US-20260127031-A1 - SYSTEM AND METHOD FOR DIGITAL RESOURCE ALLOCATION VIA AN INTERACTIVE COMPUTATIONAL FRAMEWORK

Abstract

Systems, computer program products, and methods are described herein for digital resource allocation via an interactive computational framework. The present disclosure includes receiving credentials at a first endpoint device, authenticating the credentials, receiving a digital resource allocation request from the first endpoint device, the digital resource allocation request comprising parameters of a user associated with the authenticated credentials and item parameters of an item associated with the digital resource allocation request, retrieving user data associated with the user, determining, using a machine learning model, a digital resource allocation proposal based on the user data, generating, using a generative AI model, based on the digital resource allocation proposal, a smart contract comprising ownership, appending the smart contract to a distributed ledger, and transferring digital resources according to the smart contract.

Inventors

• Pratap Dande
• Tony Aidoo
• Noell Y. Eury
• Brian Neal Jacobson
• Dennis Vranjesevic
• Rahul Yaksh
• Bojan Zdravkovic

Assignees

• BANK OF AMERICA CORPORATION

Dates

Publication Date

20260507

Application Date

20241104

Claims (20)

1. 1 . A system for digital resource allocation via an interactive computational framework, the system comprising: a processing device; and a non-transitory storage device containing instructions, when executed by the processing device, the instructions cause the processing device to perform the steps of: receiving credentials at a first endpoint device; authenticating the credentials; receiving a digital resource allocation request from the first endpoint device, the digital resource allocation request comprising parameters of a user associated with the authenticated credentials and item parameters of an item associated with the digital resource allocation request; retrieving user data associated with the user; determining, using a machine learning model, a digital resource allocation proposal based on the user data; generating, using a generative AI model, based on the digital resource allocation proposal, a smart contract comprising ownership; appending the smart contract to a distributed ledger; and transferring digital resources according to the smart contract.
2. 2 . The system of claim 1 , wherein the instructions further cause the processing device to perform the steps of: generating, by using the item parameters provided to the generative AI model, a maintenance schedule and corresponding event data for the item; transmitting the event data to the first endpoint device; receiving, at a predetermined interval, telemetry data from the item; amending, based on the telemetry data, the maintenance schedule and the corresponding event data; and transmitting, after amending, the maintenance schedule and the event data to the first endpoint device.
3. 3 . The system of claim 2 , wherein the instructions further cause the processing device to perform the steps of: receiving, at the first endpoint device, an input comprising a query; processing, via the generative AI model, the input to identify one or more keywords associated with the query, wherein the generative AI model is trained on natural language data, the smart contract, and the parameters of the user; querying, via an API call upon a condition where the input comprises a maintenance inquiry, a database comprising the event data; generating, using the generative AI model, a response to the input; and causing to display on the first endpoint device the response.
4. 4 . The system of claim 1 , wherein the user data associated with the user comprises parameters of the user selected from a group consisting of: the user data comprising social media marketplace activity, user account data, employment history from a background check API, and collateral value assessment.
5. 5 . The system of claim 1 , wherein the digital resource allocation proposal based on the user data comprises a digital resource allocation amount, rate, and time period.
6. 6 . The system of claim 1 , wherein the smart contract further comprises maintenance records.
7. 7 . The system of claim 1 , wherein the smart contract further comprises an accident history.
8. 8 . A computer program product for digital resource allocation via an interactive computational framework, the computer program product comprising a non-transitory computer-readable medium comprising code causing an apparatus to: receive credentials at a first endpoint device; authenticate the credentials; receive a digital resource allocation request from the first endpoint device, the digital resource allocation request comprising parameters of a user associated with the authenticated credentials and item parameters of an item associated with the digital resource allocation request; retrieve user data associated with the user; determine, using a machine learning model, a digital resource allocation proposal based on the user data; generate, using a generative AI model, based on the digital resource allocation proposal, a smart contract comprising ownership; append the smart contract to a distributed ledger; and transfer digital resources according to the smart contract.
9. 9 . The computer program product of claim 8 , wherein the code further causes the apparatus to: generate, by using the item parameters provided to the generative AI model, a maintenance schedule and corresponding event data for the item; transmit the event data to the first endpoint device; receive, at a predetermined interval, telemetry data from the item; amend, based on the telemetry data, the maintenance schedule and the corresponding event data; and transmit, after amending, the maintenance schedule and the event data to the first endpoint device.
10. 10 . The computer program product of claim 9 , wherein the code further causes the apparatus to: receive, at the first endpoint device, an input comprising a query; process, via the generative AI model, the input to identify one or more keywords associated with the query, wherein the generative AI model is trained on natural language data, the smart contract, and the parameters of the user; query, via an API call upon a condition where the input comprises a maintenance inquiry, a database comprising the event data; generate, using the generative AI model, a response to the input; and cause to display on the first endpoint device the response.
11. 11 . The computer program product of claim 8 , wherein the user data associated with the user comprises parameters of the user selected from a group consisting of: the user data comprising social media marketplace activity, user account data, employment history from a background check API, and collateral value assessment.
12. 12 . The computer program product of claim 8 , wherein the digital resource allocation proposal based on the user data comprises a digital resource allocation amount, rate, and time period.
13. 13 . The computer program product of claim 8 , wherein the smart contract further comprises maintenance records.
14. 14 . The computer program product of claim 8 , wherein the smart contract further comprises an accident history.
15. 15 . A method for digital resource allocation via an interactive computational framework, the method comprising: receiving credentials at a first endpoint device; authenticating the credentials; receiving a digital resource allocation request from the first endpoint device, the digital resource allocation request comprising parameters of a user associated with the authenticated credentials and item parameters of an item associated with the digital resource allocation request; retrieving user data associated with the user; determining, using a machine learning model, a digital resource allocation proposal based on the user data; generating, using a generative AI model, based on the digital resource allocation proposal, a smart contract comprising ownership; appending the smart contract to a distributed ledger; and transferring digital resources according to the smart contract.
16. 16 . The method of claim 15 , the method further comprising: generating, by using the item parameters provided to the generative AI model, a maintenance schedule and corresponding event data for the item; transmitting the event data to the first endpoint device; receiving, at a predetermined interval, telemetry data from the item; amending, based on the telemetry data, the maintenance schedule and the corresponding event data; and transmitting, after amending, the maintenance schedule and the event data to the first endpoint device.
17. 17 . The method of claim 16 , the method further comprising: receiving, at the first endpoint device, an input comprising a query; processing, via the generative AI model, the input to identify one or more keywords associated with the query, wherein the generative AI model is trained on natural language data, the smart contract, and the parameters of the user; querying, via an API call upon a condition where the input comprises a maintenance inquiry, a database comprising the event data; generating, using the generative AI model, a response to the input; and causing to display on the first endpoint device the response.
18. 18 . The method of claim 15 , wherein the user data associated with the user comprises parameters of the user selected from a group consisting of: the user data comprising social media marketplace activity, user account data, employment history from a background check API, and collateral value assessment.
19. 19 . The method of claim 15 , wherein the digital resource allocation proposal based on the user data comprises a digital resource allocation amount, rate, and time period.
20. 20 . The method of claim 15 , wherein the smart contract further comprises maintenance records.

Description

TECHNOLOGICAL FIELD Example implementations of the present disclosure relate to a system and method for digital resource allocation via an interactive computational framework. BACKGROUND In the context of vehicle acquisition, systems facilitate the process through resource allocation or usage-based agreements. Existing vehicle acquisition methods often rely on predefined models that fail to account for dynamic variables such as changing conditions related to vehicle operation. Usage agreements are typically based on static limits and predetermined structures, without adjusting for actual vehicle utilization. The data used in these processes is often manually input, leading to inefficiencies. Furthermore, generalized assumptions are applied to estimate vehicle depreciation, resulting in inaccurate projections of residual value. Coverage requirements are imposed according to fixed parameters, which may lead to unnecessary costs despite use of the vehicle. These challenges highlight the need for a system and method for digital resource allocation via an interactive computational framework. BRIEF SUMMARY Systems, methods, and computer program products are provided for digital resource allocation via an interactive computational framework. In one aspect, a system for digital resource allocation via an interactive computational framework is presented. The system may include a processing device, and a non-transitory storage device containing instructions, when executed by the processing device, the instructions cause the processing device to perform the steps of receiving credentials at a first endpoint device, authenticating the credentials, receiving a digital resource allocation request from the first endpoint device, the digital resource allocation request including parameters of a user associated with the authenticated credentials and item parameters of an item associated with the digital resource allocation request, retrieving user data associated with the user, determining, using a machine learning model, a digital resource allocation proposal based on the user data, generating, using a generative AI model, based on the digital resource allocation proposal, a smart contract including ownership, appending the smart contract to a distributed ledger, and transferring digital resources according to the smart contract. In some implementations, the instructions may further cause the processing device to perform the steps of generating, by using the item parameters provided to the generative AI model, a maintenance schedule and corresponding event data for the item, transmitting the event data to the first endpoint device, receiving, at a predetermined interval, telemetry data from the item, amending, based on the telemetry data, the maintenance schedule and the corresponding event data, and transmitting, after amending, the maintenance schedule and the event data to the first endpoint device. In some implementations, the instructions may further cause the processing device to perform the steps of receiving, at the first endpoint device, an input including a query, processing, via the generative AI model, the input to identify one or more keywords associated with the query, wherein the generative AI model is trained on natural language data, the smart contract, and the parameters of the user, querying, via an API call upon a condition where the input includes a maintenance inquiry, a database including the event data, generating, using the generative AI model, a response to the input, and causing to display on the first endpoint device the response. In some implementations, the user data associated with the user includes parameters of the user selected from a group consisting of the user data including social media marketplace activity, user account data, employment history from a background check API, and collateral value assessment. In some implementations, the digital resource allocation proposal based on the user data includes a digital resource allocation amount, rate, and time period. In some implementations, the smart contract further includes maintenance records. In some implementations, the smart contract further includes an accident history. In another aspect, a computer program product for digital resource allocation via an interactive computational framework is presented. The computer program product may include a non-transitory computer-readable medium including code causing an apparatus to receive credentials at a first endpoint device, authenticate the credentials, receive a digital resource allocation request from the first endpoint device, the digital resource allocation request including parameters of a user associated with the authenticated credentials and item parameters of an item associated with the digital resource allocation request, retrieve user data associated with the user, determine, using a machine learning model, a digital resource allocation proposal based on the user data, generate