Patents by Inventor Chad LAVY

Chad LAVY 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: 20250130982
    Abstract: In some aspects, the techniques described herein relate to a method including: determining, by an embedding engine, a first plurality of nodes in a graph database; generating, by the embedding engine, a property-level vector embedding for each node of the first plurality of nodes, wherein each property-level vector embedding is based on a node property defined by each node of the first plurality of nodes; determining, by the embedding engine, a second plurality of nodes; generating, by the embedding engine, a node-level vector embedding for each node in the second plurality of nodes, wherein each node-level vector embedding is based on a type of each node in the second plurality of nodes; and persisting, by the embedding engine, each property-level vector embedding and each node-level vector embedding in a vector database with an association to an index key.
    Type: Application
    Filed: October 20, 2023
    Publication date: April 24, 2025
    Inventors: Yawwani GUNAWARDANA, Adrian STETCO, Chad LAVY, Mani KEERAN, Xiran SHI, Denis KOCHEDYKOV
  • Publication number: 20250131037
    Abstract: In some aspects, the techniques described herein relate to a method including: receiving a first list entity vector embedding, wherein the first list entity vector embedding is of a categorical type and is associated with a list entity; generating a first similarity score between the first list entity vector embedding and a stored vector embedding of a plurality of stored vector embeddings, wherein the stored vector embedding of the plurality of stored vector embeddings is of the categorical type and is associated with a stored entity; adding the first similarity score to a second similarity score, wherein the sum of the first similarity score and the second similarity score is an overall similarity score associated with the stored entity; querying a datastore to retrieve data associated with the stored entity; and returning, as output of the list expansion module, the data associated with the stored entity.
    Type: Application
    Filed: October 20, 2023
    Publication date: April 24, 2025
    Inventors: Yawwani GUNAWARDANA, Adrian STETCO, Chad LAVY, Mani KEERAN, Xiran SHI, Denis KOCHEDYKOV
  • Publication number: 20240169373
    Abstract: Various methods, apparatuses/systems, and media for implementing a unified framework for collecting, processing, enriching, validating, and distributing explicit and implicit feedback of all types from any software application agnostic to use case and contexts are disclosed. A processor receives a query from an application to collect feedback data from a particular field within an ontology that includes mapping of application level details where all fields are being used in capturing data; analyzes the query and traverses up ontology branches of an ontology structure of the ontology to create one or more feedback collection schemas based on the received query; collects the feedback data from the particular field based on the one or more feedback collection schemas; assigns the collected feedback data an event under a topic for consumption so that an end user can subscribe to the event and consume the feedback data under the topic as desired.
    Type: Application
    Filed: January 3, 2023
    Publication date: May 23, 2024
    Applicant: JPMorgan Chase Bank, N.A.
    Inventors: Abhishek MITRA, Balasubramanian DHAKSHINAMOORTHY, Chad LAVY, Parveza RAHMAN, Ilan SELINGER
  • Publication number: 20230120826
    Abstract: A method for machine learning-based data matching and reconciliation may include: ingesting a plurality of records from a plurality of data sources; identifying company associated with each of the plurality of records; assigning a unique identifier to each uniquely identified company; matching each of the records to one of the uniquely identified companies using a trained company matching machine learning engine; identifying a primary company record in the matching records and associating other matching records with the primary company record; matching each of the records to a contact using a trained contact matching machine learning engine; identifying a primary contact record in the matching records and associating other matching records with the primary contact record; synchronizing the plurality of records in a graph database using the unique identifier; receiving feedback on the matching companies and/or matching contacts; and updating the trained company matching machine learning engine.
    Type: Application
    Filed: October 18, 2022
    Publication date: April 20, 2023
    Inventors: Sadra AMIRI MODGHADAM, Chad LAVY, Steve TURK