Bank of America Corporation

Ashwin Roongta

Anuj Shah

Sayan Banerjee

Thomas A Sodano

Zhexiao Zhang

Yvonne Y Li

Aaron Blogg

Leslieann Osborne

Aftab Khan

Beena Khushalani

Peter Michael Farrell

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

Embodiments of the present invention provide a system for electronic identification of attributes for performing maintenance, monitoring, and distribution of designated resource assets. In particular, the system may be configured to extract one or more legacy resources from a data repository of an entity system associated with an entity, wherein the legacy resources are in a first format, convert the one or more legacy resources from the first format to a second format, process the one or more legacy resources, via one or more machine learning models, identify one or more attributes based on processing the one or more legacy resources via the one or machine learning models, and implement one or more actions based on the one or more attributes.

System for electronic identification of attributes for performing maintenance, monitoring, and distribution of designated resources

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.

Systems and methods for recognizing and depositing digital files. Receive an unidentified file. Identify a target client and at least one account associated with the unidentified file. Segment the unidentified file into one or more document images. For each document image: scan the image and extract content, label the image based on its content, select an account of the target client, and deposit the labeled image in the selected account.

Digital file recognition and deposit system

A database management system for processing large volumes of data in a key-value store database is provided. The system may be configured to receive a plurality of filled fillable request forms where each request form may include a request including a plurality of field labels and a plurality of fillable text fields corresponding to each of the plurality of the field labels. The system may be configured to extract each set of inputted data from each fillable text field. The system may be configured to store, in the key-value store database, for each request form, each of the plurality of field labels and the corresponding set of inputted data as a combination key-value pair. The combination key may be equal to a WIP ID number, form ID number and field ID number. The corresponding value may be equal to the set of data of the corresponding field ID number.

EDOC utility using non-structured-query-language databases

A system for managing a digital resource vehicle within a distributed trust computing network, such as a blockchain network or the like. The digital resource vehicle monitors for occurrence of output-triggering events and in response to determining the occurrence of event, determines output recipients, distribution portions and initiates execution of the resource events. The distributed trust computing network ensures immutability of the digital resource vehicle and provides a source of truth for verification/validation of triggering events and resulting resource events.

Managing a third-party recipient digital resource vehicle via a distributed trust computing network

A system for dynamic and automated conversion of party objects based on a change in party type and/or party classification. Party-type and/or party-classification specific rules are relied on to determine which data and processes to include in the converted party object. As such, the converted party object provides for (i) retaining data and processes that are common to both party types and/or classifications, (ii) disabling or otherwise deleting data and processes not associated with the new party type and/or classification, and (iii) adding new data, data entry fields and processes for data and processes associated with the new party type and/or classification that were not associated with the previous party type.

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East Brunswick, NJ, United States of America

Ashwin Roongta