# US 2026/0094085 A1 · Technologies for constraint generation using large language models > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0094085 A1 - Document ID: US20260094085A1 - Status: Published application - Kind: A1 - Application number: 18/902,028 - Filed: 2024-09-30 - Published: 2026-04-02 - Earliest claimed: 2024-09-30 - Applicants: Genesys Cloud Services, Inc. - Inventors: Bayu Wicaksono; Wei Xun Ter; Amith Kumar Tumu; William D'Attilio; Ziyun Tang; Ryan Hazelwood - Web page: https://patentgrep.com/patent/US20260094085A1 ## CPC - G06Q 10/06: Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling - G06Q 10/063118: {Staff planning in a project environment} - G06F 16/3329: Natural language query formulation - G06F 16/90332: {Natural language query formulation or dialogue systems} - G06F 40/35: Discourse or dialogue representation - G06F 40/40: Processing or translation of natural language (natural language analysis G06F40/20; semantic analysis G06F40/30) - G06Q 10/067: Enterprise or organisation modelling - G06Q 10/105: Human resources - G06Q 10/109: Time management, e.g. calendars, reminders, meetings or time accounting - G06Q 50/50: Business processes related to the communications industry (charging, metering or billing arrangements specially adapted for data communications H04L12/14; telephonic communication involving automatic or semi-automatic exchanges H04M3/00; arrangements for metering, time-control or time indication H04M15/00; prepayment telephone systems H04M17/00; accounting or billing for wireless communication networks H04W4/24) - 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 A method of constraint generating using large language models according to an embodiment includes configuring a first large language model of a knowledge base orchestrator based on a first master prompt, configuring a second large language model of a workplan builder orchestrator based on a second master prompt, providing a set of predefined constraint queries to the knowledge base orchestrator, generating a set of first responses to the set of predefined constraint queries based on a knowledge base and the first large language model, providing the set of predefined constraint queries and the respective first responses to the set of predefined constraint queries to the workplan builder orchestrator, generating a set of second responses to the set of predefined constraint queries based on the second large language model, and determining workplan constraints based on the set of second responses. ## Claims ### Claim 1 A method of constraint generation using large language models, the method comprising: configuring, by a computing system, a first large language model of a knowledge base orchestrator based on a first master prompt; configuring, by the computing system, a second large language model of a workplan builder orchestrator based on a second master prompt; providing, by the computing system, a set of predefined constraint queries to the knowledge base orchestrator; generating, by the computing system, a set of first responses to the set of predefined constraint queries based on a knowledge base and the first large language model; providing, by the computing system, the set of predefined constraint queries and the respective first responses to the set of predefined constraint queries to the workplan builder orchestrator; generating, by the computing system, a set of second responses to the set of predefined constraint queries based on the second large language model; and determining, by the computing system, workplan constraints based on the set of second responses. ### Claim 2 (depends on 1) The method of claim 1, further comprising generating the knowledge base from documentation received from an end user. ### Claim 3 (depends on 1) The method of claim 1, wherein generating the set of second responses comprises generating the set of second responses in a format defined by the second master prompt for the second large language model. ### Claim 4 (depends on 3) The method of claim 3, wherein the format comprises a JavaScript Object Notation (JSON) format. ### Claim 5 (depends on 1) The method of claim 1, further comprising executing, by the computing system, an application programming interface (API) call to store a workplan defined by the workplan constraints to a management system. ### Claim 6 (depends on 1) The method of claim 1, further comprising generating, by the computing system, an agent schedule based on the workplan constraints. ### Claim 7 (depends on 1) The method of claim 1, wherein the second master prompt requires responses to be one-word answers. ### Claim 8 A computing system for constraint generating using large language models, the system comprising: at least one processor; and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the computing system to: configure a first large language model of a knowledge base orchestrator based on a first master prompt; configure a second large language model of a workplan builder orchestrator based on a second master prompt; provide a set of predefined constraint queries to the knowledge base orchestrator; generate a set of first responses to the set of predefined constraint queries based on a knowledge base and the first large language model; provide the set of predefined constraint queries and the respective first responses to the set of predefined constraint queries to the workplan builder orchestrator; generate a set of second responses to the set of predefined constraint queries based on the second large language model; and determine workplan constraints based on the set of second responses. ### Claim 9 (depends on 8) The computing system of claim 8, wherein the plurality of instructions further causes the computing system to generate the knowledge base from documentation received from an end user. ### Claim 10 (depends on 8) The computing system of claim 8, wherein to generate the set of second responses comprises to generate the set of second responses in a format defined by the second master prompt for the second large language model. ### Claim 11 (depends on 10) The computing system of claim 10, wherein the format comprises a JavaScript Object Notation (JSON) format. ### Claim 12 (depends on 8) The computing system of claim 8, wherein the plurality of instructions further causes the computing system to execute an application programming interface (API) call to store a workplan defined by the workplan constraints to a management system. ### Claim 13 (depends on 8) The computing system of claim 8, wherein the plurality of instructions further causes the computing system to generate an agent schedule based on the workplan constraints. ### Claim 14 (depends on 8) The computing system of claim 8, wherein the second master prompt requires responses to be one-word answers. ### Claim 15 One or more non-transitory machine-readable storage media comprising a plurality of instructions stored thereon that, in response to execution by a computing system, causes the computing system to: configure a first large language model of a knowledge base orchestrator based on a first master prompt; configure a second large language model of a workplan builder orchestrator based on a second master prompt; provide a set of predefined constraint queries to the knowledge base orchestrator; generate a set of first responses to the set of predefined constraint queries based on a knowledge base and the first large language model; provide the set of predefined constraint queries and the respective first responses to the set of predefined constraint queries to the workplan builder orchestrator; generate a set of second responses to the set of predefined constraint queries based on the second large language model; and determine workplan constraints based on the set of second responses. ### Claim 16 (depends on 15) The one or more non-transitory machine-readable storage media of claim 15, wherein the plurality of instructions further causes the computing system to generate the knowledge base from documentation received from an end user. ### Claim 17 (depends on 15) The one or more non-transitory machine-readable storage media of claim 15, wherein to generate the set of second responses comprises to generate the set of second responses in a format defined by the second master prompt for the second large language model. ### Claim 18 (depends on 17) The one or more non-transitory machine-readable storage media of claim 17, wherein the format comprises a JavaScript Object Notation (JSON) format. ### Claim 19 (depends on 15) The one or more non-transitory machine-readable storage media of claim 15, wherein the plurality of instructions further causes the computing system to execute an application programming interface (API) call to store a workplan defined by the workplan constraints to a management system. ### Claim 20 (depends on 15) The one or more non-transitory machine-readable storage media of claim 15, wherein the second master prompt requires responses to be one-word answers.