# US 2026/0064902 A1 · Generative Artificial Intelligence System and Method of Operating the same > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0064902 A1 - Document ID: US20260064902A1 - Status: Published application - Kind: A1 - Application number: 18/773,333 - Filed: 2024-07-15 - Published: 2026-03-05 - Earliest claimed: 2023-07-13 - Assignees: Incucomm, Inc. - Applicants: Incucomm, Inc. - Inventors: Randal ALLEN; Eric HANEY; Russell Shayne WENZEL - Web page: https://patentgrep.com/patent/US20260064902A1 ## CPC - G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) - G05B 13/027: {using neural networks only} - G06F 17/11: for solving equations {, e.g. nonlinear equations, general mathematical optimization problems (optimization specially adapted for a specific administrative, business or logistic context G06Q10/04)} - G06F 30/15: Vehicle, aircraft or watercraft design - G06F 30/27: using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) ## Abstract A generative artificial intelligence system and method of operating the same to control complex systems. In one embodiment, the method includes incorporating measures of merit into system requirements for an aerospace vehicle system to provide aggregated system requirements and measures of merit, and receiving design parameters of the aerospace vehicle system represented as stochastic variables. The method also includes executing first order physics-based engineering equations of the design parameters with the generative artificial intelligence system on the processor to produce a design for an operation of the aerospace vehicle system to meet the aggregated system requirements and measures of merit in a single iteration improving computational efficiency and reducing power consumption of the processor operating the generative artificial intelligence system. ## Claims ### Claim 1 A method of operating a generative artificial intelligence system on a processor and memory, comprising: incorporating measures of merit into system requirements for an aerospace vehicle system to provide aggregated system requirements and measures of merit; receiving design parameters of said aerospace vehicle system represented as stochastic variables; and executing first order physics-based engineering equations of said design parameters with said generative artificial intelligence system on said processor to produce a design for an operation of said aerospace vehicle system to meet said aggregated system requirements and measures of merit in a single iteration improving computational efficiency and reducing power consumption of said processor operating said generative artificial intelligence system. ### Claim 2 (depends on 1) The method as recited in claim 1 wherein said executing first order physics-based engineering equations includes modulating said design parameters until a multi-dimensional combination thereof arrives at said design that optimizes said measures of merit of said aggregated system requirements and measures of merit for said aerospace vehicle system. ### Claim 3 (depends on 1) The method as recited in claim 1 wherein said design parameters include aerodynamics parameters, propulsion parameters, weight parameters and trajectory parameters of said aerospace vehicle system. ### Claim 4 (depends on 1) The method as recited in claim 1 wherein said measures of merit include survivability, cost, reliability and/or safety of said aerospace vehicle system. ### Claim 5 (depends on 1) The method as recited in claim 1 further comprising providing reported design parameters extracted automatically from said stochastic variables including a sensitivity analysis for said design parameters to identify performance drivers for said design of said aerospace vehicle system. ### Claim 6 (depends on 1) The method as recited in claim 1 wherein said first order physics-based engineering equations are discontinuous, non-convex, and non-differentiable system equations. ### Claim 7 (depends on 1) The method as recited in claim 1 wherein said first order physics-based engineering equations include interdependencies between said design parameters and an overall impact on said aggregated system requirements and measures of merit for said aerospace vehicle system. ### Claim 8 (depends on 1) The method as recited in claim 1 wherein said stochastic variables include stochastic ranges of said design parameters. ### Claim 9 A generative artificial intelligence system operative on a processor and memory configured to: incorporate measures of merit into system requirements for an aerospace vehicle system to provide aggregated system requirements and measures of merit; receive design parameters of said aerospace vehicle system represented as stochastic variables; and execute first order physics-based engineering equations of said design parameters with said generative artificial intelligence system on said processor to produce a design for an operation of said aerospace vehicle system to meet said aggregated system requirements and measures of merit in a single iteration improving computational efficiency and reducing power consumption of said processor operating said generative artificial intelligence system. ### Claim 10 (depends on 9) The generative artificial intelligence system as recited in claim 9 wherein said generative artificial intelligence system is configured to modulate said design parameters until a multi-dimensional combination thereof arrives at said design that optimizes said measures of merit of said aggregated system requirements and measures of merit for said aerospace vehicle system. ### Claim 11 (depends on 9) The generative artificial intelligence system as recited in claim 9 wherein said design parameters include aerodynamics parameters, propulsion parameters, weight parameters and trajectory parameters of said aerospace vehicle system. ### Claim 12 (depends on 9) The generative artificial intelligence system as recited in claim 9 wherein said measures of merit include survivability, cost, reliability and/or safety of said aerospace vehicle system. ### Claim 13 (depends on 9) The generative artificial intelligence system as recited in claim 9 wherein said generative artificial intelligence system is configured to provide reported design parameters extracted automatically from said stochastic variables including a sensitivity analysis for said design parameters to identify performance drivers for said design of said aerospace vehicle system. ### Claim 14 (depends on 9) The generative artificial intelligence system as recited in claim 9 wherein said first order physics-based engineering equations include interdependencies between said design parameters and an overall impact on said aggregated system requirements and measures of merit for said aerospace vehicle system. ### Claim 15 A computer program product comprising program code stored in a non-transitory computer readable medium operable on a computer with a processor and memory for executing a generative artificial intelligence system and configured to: incorporate measures of merit into system requirements for an aerospace vehicle system to provide aggregated system requirements and measures of merit; receive design parameters of said aerospace vehicle system represented as stochastic variables; and execute first order physics-based engineering equations of said design parameters with said generative artificial intelligence system on said processor to produce a design for an operation of said aerospace vehicle system to meet said aggregated system requirements and measures of merit in a single iteration improving computational efficiency and reducing power consumption of said processor operating said generative artificial intelligence system. ### Claim 16 (depends on 15) The computer program product as recited in claim 15 wherein said computer program product for executing said generative artificial intelligence system is configured to modulate said design parameters until a multi-dimensional combination thereof arrives at said design that optimizes said measures of merit of said aggregated system requirements and measures of merit for said aerospace vehicle system. ### Claim 17 (depends on 15) The computer program product as recited in claim 15 wherein said design parameters include aerodynamics parameters, propulsion parameters, weight parameters and trajectory parameters of said aerospace vehicle system. ### Claim 18 (depends on 15) The computer program product as recited in claim 15 wherein said measures of merit include survivability, cost, reliability and/or safety of said aerospace vehicle system. ### Claim 19 (depends on 15) The computer program product as recited in claim 15 wherein said computer program product for executing said generative artificial intelligence system is configured to provide reported design parameters extracted automatically from said stochastic variables including a sensitivity analysis for said design parameters to identify performance drivers for said design of said aerospace vehicle system. ### Claim 20 (depends on 15) The computer program product as recited in claim 15 wherein said first order physics-based engineering equations include interdependencies between said design parameters and an overall impact on said aggregated system requirements and measures of merit for said aerospace vehicle system.