# US 2025/0165745 A1 · System and methods for creation and use of meta-models in simulated environments

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- Number: US 2025/0165745 A1
- Document ID: US20250165745A1
- Status: Published application
- Kind: A1
- Application number: 19/031,006
- Filed: 2025-01-17
- Published: 2025-05-22
- Earliest claimed: 2015-10-28
- Assignees: Qomplx LLC
- Applicants: Qomplx LLC
- Inventors: Jason Crabtree; Andrew Sellers
- Web page: https://patentgrep.com/patent/US20250165745A1

## CPC

- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- G06F 8/30: Creation or generation of source code
- G06F 8/41: Compilation
- G06F 9/455: Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30)
- G06F 30/27: using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
- G06N 20/00: Machine learning
- G06Q 10/0637: Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
- G06Q 10/0674: {using process simulation}
- H04L 41/046: comprising network management agents or mobile agents therefor
- H04L 41/0813: characterised by the conditions triggering a change of settings
- H04L 41/145: {involving simulating, designing, planning or modelling of a network}
- H04L 41/40: using virtualisation of network functions or resources, e.g. SDN or NFV entities
- H04L 67/10: in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46)
- G06N 3/126: Evolutionary algorithms, e.g. genetic algorithms or genetic programming
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06Q 10/067: Enterprise or organisation modelling
- H04L 41/142: using statistical or mathematical methods
- H04L 41/147: for predicting network behaviour
- H04L 41/149: for prediction of maintenance
- H04L 43/0876: Network utilisation, e.g. volume of load or congestion level
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- 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
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

A system and method for generating and applying meta-models in simulated environments, in which an agent simulation is selected, one or more agent goals are received, and agents are created which are individual instances of the agent simulation with each agent having at least one of the agent goals, wherein the agents are used in the execution of an environment simulation which dynamically changes based on the collective behavior of the agents. The agents operate in the environment simulation using meta-models which describe how the agents interact with other agent and how the agents interact within the simulation.

## Claims

### Claim 1

A computer system comprising a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that:
obtains one or more models of an agent;
generates source code of the one or more models of an agent;
compiles the source code to provide compiled code;
reconfigures two or more compiled codes from at least two agents to provide a meta-model relating to a plurality of interactions between the at least two agents;
receives a simulation goal related to one or more agent goals;
selects a dynamic environment simulation based on the simulation goal;
executes the dynamic environment simulation using a plurality of meta-models; and
continues the execution of the dynamic environment simulation that evolves with agent behavior from the execution of the plurality of agents and the plurality of meta-models until the simulation goal has been reached or until each agent has achieved its agent goal from the one or more agent goals.

### Claim 2 (depends on 1)

The computer system of claim 1, wherein the meta-model describes relationships between distributed simulation models operating across multiple computing nodes in a network.

### Claim 3 (depends on 1)

The computer system of claim 1, wherein the computer system comprises a distributed computing network having a plurality of computing nodes, wherein different portions of the dynamic environment simulation are executed on different computing nodes.

### Claim 4 (depends on 3)

The computer system of claim 3, wherein the computing nodes communicate via standardized application programming interfaces to coordinate the execution of the dynamic environment simulation.

### Claim 5 (depends on 1)

The computer system of claim 1, wherein the meta-model enables integration of heterogeneous simulation engines without modification of the underlying simulation models.

### Claim 6 (depends on 1)

The computer system of claim 1, wherein the computer system maintains state information for the dynamic environment simulation across multiple distributed computing nodes using a distributed database.

### Claim 7 (depends on 1)

The computer system of claim 1, wherein the meta-model specifies relationships between containerized simulation components executing in a cloud computing environment.

### Claim 8 (depends on 1)

The computer system of claim 1, wherein the computer system implements load balancing by dynamically allocating simulation processing across multiple computing nodes based on computational requirements.

### Claim 9 (depends on 1)

The computer system of claim 1, wherein the meta-model enables asynchronous communication between simulation components using message queues.

### Claim 10 (depends on 1)

The computer system of claim 1, wherein the computer system implements fault tolerance by maintaining redundant copies of simulation state across multiple computing nodes.

### Claim 11

A computer-implemented method comprising the steps of:
obtaining one or more models of an agent;
generating source code of the one or more models of an agent;
compiling the source code to provide compiled code;
reconfiguring two or more compiled codes from at least two agents to provide a meta-model relating to a plurality of interactions between the at least two agents;
receiving a simulation goal related to one or more agent goals;
selecting a dynamic environment simulation based on the simulation goal;
executing the dynamic environment simulation using a plurality of meta-models; and
continuing the execution of the dynamic environment simulation that evolves with agent behavior from the execution of the plurality of agents and the plurality of meta-models until the simulation goal has been reached or until each agent has achieved its agent goal from the one or more agent goals.

### Claim 12 (depends on 11)

The computer-implemented method of claim 11, wherein the meta-model describes relationships between distributed simulation models operating across multiple computing nodes in a network.

### Claim 13 (depends on 11)

The computer-implemented method of claim 11, wherein the method is carried out using a distributed computing network having a plurality of computing nodes, wherein different portions of the dynamic environment simulation are executed on different computing nodes.

### Claim 14 (depends on 13)

The computer-implemented method of claim 13, wherein the computing nodes communicate via standardized application programming interfaces to coordinate the execution of the dynamic environment simulation.

### Claim 15 (depends on 11)

The computer-implemented method of claim 11, wherein the meta-model enables integration of heterogeneous simulation engines without modification of the underlying simulation models.

### Claim 16 (depends on 11)

The computer-implemented method of claim 11, wherein the state information is maintained for the dynamic environment simulation across multiple distributed computing nodes using a distributed database.

### Claim 17 (depends on 11)

The computer-implemented method of claim 11, wherein the meta-model specifies relationships between containerized simulation components executing in a cloud computing environment.

### Claim 18 (depends on 11)

The computer-implemented method of claim 11, wherein load balancing is performed by dynamically allocating simulation processing across multiple computing nodes based on computational requirements.

### Claim 19 (depends on 11)

The computer-implemented method of claim 11, wherein the meta-model enables asynchronous communication between simulation components using message queues.

### Claim 20 (depends on 11)

The computer-implemented method of claim 11, wherein the fault tolerance is provided by maintaining redundant copies of simulation state across multiple computing nodes.