# US 2022/0277291 A1 · Electronic system for automatically generating resource distributions based on sms-based instructions using machine learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0277291 A1 - Document ID: US20220277291A1 - Status: Published application - Kind: A1 - Application number: 17/188,981 - Filed: 2021-03-01 - Published: 2022-09-01 - Earliest claimed: 2021-03-01 - Assignees: Bank of America Corporation - Applicants: Bank of America Corporation - Inventors: Ashwin Roongta; Aaron Blogg; Yvonne Y. Li; Leslieann Osborne; Anuj Shah; Thomas A. Sodano; Zhexiao Zhang - Web page: https://patentgrep.com/patent/US20220277291A1 ## CPC - G06Q 20/3255: {using mobile network messaging services for payment, e.g. SMS} - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06N 20/00: Machine learning - G06Q 20/385: {using an alias or single-use codes} - G06Q 20/386: {using messaging services or messaging apps (using mobile network messaging services for payment, e.g. SMS G06Q20/3255)} - G06Q 20/405: {Establishing or using transaction specific rules} - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract 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. ## Claims ### Claim 1 A system for automatically generating resource distributions, the system comprising: at least one non-transitory storage device; and at least one processing device coupled to the at least one non-transitory storage device, wherein the at least one processing device is configured to: receive a text-based instruction; determine, based on the text-based instruction, a sender alias from which the text-based instruction was sent; 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. ### Claim 2 (depends on 1) The system of claim 1, wherein the text-based instruction comprises at least one of an email message, an SMS message, recorded speech converted to text, text input to a chat function, or text recognized in an image. ### Claim 3 (depends on 1) The system of claim 1, wherein the predicted distribution elements comprise at least one of a predicted sending source retainer, a predicted receiving source retainer, a predicted amount of resources to be distributed, a predicted type of resource distribution, a predicted type of resources to be distributed, a predicted date on which resources are to be distributed, a predicted frequency at which resources are to be distributed, a predicted type of sending source retainer, or a predicted type of receiving source retainer. ### Claim 4 (depends on 1) The system of claim 1, wherein the at least one processing device is further configured to parse, using the machine learning model, the text-based instruction to determine additional information associated with the text-based instruction. ### Claim 5 (depends on 4) The system of claim 4, wherein the additional information comprises at least one of the sender alias, a recipient alias to which the text-based instruction was sent, an entity associated with the resource distribution, a name of the user, an address of the user, or content of the text-based instruction. ### Claim 6 (depends on 5) The system of claim 5, wherein the at least one processing device is configured to, when determining the user associated with the sender alias, determine, based on the user data in the user information data structure and based on the additional information, the user associated with the sender alias. ### Claim 7 (depends on 5) The system of claim 5, wherein the at least one processing device is configured to, when determining the actual distribution elements, determine, based on the user data in the user information data structure associated with the user, based on the predicted distribution elements, and based on the additional information, the actual distribution elements. ### Claim 8 (depends on 1) The system of claim 1, wherein the actual distribution elements comprise at least one of an actual sending source retainer, an actual receiving source retainer, an actual amount of resources to be distributed, an actual type of resource distribution, an actual type of resources to be distributed, an actual date on which resources are to be distributed, an actual frequency at which resources are to be distributed, an actual type of sending source retainer, or an actual type of receiving source retainer. ### Claim 9 (depends on 1) The system of claim 1, wherein the user information data structure comprises data that, for a plurality of users: associates users with aliases; associates users to source retainer officers; associates users to source retainer roles; associates users to other users; associates source retainers to distribution eligibility rules; or associates users and source retainers to historical resource distributions. ### Claim 10 (depends on 9) The system of claim 9, wherein the at least one processing device is configured to, when determining the actual distribution elements; determine source retainers associated with the user; determine source retainers for which the user is authorized to conduct resource distributions; determine source retainers of other users associated with the user; determine source retainers eligible for resource distributions via text-based instructions; and determine source retainers associated with historical resource distributions conducted by the user. ### Claim 11 (depends on 1) The system of claim 1, wherein the at least one processing device is configured to, when determining the actual distribution elements: determine that a predicted distribution element, of the predicted distribution elements, is a partial distribution element; and determine, based on the user data in the user information data structure associated with the user and based on the partial distribution element, an actual distribution element of the actual distribution elements. ### Claim 12 (depends on 1) The system of claim 1, wherein the at least one processing device is configured to, when determining the actual distribution elements: determine that multiple possible distribution elements are associated with a predicted distribution element of the predicted distribution elements; determine, based on a frequency of usage of the multiple possible distribution elements and based on a timing of usage of the multiple possible distribution elements, a likelihood of each of the multiple possible distribution elements corresponding to the predicted distribution element; and provide, to another user and in an order based on the likelihood of each of the multiple possible distribution elements corresponding to the predicted distribution element, the multiple possible distribution elements for selection by the other user for generation in the resource distribution. ### Claim 13 (depends on 1) The system of claim 1, wherein the at least one processing device is configured to provide, to another user, the resource distribution for authorization. ### Claim 14 (depends on 1) The system of claim 1, wherein the at least one processing device is configured to perform the resource distribution. ### Claim 15 A computer program product for automatically generating resource distributions comprising a non-transitory computer-readable medium comprising code causing a first apparatus to: receive a text-based instruction; determine, based on the text-based instruction, a sender alias from which the text-based instruction was sent; 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. ### Claim 16 (depends on 15) The computer program product of claim 15, wherein the text-based instruction comprises at least one of an email message, an SMS message, recorded speech converted to text, text input to a chat function, or text recognized in an image. ### Claim 17 (depends on 15) The computer program product of claim 15, wherein the predicted distribution elements comprise at least one of a predicted sending source retainer, a predicted receiving source retainer, a predicted amount of resources to be distributed, a predicted type of resource distribution, a predicted type of resources to be distributed, a predicted date on which resources are to be distributed, a predicted frequency at which resources are to be distributed, a predicted type of sending source retainer, or a predicted type of receiving source retainer. ### Claim 18 (depends on 15) The computer program product of claim 15, wherein the non-transitory computer-readable medium comprises code causing the first apparatus to parse, using the machine learning model, the text-based instruction to determine additional information associated with the text-based instruction. ### Claim 19 (depends on 15) The computer program product of claim 15, wherein the additional information comprises at least one of the sender alias, a recipient alias to which the text-based instruction was sent, an entity associated with the resource distribution, a name of the user, an address of the user, or content of the text-based instruction. ### Claim 20 A method for automatically generating resource distributions, the method comprising: receiving a text-based instruction; determining, based on the text-based instruction, a sender alias from which the text-based instruction was sent; determining, based on user data in a user information data structure, a user associated with the sender alias; determining, 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 generating, based on the actual distribution elements, a resource distribution.