# US 2025/0095035 A1 · Methods and apparatus to generate customized customer messages > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0095035 A1 - Document ID: US20250095035A1 - Status: Published application - Kind: A1 - Application number: 18/593,365 - Filed: 2024-03-01 - Published: 2025-03-20 - Earliest claimed: 2023-09-15 - Assignees: Directv, LLC - Applicants: Directv, LLC - Inventors: Johnson Hao-Wen Kuan; Taylor Philip Hosbach; Malav Hiren Shah; Usman Quddoos - Web page: https://patentgrep.com/patent/US20250095035A1 ## CPC - G06Q 30/0281: {Customer communication at a business location, e.g. providing product or service information, consulting} - G06F 16/2455: Query execution - G06F 40/186: Templates - G06F 40/40: Processing or translation of natural language (natural language analysis G06F40/20; semantic analysis G06F40/30) - G06F 40/56: Natural language generation - G06Q 30/016: After-sales - 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 - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Systems, apparatus, articles of manufacture, and methods to generate customized customer messages are disclosed. An example method includes selecting a prompt template based on an intended purpose of the customized customer message and a type of communication of the customized customer message, generating a prompt based on the customer data for an identified customer and the prompt template, providing the prompt to a large language model to cause generation of the customized customer message, and causing transmission of the customized customer message. ## Claims ### Claim 1 A non-transitory machine-readable storage medium comprising instructions that, when executed or instantiated by programmable circuitry, facilitate performance of operations, comprising: select a prompt template based on an intended purpose of a customized customer message and a type of communication of the customized customer message; generate a prompt based on the prompt template; provide the prompt to a large language model to cause generation of the customized customer message; and cause transmission of the customized customer message. ### Claim 2 (depends on 1) The non-transitory machine-readable medium of claim 1, wherein the generated prompt includes customer data. ### Claim 3 (depends on 2) The non-transitory machine-readable medium of claim 2, wherein the customer data is representative of a plurality of customers. ### Claim 4 (depends on 1) The non-transitory machine-readable medium of claim 1, wherein the operations further comprise generating an embedding based on customer data, the embedding provided to the large language model as a context for the prompt. ### Claim 5 (depends on 4) The non-transitory machine-readable medium of claim 4, wherein the operations further comprise: accessing a plurality of fields of customer data; removing fields having null or empty data from the plurality of columns; sorting the remaining fields; separating the sorted fields into a first group of fields and a second group of fields; and computing the embedding based on the first group of fields and randomly selected fields from the second group of fields. ### Claim 6 (depends on 5) The non-transitory machine-readable medium of claim 5, wherein the embedding is a first embedding and the randomly selected fields are first randomly selected fields, and the operations further comprise: generating a second embedding based on the first group of fields and second randomly selected fields from the second group of fields; and aggregating the first embedding and the second embedding to create an aggregated embedding, the aggregated embedding used as the context for the prompt. ### Claim 7 (depends on 1) The non-transitory machine-readable medium of claim 1, wherein the operations comprise analyzing the customized customer message to confirm that the customized customer message is acceptable for transmission, wherein the transmission of the customized customer message is to occur after the confirmation that the customized customer message is acceptable for transmission. ### Claim 8 (depends on 1) The non-transitory machine-readable medium of claim 1, wherein the prompt template is formatted as a structured query language query, and the execution of the structured query language query results in the generation of the prompt. ### Claim 9 A system comprising: programmable circuitry; a memory that stores executable instructions that, when executed or instantiated by the programmable circuitry, facilitate performance of operations including: select a prompt template based on an intended purpose of a customized customer message and a type of communication of the customized customer message; generate a prompt based on the prompt template; provide the prompt to a large language model to cause generation of the customized customer message; and cause transmission of the customized customer message. ### Claim 10 (depends on 9) The system of claim 9, wherein the generated prompt includes customer data. ### Claim 11 (depends on 10) The system of claim 10, wherein the customer data is representative of a plurality of customers. ### Claim 12 (depends on 9) The system of claim 9, wherein one or more of the at least one processor circuit is to cause one or more of the at least one processor circuit to generate an embedding based on customer data, the embedding provided to the large language model as a context for the prompt. ### Claim 13 (depends on 12) The system of claim 12, wherein the operations further comprise: accessing a plurality of attributes of customer data; removing attributes having null or empty data from the plurality of attributes; sorting the remaining attributes; separating the sorted attributes into a first group of attributes and a second group of attributes; and computing the embedding based on the first group of attributes and randomly selected attributes from the second group of attributes. ### Claim 14 (depends on 13) The system of claim 13, wherein the operations further comprise: generating a second embedding based on the first group of attributes and second randomly selected attributes from the second group of attributes; and aggregating the first embedding and the second embedding to create an aggregated embedding, the aggregated embedding used as the context for the prompt. ### Claim 15 (depends on 9) The system of claim 9, wherein the operations further comprise analyzing the customized customer message to confirm that the customized customer message is acceptable for transmission, wherein the transmission of the customized customer message is to occur after the confirmation that the customized customer message is acceptable for transmission. ### Claim 16 (depends on 9) The system of claim 9, wherein the prompt template is formatted as a structured query language query, and the execution of the structured query language query results in the generation of the prompt. ### Claim 17 A method for generating a customized customer message, the method comprising: selecting a prompt template based on an intended purpose of the customized customer message and a type of communication of the customized customer message; generating a prompt based on the prompt template; providing the prompt to a large language model to cause generation of the customized customer message; and causing transmission of the customized customer message. ### Claim 18 (depends on 17) The method of claim 17, wherein the prompt includes customer data. ### Claim 19 (depends on 18) The method of claim 18, wherein the customer data is representative of a plurality of customers. ### Claim 20 (depends on 17) The method of claim 17, further including generating an embedding based on customer data, the embedding provided to the large language model as a context for the prompt.