 2024
    Applicant: BANK OF AMERICA CORPORATION
    Inventors: Sakshi Bakshi, Amod Jha, Siva Kumar Paini, Ashlesha Mithra
  • Patent number: 11574304
    Abstract: Systems, methods, and apparatus are provided for a dynamic contract payment term (“payterm”) generator. A machine learning algorithm may generate a replacement payment term for a contract based on market-based parameters and blockchain metadata for the contract. The blockchain metadata may encode hierarchical interdependencies between contracts using blockchain encryption. The blockchain metadata may be applied to auto-generate machine learning inputs for related contracts having interdependent payment terms. The machine learning inputs may include contract parameters that have been extracted and encrypted as blockchain metadata, as well as market-based parameters extracted from enterprise sources.
    Type: Grant
    Filed: May 10, 2021
    Date of Patent: February 7, 2023
    Assignee: Bank of America Corporation
    Inventors: Sakshi Bakshi, Siva Kumar Paini, Amod Jha, Amit Kumar Sati
  • Publication number: 20220358495
    Abstract: Systems, methods, and apparatus are provided for a dynamic contract payment term (“payterm”) generator. A machine learning algorithm may generate a replacement payment term for a contract based on market-based parameters and blockchain metadata for the contract. The blockchain metadata may encode hierarchical interdependencies between contracts using blockchain encryption. The blockchain metadata may be applied to auto-generate machine learning inputs for related contracts having interdependent payment terms. The machine learning inputs may include contract parameters that have been extracted and encrypted as blockchain metadata, as well as market-based parameters extracted from enterprise sources.
    Type: Application
    Filed: May 10, 2021
    Publication date: November 10, 2022
    Inventors: Sakshi Bakshi, Siva Kumar Paini, Amod Jha, Amit Kumar Sati