Bank of America Corporation

Yvonne Y Li

Zhexiao Zhang

Ashwin Roongta

Anuj Shah

Thomas A Sodano

Leslieann Osborne

Aaron Blogg

Yipeng Zhu

Nizar Naitlho

Systems, computer program products, and methods are described herein for automatically building dynamic queries for identifying data in unstructured datasets. The present disclosure is configured to receive a plurality of pre-defined query templates associated with an alert type(s); dynamically filter the plurality of pre-defined query templates based on the alert type(s); identify an unstructured database note(s) based on a user identifier; apply the unstructured database note(s) and the filtered plurality of pre-defined query templates to an unstructured notes search engine; generate, by the unstructured notes search engine, at least one raw score for the at least one unstructured database note and for each alert type(s); transform, by the unstructured notes search engine, the at least one raw score to a relevancy score using a quantile transformation; and determine a highest relevancy score for each of the at least one unstructured database note for the user identifier.

Systems and methods for automatically building dynamic queries for identifying data in unstructured datasets

Systems, computer program products, and methods are described herein for automatically generating resource distributions. The present invention may be configured to receive a text-based instruction and parse, using a machine learning model, the text-based instruction to generate a structured resource distribution including predicted distribution elements. The present invention may be configured to generate, based on the structured resource distribution, a resource distribution. In some embodiments, the text-based instruction may include an email message, an SMS message, recorded speech converted to text, text input to a chat function, text recognized in an image, and/or the like.

Electronic system for automatically generating resource distributions based on SMS-based instructions using machine learning

Systems, computer program products, and methods are described herein for automatically generating resource distributions. The present invention may be configured to receive a text-based instruction and determine, based on the text-based instruction, a sender alias from which the text-based instruction was sent. The present invention may be configured to determine, based on user data in a user information data structure, a user associated with the sender alias, determine, based on user data associated with the user in the user information data structure and based on predicted distribution elements from a machine learning model, actual distribution elements, and generate, based on the actual distribution elements, a resource distribution.

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Jersey City, NJ, United States of America

Yvonne Y Li