system

Abstract

We provide the system. [Solution] A means of generating multiple intelligent agents within a virtual environment and giving each agent different characteristics, A means for having an intelligent agent perform a task within the virtual environment and for evaluating its performance, Based on the evaluation results, a means of inheriting and evolving the characteristics of superior intelligent agents to the next generation using a genetic algorithm, A means of providing an interface for users to adjust the parameters of an evolutionary algorithm, A means for visualizing the performance of each generation of intelligent agents during the evolutionary process and displaying the evaluation results, A means of simulating traffic patterns in urban environments and providing users with optimization suggestions, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, in the field of artificial intelligence research, the importance of systems that autonomously learn and evolve has been increasing. In particular, technologies for effectively evolving a plurality of intelligent agents through simulations in a virtual environment have various applications, but means for efficiently performing this are still insufficient. In addition, an interface that enables a user to intuitively understand and adjust such an evolution process is required. Furthermore, there is a need for an environment in which researchers and developers can quickly adopt effective models by developing means for visualizing and evaluating the performance of the generated agents.

Means for Solving the Problems

[0005] This invention first generates multiple intelligent agents within a virtual environment, each with different characteristics, to engage in competition and task performance. During this process, the performance of each agent is evaluated, and a genetic algorithm is used to pass on the characteristics of superior agents to the next generation. The system provides an interface that allows the user to adjust key parameters of the evolutionary algorithm, and further presents the evolutionary process and evaluation results in a visualized form, enabling easy understanding and improvement of agent performance. Furthermore, it is possible to construct virtual environments by setting multiple challenges and environmental conditions based on user requirements, thereby flexibly executing a variety of simulations.

[0006] A "virtual environment" is a type of simulation space built within a computer, where specific situations or challenges are reproduced based on conditions selected or designed by the user.

[0007] An "intelligent agent" is a program designed to possess specific characteristics and abilities, to act autonomously within a virtual environment, and to perform tasks.

[0008] "Traits" refer to the abilities and behavioral characteristics of an intelligent agent, and include elements such as speed, visual range, and endurance.

[0009] A "task" refers to an activity or operation that an intelligent agent intends to perform within a virtual environment, and is the behavior that serves as the basis for evaluation.

[0010] "Performance" is an indicator that shows the degree of efficiency and results when an intelligent agent performs a task, and it forms the basis for evaluation.

[0011] A "genetic algorithm" is a computational method designed based on the evolutionary process of organisms, and it is a method that promotes the evolution of the entire population by passing on information about individuals with superior characteristics to the next generation.

[0012] An "interface" is a point of contact provided to a system for the user to interact with it and perform operations and settings, and it includes visual or operational elements.

[0013] "Visualization" is a technique for displaying numerical or abstract information graphically or in charts, helping users intuitively understand the information.

[0014] A "parameter" is a numerical or conditional variable used to adjust the settings of a system or computational model.

[0015] "Evaluation results" refer to the analysis of performance and results obtained based on the performance of intelligent agents, and serve as feedback information that is useful for improvement and evolution in the next step. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be described.

[0019] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention involves running a virtual environment simulation on a server. First, the server constructs a virtual environment and sets various challenges and environmental parameters based on user selections. This allows for the reproduction of diverse scenarios, such as environments with many obstacles or environments with limited resources. Within this environment, the server randomly generates initial intelligent agents, each assigned different characteristics.

[0038] The server initiates a simulation in which agents autonomously act and perform tasks within a virtual environment. The agents utilize their given characteristics to execute the tasks, and the server evaluates each agent's performance. This evaluation includes factors such as resource gathering efficiency and the calculation of the shortest travel path.

[0039] Based on the evaluation results, the server applies a genetic algorithm to pass on information about agents with superior traits to the next generation. New agents are generated through crossover and mutation, and the evolutionary process is repeated. The server provides an interface for users to control these evolutionary processes, allowing users to freely adjust the evolutionary parameters.

[0040] The terminal visually displays the progress and results of the simulation based on evolutionary data received from the server. Users can use this information to observe agent performance and environmental conditions, and use it to improve the next simulation. Specifically, by focusing on a particular agent and analyzing its characteristics and behavioral patterns, it is possible to build a more efficient model.

[0041] In this way, the system of the present invention provides an environment that effectively supports the evolution of intelligent agents through the cooperation of servers, terminals, and users. Diverse simulations within the virtual environment facilitate the development of agents capable of advanced decision-making and problem-solving.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server initializes the virtual environment. Based on the user-selected task and environment parameters, it sets the size, structure, and resource allocation of the virtual environment. It also sends information related to the initial state of the environment to the terminal.

[0045] Step 2:

[0046] The server randomly generates multiple intelligent agents. Each agent is assigned different characteristics and abilities. For example, characteristics such as visual range, movement speed, and durability are randomly assigned. Information about the generated agents is sent to the terminal.

[0047] Step 3:

[0048] The terminal visually displays the agent on the user interface based on the agent information received from the server. The user then checks the agent's initial state.

[0049] Step 4:

[0050] The server initiates a simulation in which agents perform tasks within a virtual environment. Here, agents carry out pre-configured tasks such as resource exploration and competition with other agents.

[0051] Step 5:

[0052] The server monitors the running simulations and collects performance data for each agent. This includes task completion, resource gathering, and action time.

[0053] Step 6:

[0054] The server applies a genetic algorithm to generate new agents that enhance the characteristics of high-performing agents and pass them on to the next generation. Crossover and mutation techniques are used to ensure diversity.

[0055] Step 7:

[0056] The device displays a visualization of the simulation results and evolutionary process to the user. The user then refers to this and makes the necessary adjustments for the next evolutionary cycle.

[0057] Step 8:

[0058] Users adjust the parameters of the genetic algorithm via a terminal interface. By setting parameters such as the rate of evolution and the frequency of mutations, they can influence the results of the next simulation.

[0059] Step 9:

[0060] The server restarts the process from step 1 again, based on the new parameters that reflect the user's input, aiming for the evolution of a more advanced agent.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] In a virtual environment, there is a need to provide a system for the efficient evolution of intelligent agents with diverse characteristics, as well as for their evaluation and adjustment. Current systems make it difficult to manually control the agent generation and evolution process, and lack effective means of utilizing evaluation results. Therefore, there is a need to develop a system that users can intuitively operate and that allows for improved agent performance.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for generating a virtual environment, means for creating multiple intelligent agents and assigning different attributes to each agent, and means for inheriting and developing the agent attributes to the next generation using an evolutionary algorithm. This makes it possible to set various tasks based on user instructions and to visualize and optimize the behavior of intelligent agents.

[0066] A "computing device" is a device used for processing and performing calculations on data, and generally includes servers and personal computers.

[0067] A "virtual environment" is a simulated environment built within a computer that can mimic the physical laws and conditions of the real world.

[0068] An "intelligent agent" is a program entity that acts autonomously within a virtual environment and has the ability to perform specific tasks.

[0069] "Attributes" are characteristics or parameters assigned to an intelligent agent, such as movement speed and field of view.

[0070] An "evolutionary algorithm" is a mathematical method for improving the attributes of an agent based on evaluation results and passing them on to the next generation of agents.

[0071] A "user" is the entity that uses the system to configure the virtual environment and manage agents.

[0072] The "display screen" is a visual interface that allows users to adjust the settings of the evolutionary algorithm and check the simulation results.

[0073] "Visualization" is a technique that visually displays data and processes, enabling users to intuitively understand the information.

[0074] In embodiments of the present invention, the server functions as a computing device for generating a virtual environment. The server uses simulation software (such as Unity or Unreal Engine) to set physical laws and conditions within the virtual environment. Based on user instructions, the server generates multiple intelligent agents and assigns different attributes to each agent. As a result, the agents have the ability to act autonomously and perform specific tasks.

[0075] The server uses an evolutionary algorithm to pass on agent attributes to the next generation, aiming for efficient evolution. Evaluation metrics include resource gathering efficiency and optimization of movement paths, and the server evaluates the agent's performance in real time based on these metrics.

[0076] The terminal displays a simulation to the user using 3D visualization technology, based on evolutionary data received from the server. Through this, the user can observe and analyze the agent's behavior and environmental changes, and adjust the evolution settings.

[0077] As a concrete example, we can cite a simulation in which the user sets up an environment with limited resources, and the agent discovers a strategy to gather those resources in the shortest amount of time. Furthermore, the prompt input to the generated AI model would be: "Explain the process by which the agent uses evolutionary simulation to find an efficient resource gathering strategy within a virtual environment with limited resources."

[0078] This allows users to observe the behavior of intelligent agents and gain valuable insights to build more efficient strategies.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The server constructs a virtual environment. It receives user-specified environment parameters (size, obstacle placement, resource distribution, etc.) as input and reproduces them using simulation software. The output is a simulable virtual environment. During this process, physical laws and conditions within the environment are established, influencing the agent's behavior in subsequent simulations.

[0082] Step 2:

[0083] The server generates intelligent agents. As input, it receives the number of agents and their initial characteristics (such as movement speed, field of view, and resource gathering ability) specified by the user. Based on this information, the server generates agents incorporating random elements and provides data with a unique ID and attributes assigned to each agent as output. This step confirms that the agents exhibit diverse behavioral patterns.

[0084] Step 3:

[0085] The server starts simulating the agents. It receives virtual environment data and agent data as input, and the agents autonomously begin their actions. The server tracks each agent's movement and resource gathering actions in real time, generating action logs and data as output. Next, it evaluates these actions and collects data on performance metrics (e.g., resource gathering efficiency).

[0086] Step 4:

[0087] The server evaluates the agent's performance and applies an evolutionary algorithm. It receives agent behavior logs and performance data as input. Based on this information, the server executes a genetic algorithm to pass on the traits of superior agents to the next generation, generating information about the newly evolved agent as output. This step creates stronger agents through crossover and mutation.

[0088] Step 5:

[0089] The terminal visually displays the evolutionary process based on simulation data received from the server. It receives evolutionary data from the server as input and processes it to display it in an easy-to-understand manner for the user. As output, it renders the agent's behavior and evolutionary process as 3D graphics, allowing the user to observe it in real time.

[0090] Step 6:

[0091] The user observes the simulation results via a terminal and analyzes the agent's behavior. Based on the displayed data, the user adjusts the conditions for the next simulation. Specific input actions include setting new parameters via the user interface (e.g., increasing or decreasing the number of agents or changing their characteristics) and sending the results to the server. The output is the preparation of configuration information for the next simulation.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] This invention aims to optimize urban transportation in the context of smart cities. In modern urban environments, inefficient traffic flow due to congestion and accidents is frequent, which not only makes it difficult for citizens to get around but also increases the environmental burden. To solve these problems, a new system is needed that analyzes and optimizes traffic patterns in real time.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes means for generating multiple intelligent agents within a virtual environment and assigning different characteristics to each agent; means for having the intelligent agents perform tasks within the virtual environment and evaluating their performance; and means for using a genetic algorithm to inherit and evolve the characteristics of superior intelligent agents to the next generation based on the evaluation results. This makes it possible to propose efficient traffic flow through the simulation of traffic patterns in a smart city environment.

[0097] A "virtual environment" is an artificial environment created using digital technology that is different from physical reality.

[0098] An "intelligent agent" is a program or software that autonomously performs a specific task and is capable of learning and evolving based on the results.

[0099] "Traits" refer to the unique abilities and behavioral patterns that intelligent agents possess, and are factors that influence task performance.

[0100] A "genetic algorithm" is a computational method that mimics the evolutionary process of organisms and uses crossover and mutation to find the optimal solution by manipulating the characteristics of individual organisms.

[0101] An "interface" is a point of contact or screen that allows a user to access and manipulate the functions and data of a system.

[0102] "Traffic patterns" refer to the flow and dynamics of traffic within an urban environment, and include trends in the movement routes and speeds of vehicles and pedestrians.

[0103] To implement this invention, it is necessary to build a system that simulates a virtual environment and optimizes urban traffic using intelligent agents. The server generates the virtual environment and creates numerous intelligent agents. The characteristics given to these agents are random, but they can also be changed according to specific criteria.

[0104] The server sets tasks for agents to autonomously analyze traffic patterns and find the optimal travel route. Agent performance is evaluated based on resource utilization efficiency and route optimization over short periods. Based on these evaluation results, a genetic algorithm is applied to select agents with superior traits and pass those traits on to the next generation.

[0105] Users can participate in the simulation process through an interface that allows them to adjust the input parameters of the evolutionary algorithm. Furthermore, the user's terminal visually displays the agent's evolutionary process and evaluation results based on data transmitted from the server. This allows users to closely observe the behavior and characteristics of specific agents and provide feedback to improve system performance.

[0106] The realization of this system fundamentally involves building a 3D environment using Unity and implementing an intelligent agent learning algorithm using Python AI libraries (e.g., TENSORFLOW®, Keras). This will enable the simulation of traffic flow within a complex road network.

[0107] As a concrete example, we can cite a system that simulates traffic patterns during large-scale events in cities and proposes the optimal transportation route to the user. In this way, it is possible to efficiently help solve urban traffic congestion. Another example of a prompt message to be input to the generating AI model is a request such as, "Please propose the optimal route to alleviate congestion caused by a large-scale event being held in a smart city environment."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server initializes the virtual environment and generates intelligent agents with random characteristics. The input for this step is the basic configuration information of the virtual environment, and the output is multiple agents placed within the virtual environment. Based on this information, the server constructs a 3D environment in Unity and places the agents.

[0111] Step 2:

[0112] The server assigns tasks to agents and initiates actions to optimize traffic patterns. The input is a simulated traffic scenario, and the output is the agent's movement pattern and collected environmental data. The agents operate autonomously using Python's AI library to analyze the specified traffic patterns.

[0113] Step 3:

[0114] The server evaluates the performance of the agents and determines their relative strengths and weaknesses. The input consists of agent activity logs and their results, while the output is each agent's performance score. The server processes the data using criteria such as resource utilization efficiency in a short time and the degree of reduction in travel distance.

[0115] Step 4:

[0116] A genetic algorithm is applied, and the server processes the data to pass on the traits of superior agents to the next generation. The input is the performance score obtained in step 3, and the output is the traits of the next generation agents. Crossover and mutation operations are performed to generate a new set.

[0117] Step 5:

[0118] The simulation is tuned through an interface that allows the user to adjust the settings of the evolutionary algorithm. The input is the user's tuning parameters, and the output is the new simulation conditions based on those settings. The user can intuitively change the settings using the GUI.

[0119] Step 6:

[0120] The terminal visualizes data sent from the server and displays the agent's evolution process and evaluation results. The input is simulation data from the server, and the output is graphics information displayed on the terminal. The terminal analyzes the data and presents the results in a user-friendly format using a visualization library.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention provides a more adaptive interface and evolution system by considering the user's emotional state during the process of evolving intelligent agents within a virtual environment. The server first generates a virtual environment and then randomly generates intelligent agents. Each agent has different characteristics and acts autonomously within the user-defined environment to perform tasks.

[0123] In this process, the server uses an emotion engine to recognize the user's emotional state in real time. Through facial recognition and voice analysis of the user, it identifies emotions such as joy, surprise, and concentration, and uses this information to dynamically adjust the agent's behavior and the parameters of its evolutionary algorithm.

[0124] For example, if a user expresses dissatisfaction with an agent's behavior, the server receives this emotional information and either enhances the characteristics of that particular agent or applies new parameters to improve its behavior in the next evolutionary cycle. This allows the agent's evolution process to directly reflect user feedback, enabling it to achieve higher performance.

[0125] The terminal visually provides the user with simulation results and evolutionary processes from the server, and also provides feedback that the user's emotions are being recognized. For example, if the user shows interest in a particular scenario or agent, the simulation scenario is adjusted and the agent's behavior changes are visualized in detail.

[0126] Through the device interface, users can turn the automatic adjustment function based on emotional information on or off while maintaining the basic settings of the evolutionary algorithm. This allows users to leverage improvements derived from personal feedback, enabling more real-time interaction and personalized agent development.

[0127] As a result, the present invention provides a flexible and efficient evolutionary process for intelligent agents through the introduction of emotional information, which can facilitate more appropriate decision-making and detailed scenario formation.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server initializes the virtual environment. Based on the user-selected tasks and environment parameters, it sets the size and conditions of the environment. It also determines the basic constraints on which agents can operate.

[0131] Step 2:

[0132] The server randomly generates multiple intelligent agents. Each agent is assigned random characteristics and placed within a user-defined virtual environment. These characteristics include things like visual range and movement speed.

[0133] Step 3:

[0134] The terminal visually displays the virtual environment and agent information received from the server on the user interface. Through this, the user can see the initial simulation status.

[0135] Step 4:

[0136] The server activates an emotion engine and recognizes emotions in real time through the user's camera input and voice. This identifies the user's emotions, such as happiness, dissatisfaction, and level of interest.

[0137] Step 5:

[0138] The server starts the simulation and has the agent perform the configured task. Meanwhile, it monitors the user's emotional information and adjusts the agent's behavior and task progress as needed.

[0139] Step 6:

[0140] The server collects agent performance data and analyzes it along with user emotion recognition data. For example, if the user is happy while the agent is working efficiently, the server applies a genetic algorithm to enhance that behavior.

[0141] Step 7:

[0142] The device presents the user with visual data based on simulation results and evolutionary processes. This allows the user to intuitively understand the agent's evolutionary process.

[0143] Step 8:

[0144] Users can control the direction of evolution by manually adjusting evolutionary parameters and simulation settings using the device's interface, or by turning the automatic adjustment function provided by the emotion engine on or off.

[0145] Step 9:

[0146] The server incorporates user feedback and adjustments, preparing for the next evolutionary cycle. This aims to generate more advanced and user-adaptive intelligent agents.

[0147] (Example 2)

[0148] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0149] The problem this invention aims to solve is to achieve more adaptive and advanced agent performance by enabling dynamic adjustments that appropriately reflect user emotional information during the evolution process of artificial intelligence agents in virtual space. Furthermore, since conventional methods have the problem of not being able to adequately reflect user feedback, this invention aims to improve real-time interaction.

[0150] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0151] In this invention, the server includes means for generating multiple artificial intelligence agents in a virtual space and assigning different attributes to each agent; means for recognizing the user's emotional information in real time and dynamically adjusting the agent's behavior and evolution algorithm settings; and means for providing an operation screen for the user to adjust the evolution algorithm settings. This makes it possible to realize more real-time and personalized agent evolution utilizing the user's emotional information.

[0152] An "artificial intelligence agent" is a program that autonomously performs specific tasks in a virtual space and continuously improves its performance based on evolutionary algorithms.

[0153] An "evolutionary algorithm" is a computational method inspired by natural selection and genetics, used to adaptively inherit and improve the characteristics of artificial intelligence agents.

[0154] "Emotional information" refers to the real-time emotional state obtained from the user's facial recognition, voice analysis, etc., and is used to dynamically adjust the behavior of artificial intelligence agents.

[0155] A "virtual space" is an artificial environment created on a computer system, which serves as the stage for artificial intelligence agents to operate.

[0156] The "operation screen" refers to the interface that users use to adjust system settings and agent evolution algorithms, and is a screen that provides visual information.

[0157] This invention constitutes a system related to the evolution of artificial intelligence agents in a virtual space. The server constructs a virtual space using a generative AI model and randomly generates multiple artificial intelligence agents within it. These agents are configured to have different attributes and operate autonomously within an environment set by the user.

[0158] The server uses facial recognition software and voice analysis tools to recognize user emotional information in real time. Methods such as OpenCV and speech recognition APIs are used for this purpose. User emotional information (e.g., joy, surprise, concentration) is important for dynamically adjusting the agent's behavior and evolutionary algorithm settings.

[0159] The terminal visually presents the user with simulation results from the server and the agent's evolution process. Available 3D graphics libraries (e.g., Unity) animate the agent's activities to make it easier for the user to understand the changes. It also includes a feature that allows the user to provide feedback on how their emotions are perceived and how they affect the agent.

[0160] Users can adjust the settings of the evolutionary algorithm through the device's interface and turn on or off features that utilize emotional information. This allows users to incorporate their individual feedback into system improvements.

[0161] As a concrete example, consider a scenario where a user utilizes this system on an online education platform. When faced with a difficult problem, the system recognizes the user's confusion through facial expression analysis. The server then adjusts the educational agent's explanation method, evolving the agent to provide clearer hints.

[0162] An example of a prompt is, "Create a program that evolves the agent's optimal response method based on the user's sentiment information." Using this prompt, the system can provide an agent that is more tailored to specific user needs.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The server first generates a virtual space. This virtual space is dynamically constructed using a generative AI model. It receives user environment configuration data as input and generates elements of the virtual space (terrain, weather, object placement, etc.) based on this data. The output is the generated virtual environment information, which is passed on to the next agent generation step.

[0166] Step 2:

[0167] The server generates artificial intelligence agents within a virtual space. The inputs used are the virtual environment information generated in step 1 and the agent's initial configuration data (e.g., characteristics and behavioral patterns). Based on this, each agent is configured to have different characteristics. The output is the generated data for the initialized agents, enabling them to operate in the virtual space. Specifically, the agents prepare to begin executing tasks.

[0168] Step 3:

[0169] The server recognizes user emotions in real time. It takes user facial recognition data and voice data as input, analyzes them with an emotion engine, and identifies emotional states such as joy, surprise, and concentration. Image processing libraries and voice analysis APIs are used to process this data. The output is the recognized user emotion information, which is used to adjust the agent's behavior.

[0170] Step 4:

[0171] The server dynamically adjusts the agent's behavior and evolution algorithm settings based on the user's sentiment information. Inputs include the sentiment information obtained in step 3 and the current agent performance data. Through data calculations, the agent's characteristics and behavioral parameters are updated. The output of the adjusted settings is applied to the next evolutionary cycle and execution. Specifically, the agent changes to behave in a way that better aligns with the user's expectations.

[0172] Step 5:

[0173] The terminal visually presents the user with simulation results and agent evolution status received from the server. It receives agent activity data and evolution parameters from the server as input. Based on this data, an animation is generated using a 3D graphics library and presented to the user. The output is visualized information, allowing the user to understand the progress of the simulation.

[0174] Step 6:

[0175] The user changes system settings through the terminal's interface. This step involves reviewing the basic settings of the evolutionary algorithm and selecting whether to use emotional information (on or off). The user's input, configured via the terminal, is then incorporated. This adjusts the system's behavior, enabling the development of more personalized agents. The output consists of settings based on the user's selections, which are then applied to the next cycle.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] In today's online shopping environment, providing personalized experiences that appropriately reflect the user's emotional state is challenging. Furthermore, in the evolution process of intelligent agents, directly incorporating user feedback is necessary to promote the development of more flexible and efficient intelligent agents.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes means for generating multiple intelligent programs in a virtual space and giving each program different properties; means for having the intelligent programs perform tasks in the virtual space and evaluating the results; means for inheriting and evolving the properties of superior intelligent programs to the next generation using a genetic method based on the evaluation results; means for analyzing the user's mental state and dynamically adjusting the operation of the intelligent programs based on that information; and means for adjusting product recommendations and displays in a virtual store based on the user's mental state. This enables a personalized shopping experience that takes into account the user's emotional state, and makes it possible to realize a more high-performance intelligent agent by utilizing user feedback in the agent's evolution process.

[0181] A "virtual space" is an artificial three-dimensional space created using computer technology, an environment in which users can interact.

[0182] An "intelligent program" is a program designed to solve a specific problem, which operates autonomously and produces results.

[0183] "Nature" refers to the specific characteristics or abilities that an intelligent program possesses, which influence its operation and results.

[0184] "Means of evaluating results" refer to methods for quantitatively or qualitatively measuring the outcomes achieved by an intelligent program and determining its effectiveness.

[0185] A "genetic method" is an algorithm based on the theory of biological evolution, used to select programs with superior traits, pass them on to the next generation, and improve them.

[0186] "Means of analyzing mental state" refers to techniques that analyze emotional indicators such as the user's facial expressions and voice to recognize their psychological state.

[0187] "Dynamic adjustment methods" refer to methods of adaptively changing the behavior and display of a program based on real-time changing circumstances and data.

[0188] "Means of adjusting product recommendations and displays" refers to methods of changing product displays and presentations to provide optimal information according to the user's situation and interests.

[0189] This system provides a personalized user experience through the evolutionary process of intelligent programs operating in a virtual space. The server generates the virtual space and randomly places multiple intelligent programs within it. Each program has different properties and autonomously performs tasks under conditions set by the user.

[0190] The server uses software such as OpenCV and emotion recognition libraries to acquire video and audio from the user's terminal, analyze the data, and recognize the user's mental state in real time. Based on this information, it dynamically adjusts the operation of intelligent programs and the parameters of evolutionary algorithms. For example, if the user shows interest, the products suggested in the virtual store can be adjusted according to that emotion.

[0191] The terminal visually presents the user with the simulation results and evolutionary process transmitted from the server. Furthermore, users can customize the numerical values ​​of the evolutionary method through their interface, thereby further enhancing their individual experience.

[0192] As a concrete example, let's say a user visits a virtual store using their smartphone. In this case, the smartphone's camera captures the user's facial expressions, and an emotion recognition library analyzes this data to recognize feelings of joy or interest. Based on these results, the server can recommend the latest fashion items or other products that might interest the user. An example of a prompt might be, "If the user is happy, recommend the latest fashion trend items."

[0193] In this way, this system uses emotional information to improve the user experience and effectively promotes the evolution of intelligent programs.

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The server generates a virtual space and randomly places multiple intelligent programs within it. The input is user-defined environmental conditions, and the output is the initial state of the virtual space. Random properties are assigned to the intelligent programs, and the simulation begins in the virtual space.

[0197] Step 2:

[0198] The user's device captures facial expressions and audio data using its built-in camera and microphone. The input data consists of raw video and audio, while the output is in a format that can be processed. Specifically, the camera acquires video and the microphone collects audio.

[0199] Step 3:

[0200] The server uses an emotion recognition library to analyze the user's mental state from facial expressions and audio data. The input is the video and audio data acquired in step 2, and the output is the user's emotion information. The analysis program performs facial expression template matching and audio tone analysis.

[0201] Step 4:

[0202] The server dynamically adjusts the behavior of the intelligent program and the parameters of its evolutionary algorithm based on the analyzed emotional information. The input is the emotional information obtained in step 3, and the output is the adjusted program's behavioral guidelines. Specifically, the program's behavioral parameters are changed in real time.

[0203] Step 5:

[0204] The terminal visually presents the adjusted simulation results to the user. The input is the result of the intelligent program's operation adjusted in step 4, and the output is the user's visual feedback. Specifically, the latest simulation status is displayed on the screen.

[0205] Step 6:

[0206] Users can customize the numerical values ​​of the evolutionary method through the interface provided by the device. Input is a request for setting changes made by the user, and output is the updated parameters of the evolutionary algorithm. Users adjust the evolutionary parameters by operating the touchscreen or buttons.

[0207] Step 7:

[0208] The server repeats the above process, generating simulation results of the evolved intelligent program. Using the AI ​​model, it creates prompt messages and, if the user is pleased, presents information to the user, for example, "Please recommend the latest fashion trend items."

[0209] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0210] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0211] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0215] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0216] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0217] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0218] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0219] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0220] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0221] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0222] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0223] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0224] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0225] This invention involves running a virtual environment simulation on a server. First, the server constructs a virtual environment and sets various challenges and environmental parameters based on user selections. This allows for the reproduction of diverse scenarios, such as environments with many obstacles or environments with limited resources. Within this environment, the server randomly generates initial intelligent agents, each assigned different characteristics.

[0226] The server initiates a simulation in which agents autonomously act and perform tasks within a virtual environment. The agents utilize their given characteristics to execute the tasks, and the server evaluates each agent's performance. This evaluation includes factors such as resource gathering efficiency and the calculation of the shortest travel path.

[0227] Based on the evaluation results, the server applies a genetic algorithm to pass on information about agents with superior traits to the next generation. New agents are generated through crossover and mutation, and the evolutionary process is repeated. The server provides an interface for users to control these evolutionary processes, allowing users to freely adjust the evolutionary parameters.

[0228] The terminal visually displays the progress and results of the simulation based on evolutionary data received from the server. Users can use this information to observe agent performance and environmental conditions, and use it to improve the next simulation. Specifically, by focusing on a particular agent and analyzing its characteristics and behavioral patterns, it is possible to build a more efficient model.

[0229] In this way, the system of the present invention provides an environment that effectively supports the evolution of intelligent agents through the cooperation of servers, terminals, and users. Diverse simulations within the virtual environment facilitate the development of agents capable of advanced decision-making and problem-solving.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The server initializes the virtual environment. Based on the user-selected task and environment parameters, it sets the size, structure, and resource allocation of the virtual environment. It also sends information related to the initial state of the environment to the terminal.

[0233] Step 2:

[0234] The server randomly generates multiple intelligent agents. Each agent is assigned different characteristics and abilities. For example, characteristics such as visual range, movement speed, and durability are randomly assigned. Information about the generated agents is sent to the terminal.

[0235] Step 3:

[0236] The terminal visually displays the agent on the user interface based on the agent information received from the server. The user then checks the agent's initial state.

[0237] Step 4:

[0238] The server initiates a simulation in which agents perform tasks within a virtual environment. Here, agents carry out pre-configured tasks such as resource exploration and competition with other agents.

[0239] Step 5:

[0240] The server monitors the running simulations and collects performance data for each agent. This includes task completion, resource gathering, and action time.

[0241] Step 6:

[0242] The server applies a genetic algorithm to generate new agents that enhance the characteristics of high-performing agents and pass them on to the next generation. Crossover and mutation techniques are used to ensure diversity.

[0243] Step 7:

[0244] The device displays a visualization of the simulation results and evolutionary process to the user. The user then refers to this and makes the necessary adjustments for the next evolutionary cycle.

[0245] Step 8:

[0246] Users adjust the parameters of the genetic algorithm via a terminal interface. By setting parameters such as the rate of evolution and the frequency of mutations, they can influence the results of the next simulation.

[0247] Step 9:

[0248] The server restarts the process from step 1 again, based on the new parameters that reflect the user's input, aiming for the evolution of a more advanced agent.

[0249] (Example 1)

[0250] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0251] In a virtual environment, there is a need to provide a system for the efficient evolution of intelligent agents with diverse characteristics, as well as for their evaluation and adjustment. Current systems make it difficult to manually control the agent generation and evolution process, and lack effective means of utilizing evaluation results. Therefore, there is a need to develop a system that users can intuitively operate and that allows for improved agent performance.

[0252] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0253] In this invention, the server includes means for generating a virtual environment, means for creating multiple intelligent agents and assigning different attributes to each agent, and means for inheriting and developing the agent attributes to the next generation using an evolutionary algorithm. This makes it possible to set various tasks based on user instructions and to visualize and optimize the behavior of intelligent agents.

[0254] A "computing device" is a device used for processing and performing calculations on data, and generally includes servers and personal computers.

[0255] A "virtual environment" is a simulated environment built within a computer that can mimic the physical laws and conditions of the real world.

[0256] An "intelligent agent" is a program entity that acts autonomously within a virtual environment and has the ability to perform specific tasks.

[0257] "Attributes" are characteristics or parameters assigned to an intelligent agent, such as movement speed and field of view.

[0258] An "evolutionary algorithm" is a mathematical method for improving the attributes of an agent based on evaluation results and passing them on to the next generation of agents.

[0259] A "user" is the entity that uses the system to configure the virtual environment and manage agents.

[0260] The "display screen" is a visual interface that allows users to adjust the settings of the evolutionary algorithm and check the simulation results.

[0261] "Visualization" is a technique that visually displays data and processes, enabling users to intuitively understand the information.

[0262] In embodiments of the present invention, the server functions as a computing device for generating a virtual environment. The server uses simulation software (such as Unity or Unreal Engine) to set physical laws and conditions within the virtual environment. Based on user instructions, the server generates multiple intelligent agents and assigns different attributes to each agent. As a result, the agents have the ability to act autonomously and perform specific tasks.

[0263] The server uses an evolutionary algorithm to pass on agent attributes to the next generation, aiming for efficient evolution. Evaluation metrics include resource gathering efficiency and optimization of movement paths, and the server evaluates the agent's performance in real time based on these metrics.

[0264] The terminal displays a simulation to the user using 3D visualization technology, based on evolutionary data received from the server. Through this, the user can observe and analyze the agent's behavior and environmental changes, and adjust the evolution settings.

[0265] As a concrete example, we can cite a simulation in which the user sets up an environment with limited resources, and the agent discovers a strategy to gather those resources in the shortest amount of time. Furthermore, the prompt input to the generated AI model would be: "Explain the process by which the agent uses evolutionary simulation to find an efficient resource gathering strategy within a virtual environment with limited resources."

[0266] This allows users to observe the behavior of intelligent agents and gain valuable insights to build more efficient strategies.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The server constructs a virtual environment. It receives user-specified environment parameters (size, obstacle placement, resource distribution, etc.) as input and reproduces them using simulation software. The output is a simulable virtual environment. During this process, physical laws and conditions within the environment are established, influencing the agent's behavior in subsequent simulations.

[0270] Step 2:

[0271] The server generates intelligent agents. As input, it receives the number of agents and their initial characteristics (such as movement speed, field of view, and resource gathering ability) specified by the user. Based on this information, the server generates agents incorporating random elements and provides data with a unique ID and attributes assigned to each agent as output. This step confirms that the agents exhibit diverse behavioral patterns.

[0272] Step 3:

[0273] The server starts simulating the agents. It receives virtual environment data and agent data as input, and the agents autonomously begin their actions. The server tracks each agent's movement and resource gathering actions in real time, generating action logs and data as output. Next, it evaluates these actions and collects data on performance metrics (e.g., resource gathering efficiency).

[0274] Step 4:

[0275] The server evaluates the agent's performance and applies an evolutionary algorithm. It receives agent behavior logs and performance data as input. Based on this information, the server executes a genetic algorithm to pass on the traits of superior agents to the next generation, generating information about the newly evolved agent as output. This step creates stronger agents through crossover and mutation.

[0276] Step 5:

[0277] The terminal visually displays the evolutionary process based on simulation data received from the server. It receives evolutionary data from the server as input and processes it to display it in an easy-to-understand manner for the user. As output, it renders the agent's behavior and evolutionary process as 3D graphics, allowing the user to observe it in real time.

[0278] Step 6:

[0279] The user observes the results of the simulation via the terminal and analyzes the behavior of the agents. Based on the displayed data, the user adjusts the conditions for the next simulation. As a specific input action, the user performs new parameter settings (such as increasing or decreasing the number of agents or changing their characteristics) via the user interface and sends the results to the server. As output, the setting information for the next simulation is prepared.

[0280] (Application Example 1)

[0281] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0282] The present invention aims to optimize urban traffic in the context of a smart city. In modern urban environments, inefficient traffic flows due to traffic congestion and accidents frequently occur, which not only makes it difficult for citizens to move but also increases the environmental burden. To solve such problems, a new system for analyzing and optimizing traffic patterns in real time is required.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0284] In this invention, the server includes means for generating a plurality of intelligent agents in a virtual environment and giving different characteristics to each agent, means for causing the intelligent agents to execute tasks in the virtual environment and evaluating their performance, and means for inheriting and evolving the characteristics of excellent intelligent agents to the next generation using a genetic algorithm based on the evaluation results. Thereby, through the simulation of traffic patterns in a smart city environment, it becomes possible to propose an efficient traffic flow.

[0285] The "virtual environment" is an artificial environment using digital technology that is different from the physical reality.

[0286] An "intelligent agent" is a program or software that can autonomously execute specific tasks and can learn and evolve based on the results.

[0287] "Characteristics" refer to the inherent capabilities and behavioral patterns of an intelligent agent, which are factors affecting the performance of tasks.

[0288] A "genetic algorithm" is a computational method that mimics the evolutionary process of organisms and cross-breeds and mutates the characteristics of individuals to find an optimal solution.

[0289] An "interface" is a connection point or screen through which a user can access and operate on the functions and data of a system.

[0290] A "traffic pattern" indicates the flow and dynamics of traffic within an urban environment, referring to the movement routes and speed trends of vehicles and pedestrians.

[0291] To implement this invention, it is necessary to construct a system that simulates a virtual environment and optimizes urban traffic using intelligent agents. The server generates a virtual environment and creates a large number of intelligent agents. The characteristics given to these agents are random, but they can also be changed according to specific criteria.

[0292] The server sets tasks so that the agents can autonomously analyze traffic patterns and find optimal movement routes. The performance of the agents is evaluated based on the resource utilization efficiency in a short time and the optimality of the movement routes. Based on these evaluation results, a genetic algorithm is applied to select agents with excellent characteristics and inherit the characteristics to the next generation.

[0293] Users can participate in the simulation process through an interface that allows them to adjust the input parameters of the evolutionary algorithm. Furthermore, the user's terminal visually displays the agent's evolutionary process and evaluation results based on data transmitted from the server. This allows users to closely observe the behavior and characteristics of specific agents and provide feedback to improve system performance.

[0294] The realization of this system fundamentally involves building a 3D environment using Unity and implementing an intelligent agent learning algorithm using Python AI libraries (e.g., TensorFlow, Keras). This will enable the simulation of traffic flow within a complex road network.

[0295] As a concrete example, we can cite a system that simulates traffic patterns during large-scale events in cities and proposes the optimal transportation route to the user. In this way, it is possible to efficiently help solve urban traffic congestion. Another example of a prompt message to be input to the generating AI model is a request such as, "Please propose the optimal route to alleviate congestion caused by a large-scale event being held in a smart city environment."

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The server initializes the virtual environment and generates intelligent agents with random characteristics. The input for this step is the basic configuration information of the virtual environment, and the output is multiple agents placed within the virtual environment. Based on this information, the server constructs a 3D environment in Unity and places the agents.

[0299] Step 2:

[0300] The server assigns tasks to agents and initiates actions to optimize traffic patterns. The input is a simulation scenario of the traffic situation, and the output is the movement pattern of the agents and the collected environmental data. The agents operate autonomously using a Python AI library and analyze the specified traffic patterns.

[0301] Step 3:

[0302] The server evaluates the performance of the agents and determines their superiority or inferiority. The input is the action log of the agents and their results, and the output is the performance score of each agent. The server processes the data using evaluation criteria such as resource utilization efficiency in a short period of time and reduction in travel distance.

[0303] Step 4:

[0304] The server applies a genetic algorithm to perform processing so that the characteristics of agents with excellent characteristics are inherited by the next generation. The input is the performance score obtained in Step 3, and the output is the characteristics of the agents in the next generation. Operations such as crossover and mutation are carried out to generate a new set.

[0305] Step 5:

[0306] The simulation is adjusted through an interface for the user to adjust the settings of the evolutionary algorithm. The input is the user's adjustment parameters, and the output is the new simulation conditions based on the settings. The user can intuitively change the settings using the GUI.

[0307] Step 6:

[0308] The terminal visualizes data sent from the server and displays the agent's evolution process and evaluation results. The input is simulation data from the server, and the output is graphics information displayed on the terminal. The terminal analyzes the data and presents the results in a user-friendly format using a visualization library.

[0309] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0310] This invention provides a more adaptive interface and evolution system by considering the user's emotional state during the process of evolving intelligent agents within a virtual environment. The server first generates a virtual environment and then randomly generates intelligent agents. Each agent has different characteristics and acts autonomously within the user-defined environment to perform tasks.

[0311] In this process, the server uses an emotion engine to recognize the user's emotional state in real time. Through facial recognition and voice analysis of the user, it identifies emotions such as joy, surprise, and concentration, and uses this information to dynamically adjust the agent's behavior and the parameters of its evolutionary algorithm.

[0312] For example, if a user expresses dissatisfaction with an agent's behavior, the server receives this emotional information and either enhances the characteristics of that particular agent or applies new parameters to improve its behavior in the next evolutionary cycle. This allows the agent's evolution process to directly reflect user feedback, enabling it to achieve higher performance.

[0313] The terminal visually provides the user with simulation results and evolutionary processes from the server, and also provides feedback that the user's emotions are being recognized. For example, if the user shows interest in a particular scenario or agent, the simulation scenario is adjusted and the agent's behavior changes are visualized in detail.

[0314] Through the device interface, users can turn the automatic adjustment function based on emotional information on or off while maintaining the basic settings of the evolutionary algorithm. This allows users to leverage improvements derived from personal feedback, enabling more real-time interaction and personalized agent development.

[0315] As a result, the present invention provides a flexible and efficient evolutionary process for intelligent agents through the introduction of emotional information, which can facilitate more appropriate decision-making and detailed scenario formation.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The server initializes the virtual environment. Based on the user-selected tasks and environment parameters, it sets the size and conditions of the environment. It also determines the basic constraints on which agents can operate.

[0319] Step 2:

[0320] The server randomly generates multiple intelligent agents. Each agent is assigned random characteristics and placed within a user-defined virtual environment. These characteristics include things like visual range and movement speed.

[0321] Step 3:

[0322] The terminal visually displays the virtual environment and agent information received from the server on the user interface. Through this, the user can see the initial simulation status.

[0323] Step 4:

[0324] The server activates an emotion engine and recognizes emotions in real time through the user's camera input and voice. This identifies the user's emotions, such as happiness, dissatisfaction, and level of interest.

[0325] Step 5:

[0326] The server starts the simulation and has the agent perform the configured task. Meanwhile, it monitors the user's emotional information and adjusts the agent's behavior and task progress as needed.

[0327] Step 6:

[0328] The server collects agent performance data and analyzes it along with user emotion recognition data. For example, if the user is happy while the agent is working efficiently, the server applies a genetic algorithm to enhance that behavior.

[0329] Step 7:

[0330] The device presents the user with visual data based on simulation results and evolutionary processes. This allows the user to intuitively understand the agent's evolutionary process.

[0331] Step 8:

[0332] Users can control the direction of evolution by manually adjusting evolutionary parameters and simulation settings using the device's interface, or by turning the automatic adjustment function provided by the emotion engine on or off.

[0333] Step 9:

[0334] The server incorporates user feedback and adjustments, preparing for the next evolutionary cycle. This aims to generate more advanced and user-adaptive intelligent agents.

[0335] (Example 2)

[0336] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0337] The problem this invention aims to solve is to achieve more adaptive and advanced agent performance by enabling dynamic adjustments that appropriately reflect user emotional information during the evolution process of artificial intelligence agents in virtual space. Furthermore, since conventional methods have the problem of not being able to adequately reflect user feedback, this invention aims to improve real-time interaction.

[0338] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0339] In this invention, the server includes means for generating multiple artificial intelligence agents in a virtual space and assigning different attributes to each agent; means for recognizing the user's emotional information in real time and dynamically adjusting the agent's behavior and evolution algorithm settings; and means for providing an operation screen for the user to adjust the evolution algorithm settings. This makes it possible to realize more real-time and personalized agent evolution utilizing the user's emotional information.

[0340] An "artificial intelligence agent" is a program that autonomously performs specific tasks in a virtual space and continuously improves its performance based on evolutionary algorithms.

[0341] An "evolutionary algorithm" is a computational method inspired by natural selection and genetics, used to adaptively inherit and improve the characteristics of artificial intelligence agents.

[0342] "Emotional information" refers to the real-time emotional state obtained from the user's facial recognition, voice analysis, etc., and is used to dynamically adjust the behavior of artificial intelligence agents.

[0343] A "virtual space" is an artificial environment created on a computer system, which serves as the stage for artificial intelligence agents to operate.

[0344] The "operation screen" refers to the interface that users use to adjust system settings and agent evolution algorithms, and is a screen that provides visual information.

[0345] This invention constitutes a system related to the evolution of artificial intelligence agents in a virtual space. The server constructs a virtual space using a generative AI model and randomly generates multiple artificial intelligence agents within it. These agents are configured to have different attributes and operate autonomously within an environment set by the user.

[0346] The server uses facial recognition software and voice analysis tools to recognize user emotional information in real time. Methods such as OpenCV and speech recognition APIs are used for this purpose. User emotional information (e.g., joy, surprise, concentration) is important for dynamically adjusting the agent's behavior and evolutionary algorithm settings.

[0347] The terminal visually presents the user with simulation results from the server and the agent's evolution process. Available 3D graphics libraries (e.g., Unity) animate the agent's activities to make it easier for the user to understand the changes. It also includes a feature that allows the user to provide feedback on how their emotions are perceived and how they affect the agent.

[0348] Users can adjust the settings of the evolutionary algorithm through the device's interface and turn on or off features that utilize emotional information. This allows users to incorporate their individual feedback into system improvements.

[0349] As a concrete example, consider a scenario where a user utilizes this system on an online education platform. When faced with a difficult problem, the system recognizes the user's confusion through facial expression analysis. The server then adjusts the educational agent's explanation method, evolving the agent to provide clearer hints.

[0350] An example of a prompt is, "Create a program that evolves the agent's optimal response method based on the user's sentiment information." Using this prompt, the system can provide an agent that is more tailored to specific user needs.

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] The server first generates a virtual space. This virtual space is dynamically constructed using a generative AI model. It receives user environment configuration data as input and generates elements of the virtual space (terrain, weather, object placement, etc.) based on this data. The output is the generated virtual environment information, which is passed on to the next agent generation step.

[0354] Step 2:

[0355] The server generates artificial intelligence agents within a virtual space. The inputs used are the virtual environment information generated in step 1 and the agent's initial configuration data (e.g., characteristics and behavioral patterns). Based on this, each agent is configured to have different characteristics. The output is the generated data for the initialized agents, enabling them to operate in the virtual space. Specifically, the agents prepare to begin executing tasks.

[0356] Step 3:

[0357] The server recognizes user emotions in real time. It takes user facial recognition data and voice data as input, analyzes them with an emotion engine, and identifies emotional states such as joy, surprise, and concentration. Image processing libraries and voice analysis APIs are used to process this data. The output is the recognized user emotion information, which is used to adjust the agent's behavior.

[0358] Step 4:

[0359] The server dynamically adjusts the agent's behavior and evolution algorithm settings based on the user's sentiment information. Inputs include the sentiment information obtained in step 3 and the current agent performance data. Through data calculations, the agent's characteristics and behavioral parameters are updated. The output of the adjusted settings is applied to the next evolutionary cycle and execution. Specifically, the agent changes to behave in a way that better aligns with the user's expectations.

[0360] Step 5:

[0361] The terminal visually presents the user with simulation results and agent evolution status received from the server. It receives agent activity data and evolution parameters from the server as input. Based on this data, an animation is generated using a 3D graphics library and presented to the user. The output is visualized information, allowing the user to understand the progress of the simulation.

[0362] Step 6:

[0363] The user changes system settings through the terminal's interface. This step involves reviewing the basic settings of the evolutionary algorithm and selecting whether to use emotional information (on or off). The user's input, configured via the terminal, is then incorporated. This adjusts the system's behavior, enabling the development of more personalized agents. The output consists of settings based on the user's selections, which are then applied to the next cycle.

[0364] (Application Example 2)

[0365] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0366] In today's online shopping environment, providing personalized experiences that appropriately reflect the user's emotional state is challenging. Furthermore, in the evolution process of intelligent agents, directly incorporating user feedback is necessary to promote the development of more flexible and efficient intelligent agents.

[0367] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0368] In this invention, the server includes means for generating multiple intelligent programs in a virtual space and giving each program different properties; means for having the intelligent programs perform tasks in the virtual space and evaluating the results; means for inheriting and evolving the properties of superior intelligent programs to the next generation using a genetic method based on the evaluation results; means for analyzing the user's mental state and dynamically adjusting the operation of the intelligent programs based on that information; and means for adjusting product recommendations and displays in a virtual store based on the user's mental state. This enables a personalized shopping experience that takes into account the user's emotional state, and makes it possible to realize a more high-performance intelligent agent by utilizing user feedback in the agent's evolution process.

[0369] A "virtual space" is an artificial three-dimensional space created using computer technology, an environment in which users can interact.

[0370] An "intelligent program" is a program designed to solve a specific problem, which operates autonomously and produces results.

[0371] "Nature" refers to the specific characteristics or abilities that an intelligent program possesses, which influence its operation and results.

[0372] "Means of evaluating results" refer to methods for quantitatively or qualitatively measuring the outcomes achieved by an intelligent program and determining its effectiveness.

[0373] A "genetic method" is an algorithm based on the theory of biological evolution, used to select programs with superior traits, pass them on to the next generation, and improve them.

[0374] "Means of analyzing mental state" refers to techniques that analyze emotional indicators such as the user's facial expressions and voice to recognize their psychological state.

[0375] "Dynamic adjustment methods" refer to methods of adaptively changing the behavior and display of a program based on real-time changing circumstances and data.

[0376] "Means of adjusting product recommendations and displays" refers to methods of changing product displays and presentations to provide optimal information according to the user's situation and interests.

[0377] This system provides a personalized user experience through the evolutionary process of intelligent programs operating in a virtual space. The server generates the virtual space and randomly places multiple intelligent programs within it. Each program has different properties and autonomously performs tasks under conditions set by the user.

[0378] The server uses software such as OpenCV and emotion recognition libraries to acquire video and audio from the user's terminal, analyze the data, and recognize the user's mental state in real time. Based on this information, it dynamically adjusts the operation of intelligent programs and the parameters of evolutionary algorithms. For example, if the user shows interest, the products suggested in the virtual store can be adjusted according to that emotion.

[0379] The terminal visually presents the user with the simulation results and evolutionary process transmitted from the server. Furthermore, users can customize the numerical values ​​of the evolutionary method through their interface, thereby further enhancing their individual experience.

[0380] As a concrete example, let's say a user visits a virtual store using their smartphone. In this case, the smartphone's camera captures the user's facial expressions, and an emotion recognition library analyzes this data to recognize feelings of joy or interest. Based on these results, the server can recommend the latest fashion items or other products that might interest the user. An example of a prompt might be, "If the user is happy, recommend the latest fashion trend items."

[0381] In this way, this system uses emotional information to improve the user experience and effectively promotes the evolution of intelligent programs.

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The server generates a virtual space and randomly places multiple intelligent programs within it. The input is user-defined environmental conditions, and the output is the initial state of the virtual space. Random properties are assigned to the intelligent programs, and the simulation begins in the virtual space.

[0385] Step 2:

[0386] The user's device captures facial expressions and audio data using its built-in camera and microphone. The input data consists of raw video and audio, while the output is in a format that can be processed. Specifically, the camera acquires video and the microphone collects audio.

[0387] Step 3:

[0388] The server uses an emotion recognition library to analyze the user's mental state from facial expressions and audio data. The input is the video and audio data acquired in step 2, and the output is the user's emotion information. The analysis program performs facial expression template matching and audio tone analysis.

[0389] Step 4:

[0390] The server dynamically adjusts the behavior of the intelligent program and the parameters of its evolutionary algorithm based on the analyzed emotional information. The input is the emotional information obtained in step 3, and the output is the adjusted program's behavioral guidelines. Specifically, the program's behavioral parameters are changed in real time.

[0391] Step 5:

[0392] The terminal visually presents the adjusted simulation results to the user. The input is the result of the intelligent program's operation adjusted in step 4, and the output is the user's visual feedback. Specifically, the latest simulation status is displayed on the screen.

[0393] Step 6:

[0394] Users can customize the numerical values ​​of the evolutionary method through the interface provided by the device. Input is a request for setting changes made by the user, and output is the updated parameters of the evolutionary algorithm. Users adjust the evolutionary parameters by operating the touchscreen or buttons.

[0395] Step 7:

[0396] The server repeats the above process, generating simulation results of the evolved intelligent program. Using the AI ​​model, it creates prompt messages and, if the user is pleased, presents information to the user, for example, "Please recommend the latest fashion trend items."

[0397] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0398] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0399] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0403] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0404] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0405] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0406] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0407] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0408] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0409] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0410] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0411] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0412] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0413] This invention involves running a virtual environment simulation on a server. First, the server constructs a virtual environment and sets various challenges and environmental parameters based on user selections. This allows for the reproduction of diverse scenarios, such as environments with many obstacles or environments with limited resources. Within this environment, the server randomly generates initial intelligent agents, each assigned different characteristics.

[0414] The server initiates a simulation in which agents autonomously act and perform tasks within a virtual environment. The agents utilize their given characteristics to execute the tasks, and the server evaluates each agent's performance. This evaluation includes factors such as resource gathering efficiency and the calculation of the shortest travel path.

[0415] Based on the evaluation results, the server applies a genetic algorithm to pass on information about agents with superior traits to the next generation. New agents are generated through crossover and mutation, and the evolutionary process is repeated. The server provides an interface for users to control these evolutionary processes, allowing users to freely adjust the evolutionary parameters.

[0416] The terminal visually displays the progress and results of the simulation based on evolutionary data received from the server. Users can use this information to observe agent performance and environmental conditions, and use it to improve the next simulation. Specifically, by focusing on a particular agent and analyzing its characteristics and behavioral patterns, it is possible to build a more efficient model.

[0417] In this way, the system of the present invention provides an environment that effectively supports the evolution of intelligent agents through the cooperation of servers, terminals, and users. Diverse simulations within the virtual environment facilitate the development of agents capable of advanced decision-making and problem-solving.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] The server initializes the virtual environment. Based on the user-selected task and environment parameters, it sets the size, structure, and resource allocation of the virtual environment. It also sends information related to the initial state of the environment to the terminal.

[0421] Step 2:

[0422] The server randomly generates multiple intelligent agents. Each agent is assigned different characteristics and abilities. For example, characteristics such as visual range, movement speed, and durability are randomly assigned. Information about the generated agents is sent to the terminal.

[0423] Step 3:

[0424] The terminal visually displays the agent on the user interface based on the agent information received from the server. The user then checks the agent's initial state.

[0425] Step 4:

[0426] The server initiates a simulation in which agents perform tasks within a virtual environment. Here, agents carry out pre-configured tasks such as resource exploration and competition with other agents.

[0427] Step 5:

[0428] The server monitors the running simulations and collects performance data for each agent. This includes task completion, resource gathering, and action time.

[0429] Step 6:

[0430] The server applies a genetic algorithm to generate new agents that enhance the characteristics of high-performing agents and pass them on to the next generation. Crossover and mutation techniques are used to ensure diversity.

[0431] Step 7:

[0432] The device displays a visualization of the simulation results and evolutionary process to the user. The user then refers to this and makes the necessary adjustments for the next evolutionary cycle.

[0433] Step 8:

[0434] Users adjust the parameters of the genetic algorithm via a terminal interface. By setting parameters such as the rate of evolution and the frequency of mutations, they can influence the results of the next simulation.

[0435] Step 9:

[0436] The server restarts the process from step 1 again, based on the new parameters that reflect the user's input, aiming for the evolution of a more advanced agent.

[0437] (Example 1)

[0438] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0439] In a virtual environment, there is a need to provide a system for the efficient evolution of intelligent agents with diverse characteristics, as well as for their evaluation and adjustment. Current systems make it difficult to manually control the agent generation and evolution process, and lack effective means of utilizing evaluation results. Therefore, there is a need to develop a system that users can intuitively operate and that allows for improved agent performance.

[0440] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0441] In this invention, the server includes means for generating a virtual environment, means for creating multiple intelligent agents and assigning different attributes to each agent, and means for inheriting and developing the agent attributes to the next generation using an evolutionary algorithm. This makes it possible to set various tasks based on user instructions and to visualize and optimize the behavior of intelligent agents.

[0442] A "computing device" is a device used for processing and performing calculations on data, and generally includes servers and personal computers.

[0443] A "virtual environment" is a simulated environment built within a computer that can mimic the physical laws and conditions of the real world.

[0444] An "intelligent agent" is a program entity that acts autonomously within a virtual environment and has the ability to perform specific tasks.

[0445] "Attributes" are characteristics or parameters assigned to an intelligent agent, such as movement speed and field of view.

[0446] An "evolutionary algorithm" is a mathematical method for improving the attributes of an agent based on evaluation results and passing them on to the next generation of agents.

[0447] A "user" is the entity that uses the system to configure the virtual environment and manage agents.

[0448] The "display screen" is a visual interface that allows users to adjust the settings of the evolutionary algorithm and check the simulation results.

[0449] "Visualization" is a technique that visually displays data and processes, enabling users to intuitively understand the information.

[0450] In embodiments of the present invention, the server functions as a computing device for generating a virtual environment. The server uses simulation software (such as Unity or Unreal Engine) to set physical laws and conditions within the virtual environment. Based on user instructions, the server generates multiple intelligent agents and assigns different attributes to each agent. As a result, the agents have the ability to act autonomously and perform specific tasks.

[0451] The server uses an evolutionary algorithm to pass on agent attributes to the next generation, aiming for efficient evolution. Evaluation metrics include resource gathering efficiency and optimization of movement paths, and the server evaluates the agent's performance in real time based on these metrics.

[0452] The terminal displays a simulation to the user using 3D visualization technology, based on evolutionary data received from the server. Through this, the user can observe and analyze the agent's behavior and environmental changes, and adjust the evolution settings.

[0453] As a concrete example, we can cite a simulation in which the user sets up an environment with limited resources, and the agent discovers a strategy to gather those resources in the shortest amount of time. Furthermore, the prompt input to the generated AI model would be: "Explain the process by which the agent uses evolutionary simulation to find an efficient resource gathering strategy within a virtual environment with limited resources."

[0454] This allows users to observe the behavior of intelligent agents and gain valuable insights to build more efficient strategies.

[0455] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0456] Step 1:

[0457] The server constructs a virtual environment. It receives user-specified environment parameters (size, obstacle placement, resource distribution, etc.) as input and reproduces them using simulation software. The output is a simulable virtual environment. During this process, physical laws and conditions within the environment are established, influencing the agent's behavior in subsequent simulations.

[0458] Step 2:

[0459] The server generates intelligent agents. As input, it receives the number of agents and their initial characteristics (such as movement speed, field of view, and resource gathering ability) specified by the user. Based on this information, the server generates agents incorporating random elements and provides data with a unique ID and attributes assigned to each agent as output. This step confirms that the agents exhibit diverse behavioral patterns.

[0460] Step 3:

[0461] The server starts simulating the agents. It receives virtual environment data and agent data as input, and the agents autonomously begin their actions. The server tracks each agent's movement and resource gathering actions in real time, generating action logs and data as output. Next, it evaluates these actions and collects data on performance metrics (e.g., resource gathering efficiency).

[0462] Step 4:

[0463] The server evaluates the agent's performance and applies an evolutionary algorithm. It receives agent behavior logs and performance data as input. Based on this information, the server executes a genetic algorithm to pass on the traits of superior agents to the next generation, generating information about the newly evolved agent as output. This step creates stronger agents through crossover and mutation.

[0464] Step 5:

[0465] The terminal visually displays the evolutionary process based on simulation data received from the server. It receives evolutionary data from the server as input and processes it to display it in an easy-to-understand manner for the user. As output, it renders the agent's behavior and evolutionary process as 3D graphics, allowing the user to observe it in real time.

[0466] Step 6:

[0467] The user observes the simulation results via a terminal and analyzes the agent's behavior. Based on the displayed data, the user adjusts the conditions for the next simulation. Specific input actions include setting new parameters via the user interface (e.g., increasing or decreasing the number of agents or changing their characteristics) and sending the results to the server. The output is the preparation of configuration information for the next simulation.

[0468] (Application Example 1)

[0469] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0470] This invention aims to optimize urban transportation in the context of smart cities. In modern urban environments, inefficient traffic flow due to congestion and accidents is frequent, which not only makes it difficult for citizens to get around but also increases the environmental burden. To solve these problems, a new system is needed that analyzes and optimizes traffic patterns in real time.

[0471] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0472] In this invention, the server includes means for generating multiple intelligent agents within a virtual environment and assigning different characteristics to each agent; means for having the intelligent agents perform tasks within the virtual environment and evaluating their performance; and means for using a genetic algorithm to inherit and evolve the characteristics of superior intelligent agents to the next generation based on the evaluation results. This makes it possible to propose efficient traffic flow through the simulation of traffic patterns in a smart city environment.

[0473] A "virtual environment" is an artificial environment created using digital technology that is different from physical reality.

[0474] An "intelligent agent" is a program or software that autonomously performs a specific task and is capable of learning and evolving based on the results.

[0475] "Traits" refer to the unique abilities and behavioral patterns that intelligent agents possess, and are factors that influence task performance.

[0476] A "genetic algorithm" is a computational method that mimics the evolutionary process of organisms and uses crossover and mutation to find the optimal solution by manipulating the characteristics of individual organisms.

[0477] An "interface" is a point of contact or screen that allows a user to access and manipulate the functions and data of a system.

[0478] "Traffic patterns" refer to the flow and dynamics of traffic within an urban environment, and include trends in the movement routes and speeds of vehicles and pedestrians.

[0479] To implement this invention, it is necessary to build a system that simulates a virtual environment and optimizes urban traffic using intelligent agents. The server generates the virtual environment and creates numerous intelligent agents. The characteristics given to these agents are random, but they can also be changed according to specific criteria.

[0480] The server sets tasks for agents to autonomously analyze traffic patterns and find the optimal travel route. Agent performance is evaluated based on resource utilization efficiency and route optimization over short periods. Based on these evaluation results, a genetic algorithm is applied to select agents with superior traits and pass those traits on to the next generation.

[0481] Users can participate in the simulation process through an interface that allows them to adjust the input parameters of the evolutionary algorithm. Furthermore, the user's terminal visually displays the agent's evolutionary process and evaluation results based on data transmitted from the server. This allows users to closely observe the behavior and characteristics of specific agents and provide feedback to improve system performance.

[0482] The realization of this system fundamentally involves building a 3D environment using Unity and implementing an intelligent agent learning algorithm using Python AI libraries (e.g., TensorFlow, Keras). This will enable the simulation of traffic flow within a complex road network.

[0483] As a concrete example, we can cite a system that simulates traffic patterns during large-scale events in cities and proposes the optimal transportation route to the user. In this way, it is possible to efficiently help solve urban traffic congestion. Another example of a prompt message to be input to the generating AI model is a request such as, "Please propose the optimal route to alleviate congestion caused by a large-scale event being held in a smart city environment."

[0484] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0485] Step 1:

[0486] The server initializes the virtual environment and generates intelligent agents with random characteristics. The input for this step is the basic configuration information of the virtual environment, and the output is multiple agents placed within the virtual environment. Based on this information, the server constructs a 3D environment in Unity and places the agents.

[0487] Step 2:

[0488] The server assigns tasks to agents and initiates actions to optimize traffic patterns. The input is a simulated traffic scenario, and the output is the agent's movement pattern and collected environmental data. The agents operate autonomously using Python's AI library to analyze the specified traffic patterns.

[0489] Step 3:

[0490] The server evaluates the performance of the agents and determines their relative strengths and weaknesses. The input consists of agent activity logs and their results, while the output is each agent's performance score. The server processes the data using criteria such as resource utilization efficiency in a short time and the degree of reduction in travel distance.

[0491] Step 4:

[0492] A genetic algorithm is applied, and the server processes the data to pass on the traits of superior agents to the next generation. The input is the performance score obtained in step 3, and the output is the traits of the next generation agents. Crossover and mutation operations are performed to generate a new set.

[0493] Step 5:

[0494] The simulation is tuned through an interface that allows the user to adjust the settings of the evolutionary algorithm. The input is the user's tuning parameters, and the output is the new simulation conditions based on those settings. The user can intuitively change the settings using the GUI.

[0495] Step 6:

[0496] The terminal visualizes data sent from the server and displays the agent's evolution process and evaluation results. The input is simulation data from the server, and the output is graphics information displayed on the terminal. The terminal analyzes the data and presents the results in a user-friendly format using a visualization library.

[0497] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0498] This invention provides a more adaptive interface and evolution system by considering the user's emotional state during the process of evolving intelligent agents within a virtual environment. The server first generates a virtual environment and then randomly generates intelligent agents. Each agent has different characteristics and acts autonomously within the user-defined environment to perform tasks.

[0499] In this process, the server uses an emotion engine to recognize the user's emotional state in real time. Through facial recognition and voice analysis of the user, it identifies emotions such as joy, surprise, and concentration, and uses this information to dynamically adjust the agent's behavior and the parameters of its evolutionary algorithm.

[0500] For example, if a user expresses dissatisfaction with an agent's behavior, the server receives this emotional information and either enhances the characteristics of that particular agent or applies new parameters to improve its behavior in the next evolutionary cycle. This allows the agent's evolution process to directly reflect user feedback, enabling it to achieve higher performance.

[0501] The terminal visually provides the user with simulation results and evolutionary processes from the server, and also provides feedback that the user's emotions are being recognized. For example, if the user shows interest in a particular scenario or agent, the simulation scenario is adjusted and the agent's behavior changes are visualized in detail.

[0502] Through the device interface, users can turn the automatic adjustment function based on emotional information on or off while maintaining the basic settings of the evolutionary algorithm. This allows users to leverage improvements derived from personal feedback, enabling more real-time interaction and personalized agent development.

[0503] As a result, the present invention provides a flexible and efficient evolutionary process for intelligent agents through the introduction of emotional information, which can facilitate more appropriate decision-making and detailed scenario formation.

[0504] The following describes the processing flow.

[0505] Step 1:

[0506] The server initializes the virtual environment. Based on the user-selected tasks and environment parameters, it sets the size and conditions of the environment. It also determines the basic constraints on which agents can operate.

[0507] Step 2:

[0508] The server randomly generates multiple intelligent agents. Each agent is assigned random characteristics and placed within a user-defined virtual environment. These characteristics include things like visual range and movement speed.

[0509] Step 3:

[0510] The terminal visually displays the virtual environment and agent information received from the server on the user interface. Through this, the user can see the initial simulation status.

[0511] Step 4:

[0512] The server activates an emotion engine and recognizes emotions in real time through the user's camera input and voice. This identifies the user's emotions, such as happiness, dissatisfaction, and level of interest.

[0513] Step 5:

[0514] The server starts the simulation and has the agent perform the configured task. Meanwhile, it monitors the user's emotional information and adjusts the agent's behavior and task progress as needed.

[0515] Step 6:

[0516] The server collects agent performance data and analyzes it along with user emotion recognition data. For example, if the user is happy while the agent is working efficiently, the server applies a genetic algorithm to enhance that behavior.

[0517] Step 7:

[0518] The device presents the user with visual data based on simulation results and evolutionary processes. This allows the user to intuitively understand the agent's evolutionary process.

[0519] Step 8:

[0520] Users can control the direction of evolution by manually adjusting evolutionary parameters and simulation settings using the device's interface, or by turning the automatic adjustment function provided by the emotion engine on or off.

[0521] Step 9:

[0522] The server incorporates user feedback and adjustments, preparing for the next evolutionary cycle. This aims to generate more advanced and user-adaptive intelligent agents.

[0523] (Example 2)

[0524] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0525] The problem this invention aims to solve is to achieve more adaptive and advanced agent performance by enabling dynamic adjustments that appropriately reflect user emotional information during the evolution process of artificial intelligence agents in virtual space. Furthermore, since conventional methods have the problem of not being able to adequately reflect user feedback, this invention aims to improve real-time interaction.

[0526] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0527] In this invention, the server includes means for generating multiple artificial intelligence agents in a virtual space and assigning different attributes to each agent; means for recognizing the user's emotional information in real time and dynamically adjusting the agent's behavior and evolution algorithm settings; and means for providing an operation screen for the user to adjust the evolution algorithm settings. This makes it possible to realize more real-time and personalized agent evolution utilizing the user's emotional information.

[0528] An "artificial intelligence agent" is a program that autonomously performs specific tasks in a virtual space and continuously improves its performance based on evolutionary algorithms.

[0529] An "evolutionary algorithm" is a computational method inspired by natural selection and genetics, used to adaptively inherit and improve the characteristics of artificial intelligence agents.

[0530] "Emotional information" refers to the real-time emotional state obtained from the user's facial recognition, voice analysis, etc., and is used to dynamically adjust the behavior of artificial intelligence agents.

[0531] A "virtual space" is an artificial environment created on a computer system, which serves as the stage for artificial intelligence agents to operate.

[0532] The "operation screen" refers to the interface that users use to adjust system settings and agent evolution algorithms, and is a screen that provides visual information.

[0533] This invention constitutes a system related to the evolution of artificial intelligence agents in a virtual space. The server constructs a virtual space using a generative AI model and randomly generates multiple artificial intelligence agents within it. These agents are configured to have different attributes and operate autonomously within an environment set by the user.

[0534] The server uses facial recognition software and voice analysis tools to recognize user emotional information in real time. Methods such as OpenCV and speech recognition APIs are used for this purpose. User emotional information (e.g., joy, surprise, concentration) is important for dynamically adjusting the agent's behavior and evolutionary algorithm settings.

[0535] The terminal visually presents the user with simulation results from the server and the agent's evolution process. Available 3D graphics libraries (e.g., Unity) animate the agent's activities to make it easier for the user to understand the changes. It also includes a feature that allows the user to provide feedback on how their emotions are perceived and how they affect the agent.

[0536] Users can adjust the settings of the evolutionary algorithm through the device's interface and turn on or off features that utilize emotional information. This allows users to incorporate their individual feedback into system improvements.

[0537] As a concrete example, consider a scenario where a user utilizes this system on an online education platform. When faced with a difficult problem, the system recognizes the user's confusion through facial expression analysis. The server then adjusts the educational agent's explanation method, evolving the agent to provide clearer hints.

[0538] An example of a prompt is, "Create a program that evolves the agent's optimal response method based on the user's sentiment information." Using this prompt, the system can provide an agent that is more tailored to specific user needs.

[0539] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0540] Step 1:

[0541] The server first generates a virtual space. This virtual space is dynamically constructed using a generative AI model. It receives user environment configuration data as input and generates elements of the virtual space (terrain, weather, object placement, etc.) based on this data. The output is the generated virtual environment information, which is passed on to the next agent generation step.

[0542] Step 2:

[0543] The server generates artificial intelligence agents within a virtual space. The inputs used are the virtual environment information generated in step 1 and the agent's initial configuration data (e.g., characteristics and behavioral patterns). Based on this, each agent is configured to have different characteristics. The output is the generated data for the initialized agents, enabling them to operate in the virtual space. Specifically, the agents prepare to begin executing tasks.

[0544] Step 3:

[0545] The server recognizes user emotions in real time. It takes user facial recognition data and voice data as input, analyzes them with an emotion engine, and identifies emotional states such as joy, surprise, and concentration. Image processing libraries and voice analysis APIs are used to process this data. The output is the recognized user emotion information, which is used to adjust the agent's behavior.

[0546] Step 4:

[0547] The server dynamically adjusts the agent's behavior and evolution algorithm settings based on the user's sentiment information. Inputs include the sentiment information obtained in step 3 and the current agent performance data. Through data calculations, the agent's characteristics and behavioral parameters are updated. The output of the adjusted settings is applied to the next evolutionary cycle and execution. Specifically, the agent changes to behave in a way that better aligns with the user's expectations.

[0548] Step 5:

[0549] The terminal visually presents the user with simulation results and agent evolution status received from the server. It receives agent activity data and evolution parameters from the server as input. Based on this data, an animation is generated using a 3D graphics library and presented to the user. The output is visualized information, allowing the user to understand the progress of the simulation.

[0550] Step 6:

[0551] The user changes system settings through the terminal's interface. This step involves reviewing the basic settings of the evolutionary algorithm and selecting whether to use emotional information (on or off). The user's input, configured via the terminal, is then incorporated. This adjusts the system's behavior, enabling the development of more personalized agents. The output consists of settings based on the user's selections, which are then applied to the next cycle.

[0552] (Application Example 2)

[0553] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0554] In today's online shopping environment, providing personalized experiences that appropriately reflect the user's emotional state is challenging. Furthermore, in the evolution process of intelligent agents, directly incorporating user feedback is necessary to promote the development of more flexible and efficient intelligent agents.

[0555] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0556] In this invention, the server includes means for generating multiple intelligent programs in a virtual space and giving each program different properties; means for having the intelligent programs perform tasks in the virtual space and evaluating the results; means for inheriting and evolving the properties of superior intelligent programs to the next generation using a genetic method based on the evaluation results; means for analyzing the user's mental state and dynamically adjusting the operation of the intelligent programs based on that information; and means for adjusting product recommendations and displays in a virtual store based on the user's mental state. This enables a personalized shopping experience that takes into account the user's emotional state, and makes it possible to realize a more high-performance intelligent agent by utilizing user feedback in the agent's evolution process.

[0557] A "virtual space" is an artificial three-dimensional space created using computer technology, an environment in which users can interact.

[0558] An "intelligent program" is a program designed to solve a specific problem, which operates autonomously and produces results.

[0559] "Nature" refers to the specific characteristics or abilities that an intelligent program possesses, which influence its operation and results.

[0560] "Means of evaluating results" refer to methods for quantitatively or qualitatively measuring the outcomes achieved by an intelligent program and determining its effectiveness.

[0561] A "genetic method" is an algorithm based on the theory of biological evolution, used to select programs with superior traits, pass them on to the next generation, and improve them.

[0562] "Means of analyzing mental state" refers to techniques that analyze emotional indicators such as the user's facial expressions and voice to recognize their psychological state.

[0563] "Dynamic adjustment methods" refer to methods of adaptively changing the behavior and display of a program based on real-time changing circumstances and data.

[0564] "Means of adjusting product recommendations and displays" refers to methods of changing product displays and presentations to provide optimal information according to the user's situation and interests.

[0565] This system provides a personalized user experience through the evolutionary process of intelligent programs operating in a virtual space. The server generates the virtual space and randomly places multiple intelligent programs within it. Each program has different properties and autonomously performs tasks under conditions set by the user.

[0566] The server uses software such as OpenCV and emotion recognition libraries to acquire video and audio from the user's terminal, analyze the data, and recognize the user's mental state in real time. Based on this information, it dynamically adjusts the operation of intelligent programs and the parameters of evolutionary algorithms. For example, if the user shows interest, the products suggested in the virtual store can be adjusted according to that emotion.

[0567] The terminal visually presents the user with the simulation results and evolutionary process transmitted from the server. Furthermore, users can customize the numerical values ​​of the evolutionary method through their interface, thereby further enhancing their individual experience.

[0568] As a concrete example, let's say a user visits a virtual store using their smartphone. In this case, the smartphone's camera captures the user's facial expressions, and an emotion recognition library analyzes this data to recognize feelings of joy or interest. Based on these results, the server can recommend the latest fashion items or other products that might interest the user. An example of a prompt might be, "If the user is happy, recommend the latest fashion trend items."

[0569] In this way, this system uses emotional information to improve the user experience and effectively promotes the evolution of intelligent programs.

[0570] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0571] Step 1:

[0572] The server generates a virtual space and randomly places multiple intelligent programs within it. The input is user-defined environmental conditions, and the output is the initial state of the virtual space. Random properties are assigned to the intelligent programs, and the simulation begins in the virtual space.

[0573] Step 2:

[0574] The user's device captures facial expressions and audio data using its built-in camera and microphone. The input data consists of raw video and audio, while the output is in a format that can be processed. Specifically, the camera acquires video and the microphone collects audio.

[0575] Step 3:

[0576] The server uses an emotion recognition library to analyze the user's mental state from facial expressions and audio data. The input is the video and audio data acquired in step 2, and the output is the user's emotion information. The analysis program performs facial expression template matching and audio tone analysis.

[0577] Step 4:

[0578] The server dynamically adjusts the behavior of the intelligent program and the parameters of its evolutionary algorithm based on the analyzed emotional information. The input is the emotional information obtained in step 3, and the output is the adjusted program's behavioral guidelines. Specifically, the program's behavioral parameters are changed in real time.

[0579] Step 5:

[0580] The terminal visually presents the adjusted simulation results to the user. The input is the result of the intelligent program's operation adjusted in step 4, and the output is the user's visual feedback. Specifically, the latest simulation status is displayed on the screen.

[0581] Step 6:

[0582] Users can customize the numerical values ​​of the evolutionary method through the interface provided by the device. Input is a request for setting changes made by the user, and output is the updated parameters of the evolutionary algorithm. Users adjust the evolutionary parameters by operating the touchscreen or buttons.

[0583] Step 7:

[0584] The server repeats the above process, generating simulation results of the evolved intelligent program. Using the AI ​​model, it creates prompt messages and, if the user is pleased, presents information to the user, for example, "Please recommend the latest fashion trend items."

[0585] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0586] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0587] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0588] [Fourth Embodiment]

[0589] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0590] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0591] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0592] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0593] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0594] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0595] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0596] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0597] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0598] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0599] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0600] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0601] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0602] This invention involves running a virtual environment simulation on a server. First, the server constructs a virtual environment and sets various challenges and environmental parameters based on user selections. This allows for the reproduction of diverse scenarios, such as environments with many obstacles or environments with limited resources. Within this environment, the server randomly generates initial intelligent agents, each assigned different characteristics.

[0603] The server initiates a simulation in which agents autonomously act and perform tasks within a virtual environment. The agents utilize their given characteristics to execute the tasks, and the server evaluates each agent's performance. This evaluation includes factors such as resource gathering efficiency and the calculation of the shortest travel path.

[0604] Based on the evaluation results, the server applies a genetic algorithm to pass on information about agents with superior traits to the next generation. New agents are generated through crossover and mutation, and the evolutionary process is repeated. The server provides an interface for users to control these evolutionary processes, allowing users to freely adjust the evolutionary parameters.

[0605] The terminal visually displays the progress and results of the simulation based on evolutionary data received from the server. Users can use this information to observe agent performance and environmental conditions, and use it to improve the next simulation. Specifically, by focusing on a particular agent and analyzing its characteristics and behavioral patterns, it is possible to build a more efficient model.

[0606] In this way, the system of the present invention provides an environment that effectively supports the evolution of intelligent agents through the cooperation of servers, terminals, and users. Diverse simulations within the virtual environment facilitate the development of agents capable of advanced decision-making and problem-solving.

[0607] The following describes the processing flow.

[0608] Step 1:

[0609] The server initializes the virtual environment. Based on the user-selected task and environment parameters, it sets the size, structure, and resource allocation of the virtual environment. It also sends information related to the initial state of the environment to the terminal.

[0610] Step 2:

[0611] The server randomly generates multiple intelligent agents. Each agent is assigned different characteristics and abilities. For example, characteristics such as visual range, movement speed, and durability are randomly assigned. Information about the generated agents is sent to the terminal.

[0612] Step 3:

[0613] The terminal visually displays the agent on the user interface based on the agent information received from the server. The user then checks the agent's initial state.

[0614] Step 4:

[0615] The server initiates a simulation in which agents perform tasks within a virtual environment. Here, agents carry out pre-configured tasks such as resource exploration and competition with other agents.

[0616] Step 5:

[0617] The server monitors the running simulations and collects performance data for each agent. This includes task completion, resource gathering, and action time.

[0618] Step 6:

[0619] The server applies a genetic algorithm to generate new agents that enhance the characteristics of high-performing agents and pass them on to the next generation. Crossover and mutation techniques are used to ensure diversity.

[0620] Step 7:

[0621] The device displays a visualization of the simulation results and evolutionary process to the user. The user then refers to this and makes the necessary adjustments for the next evolutionary cycle.

[0622] Step 8:

[0623] Users adjust the parameters of the genetic algorithm via a terminal interface. By setting parameters such as the rate of evolution and the frequency of mutations, they can influence the results of the next simulation.

[0624] Step 9:

[0625] The server restarts the process from step 1 again, based on the new parameters that reflect the user's input, aiming for the evolution of a more advanced agent.

[0626] (Example 1)

[0627] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0628] In a virtual environment, there is a need to provide a system for the efficient evolution of intelligent agents with diverse characteristics, as well as for their evaluation and adjustment. Current systems make it difficult to manually control the agent generation and evolution process, and lack effective means of utilizing evaluation results. Therefore, there is a need to develop a system that users can intuitively operate and that allows for improved agent performance.

[0629] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0630] In this invention, the server includes means for generating a virtual environment, means for creating multiple intelligent agents and assigning different attributes to each agent, and means for inheriting and developing the agent attributes to the next generation using an evolutionary algorithm. This makes it possible to set various tasks based on user instructions and to visualize and optimize the behavior of intelligent agents.

[0631] A "computing device" is a device used for processing and performing calculations on data, and generally includes servers and personal computers.

[0632] A "virtual environment" is a simulated environment built within a computer that can mimic the physical laws and conditions of the real world.

[0633] An "intelligent agent" is a program entity that acts autonomously within a virtual environment and has the ability to perform specific tasks.

[0634] "Attributes" are characteristics or parameters assigned to an intelligent agent, such as movement speed and field of view.

[0635] An "evolutionary algorithm" is a mathematical method for improving the attributes of an agent based on evaluation results and passing them on to the next generation of agents.

[0636] A "user" is the entity that uses the system to configure the virtual environment and manage agents.

[0637] The "display screen" is a visual interface that allows users to adjust the settings of the evolutionary algorithm and check the simulation results.

[0638] "Visualization" is a technique that visually displays data and processes, enabling users to intuitively understand the information.

[0639] In embodiments of the present invention, the server functions as a computing device for generating a virtual environment. The server uses simulation software (such as Unity or Unreal Engine) to set physical laws and conditions within the virtual environment. Based on user instructions, the server generates multiple intelligent agents and assigns different attributes to each agent. As a result, the agents have the ability to act autonomously and perform specific tasks.

[0640] The server uses an evolutionary algorithm to pass on agent attributes to the next generation, aiming for efficient evolution. Evaluation metrics include resource gathering efficiency and optimization of movement paths, and the server evaluates the agent's performance in real time based on these metrics.

[0641] The terminal displays a simulation to the user using 3D visualization technology, based on evolutionary data received from the server. Through this, the user can observe and analyze the agent's behavior and environmental changes, and adjust the evolution settings.

[0642] As a concrete example, we can cite a simulation in which the user sets up an environment with limited resources, and the agent discovers a strategy to gather those resources in the shortest amount of time. Furthermore, the prompt input to the generated AI model would be: "Explain the process by which the agent uses evolutionary simulation to find an efficient resource gathering strategy within a virtual environment with limited resources."

[0643] This allows users to observe the behavior of intelligent agents and gain valuable insights to build more efficient strategies.

[0644] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0645] Step 1:

[0646] The server constructs a virtual environment. It receives user-specified environment parameters (size, obstacle placement, resource distribution, etc.) as input and reproduces them using simulation software. The output is a simulable virtual environment. During this process, physical laws and conditions within the environment are established, influencing the agent's behavior in subsequent simulations.

[0647] Step 2:

[0648] The server generates intelligent agents. As input, it receives the number of agents and their initial characteristics (such as movement speed, field of view, and resource gathering ability) specified by the user. Based on this information, the server generates agents incorporating random elements and provides data with a unique ID and attributes assigned to each agent as output. This step confirms that the agents exhibit diverse behavioral patterns.

[0649] Step 3:

[0650] The server starts simulating the agents. It receives virtual environment data and agent data as input, and the agents autonomously begin their actions. The server tracks each agent's movement and resource gathering actions in real time, generating action logs and data as output. Next, it evaluates these actions and collects data on performance metrics (e.g., resource gathering efficiency).

[0651] Step 4:

[0652] The server evaluates the agent's performance and applies an evolutionary algorithm. It receives agent behavior logs and performance data as input. Based on this information, the server executes a genetic algorithm to pass on the traits of superior agents to the next generation, generating information about the newly evolved agent as output. This step creates stronger agents through crossover and mutation.

[0653] Step 5:

[0654] The terminal visually displays the evolutionary process based on simulation data received from the server. It receives evolutionary data from the server as input and processes it to display it in an easy-to-understand manner for the user. As output, it renders the agent's behavior and evolutionary process as 3D graphics, allowing the user to observe it in real time.

[0655] Step 6:

[0656] The user observes the simulation results via a terminal and analyzes the agent's behavior. Based on the displayed data, the user adjusts the conditions for the next simulation. Specific input actions include setting new parameters via the user interface (e.g., increasing or decreasing the number of agents or changing their characteristics) and sending the results to the server. The output is the preparation of configuration information for the next simulation.

[0657] (Application Example 1)

[0658] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0659] This invention aims to optimize urban transportation in the context of smart cities. In modern urban environments, inefficient traffic flow due to congestion and accidents is frequent, which not only makes it difficult for citizens to get around but also increases the environmental burden. To solve these problems, a new system is needed that analyzes and optimizes traffic patterns in real time.

[0660] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0661] In this invention, the server includes means for generating multiple intelligent agents within a virtual environment and assigning different characteristics to each agent; means for having the intelligent agents perform tasks within the virtual environment and evaluating their performance; and means for using a genetic algorithm to inherit and evolve the characteristics of superior intelligent agents to the next generation based on the evaluation results. This makes it possible to propose efficient traffic flow through the simulation of traffic patterns in a smart city environment.

[0662] A "virtual environment" is an artificial environment created using digital technology that is different from physical reality.

[0663] An "intelligent agent" is a program or software that autonomously performs a specific task and is capable of learning and evolving based on the results.

[0664] "Traits" refer to the unique abilities and behavioral patterns that intelligent agents possess, and are factors that influence task performance.

[0665] A "genetic algorithm" is a computational method that mimics the evolutionary process of organisms and uses crossover and mutation to find the optimal solution by manipulating the characteristics of individual organisms.

[0666] An "interface" is a point of contact or screen that allows a user to access and manipulate the functions and data of a system.

[0667] "Traffic patterns" refer to the flow and dynamics of traffic within an urban environment, and include trends in the movement routes and speeds of vehicles and pedestrians.

[0668] To implement this invention, it is necessary to build a system that simulates a virtual environment and optimizes urban traffic using intelligent agents. The server generates the virtual environment and creates numerous intelligent agents. The characteristics given to these agents are random, but they can also be changed according to specific criteria.

[0669] The server sets tasks for agents to autonomously analyze traffic patterns and find the optimal travel route. Agent performance is evaluated based on resource utilization efficiency and route optimization over short periods. Based on these evaluation results, a genetic algorithm is applied to select agents with superior traits and pass those traits on to the next generation.

[0670] Users can participate in the simulation process through an interface that allows them to adjust the input parameters of the evolutionary algorithm. Furthermore, the user's terminal visually displays the agent's evolutionary process and evaluation results based on data transmitted from the server. This allows users to closely observe the behavior and characteristics of specific agents and provide feedback to improve system performance.

[0671] The realization of this system fundamentally involves building a 3D environment using Unity and implementing an intelligent agent learning algorithm using Python AI libraries (e.g., TensorFlow, Keras). This will enable the simulation of traffic flow within a complex road network.

[0672] As a concrete example, we can cite a system that simulates traffic patterns during large-scale events in cities and proposes the optimal transportation route to the user. In this way, it is possible to efficiently help solve urban traffic congestion. Another example of a prompt message to be input to the generating AI model is a request such as, "Please propose the optimal route to alleviate congestion caused by a large-scale event being held in a smart city environment."

[0673] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0674] Step 1:

[0675] The server initializes the virtual environment and generates intelligent agents with random characteristics. The input for this step is the basic configuration information of the virtual environment, and the output is multiple agents placed within the virtual environment. Based on this information, the server constructs a 3D environment in Unity and places the agents.

[0676] Step 2:

[0677] The server assigns tasks to agents and initiates actions to optimize traffic patterns. The input is a simulated traffic scenario, and the output is the agent's movement pattern and collected environmental data. The agents operate autonomously using Python's AI library to analyze the specified traffic patterns.

[0678] Step 3:

[0679] The server evaluates the performance of the agents and determines their relative strengths and weaknesses. The input consists of agent activity logs and their results, while the output is each agent's performance score. The server processes the data using criteria such as resource utilization efficiency in a short time and the degree of reduction in travel distance.

[0680] Step 4:

[0681] A genetic algorithm is applied, and the server processes the data to pass on the traits of superior agents to the next generation. The input is the performance score obtained in step 3, and the output is the traits of the next generation agents. Crossover and mutation operations are performed to generate a new set.

[0682] Step 5:

[0683] The simulation is tuned through an interface that allows the user to adjust the settings of the evolutionary algorithm. The input is the user's tuning parameters, and the output is the new simulation conditions based on those settings. The user can intuitively change the settings using the GUI.

[0684] Step 6:

[0685] The terminal visualizes data sent from the server and displays the agent's evolution process and evaluation results. The input is simulation data from the server, and the output is graphics information displayed on the terminal. The terminal analyzes the data and presents the results in a user-friendly format using a visualization library.

[0686] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0687] This invention provides a more adaptive interface and evolution system by considering the user's emotional state during the process of evolving intelligent agents within a virtual environment. The server first generates a virtual environment and then randomly generates intelligent agents. Each agent has different characteristics and acts autonomously within the user-defined environment to perform tasks.

[0688] In this process, the server uses an emotion engine to recognize the user's emotional state in real time. Through facial recognition and voice analysis of the user, it identifies emotions such as joy, surprise, and concentration, and uses this information to dynamically adjust the agent's behavior and the parameters of its evolutionary algorithm.

[0689] For example, if a user expresses dissatisfaction with an agent's behavior, the server receives this emotional information and either enhances the characteristics of that particular agent or applies new parameters to improve its behavior in the next evolutionary cycle. This allows the agent's evolution process to directly reflect user feedback, enabling it to achieve higher performance.

[0690] The terminal visually provides the user with simulation results and evolutionary processes from the server, and also provides feedback that the user's emotions are being recognized. For example, if the user shows interest in a particular scenario or agent, the simulation scenario is adjusted and the agent's behavior changes are visualized in detail.

[0691] Through the device interface, users can turn the automatic adjustment function based on emotional information on or off while maintaining the basic settings of the evolutionary algorithm. This allows users to leverage improvements derived from personal feedback, enabling more real-time interaction and personalized agent development.

[0692] As a result, the present invention provides a flexible and efficient evolutionary process for intelligent agents through the introduction of emotional information, which can facilitate more appropriate decision-making and detailed scenario formation.

[0693] The following describes the processing flow.

[0694] Step 1:

[0695] The server initializes the virtual environment. Based on the user-selected tasks and environment parameters, it sets the size and conditions of the environment. It also determines the basic constraints on which agents can operate.

[0696] Step 2:

[0697] The server randomly generates multiple intelligent agents. Each agent is assigned random characteristics and placed within a user-defined virtual environment. These characteristics include things like visual range and movement speed.

[0698] Step 3:

[0699] The terminal visually displays the virtual environment and agent information received from the server on the user interface. Through this, the user can see the initial simulation status.

[0700] Step 4:

[0701] The server activates an emotion engine and recognizes emotions in real time through the user's camera input and voice. This identifies the user's emotions, such as happiness, dissatisfaction, and level of interest.

[0702] Step 5:

[0703] The server starts the simulation and has the agent perform the configured task. Meanwhile, it monitors the user's emotional information and adjusts the agent's behavior and task progress as needed.

[0704] Step 6:

[0705] The server collects agent performance data and analyzes it along with user emotion recognition data. For example, if the user is happy while the agent is working efficiently, the server applies a genetic algorithm to enhance that behavior.

[0706] Step 7:

[0707] The device presents the user with visual data based on simulation results and evolutionary processes. This allows the user to intuitively understand the agent's evolutionary process.

[0708] Step 8:

[0709] Users can control the direction of evolution by manually adjusting evolutionary parameters and simulation settings using the device's interface, or by turning the automatic adjustment function provided by the emotion engine on or off.

[0710] Step 9:

[0711] The server incorporates user feedback and adjustments, preparing for the next evolutionary cycle. This aims to generate more advanced and user-adaptive intelligent agents.

[0712] (Example 2)

[0713] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0714] The problem this invention aims to solve is to achieve more adaptive and advanced agent performance by enabling dynamic adjustments that appropriately reflect user emotional information during the evolution process of artificial intelligence agents in virtual space. Furthermore, since conventional methods have the problem of not being able to adequately reflect user feedback, this invention aims to improve real-time interaction.

[0715] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0716] In this invention, the server includes means for generating multiple artificial intelligence agents in a virtual space and assigning different attributes to each agent; means for recognizing the user's emotional information in real time and dynamically adjusting the agent's behavior and evolution algorithm settings; and means for providing an operation screen for the user to adjust the evolution algorithm settings. This makes it possible to realize more real-time and personalized agent evolution utilizing the user's emotional information.

[0717] An "artificial intelligence agent" is a program that autonomously performs specific tasks in a virtual space and continuously improves its performance based on evolutionary algorithms.

[0718] An "evolutionary algorithm" is a computational method inspired by natural selection and genetics, used to adaptively inherit and improve the characteristics of artificial intelligence agents.

[0719] "Emotional information" refers to the real-time emotional state obtained from the user's facial recognition, voice analysis, etc., and is used to dynamically adjust the behavior of artificial intelligence agents.

[0720] A "virtual space" is an artificial environment created on a computer system, which serves as the stage for artificial intelligence agents to operate.

[0721] The "operation screen" refers to the interface that users use to adjust system settings and agent evolution algorithms, and is a screen that provides visual information.

[0722] This invention constitutes a system related to the evolution of artificial intelligence agents in a virtual space. The server constructs a virtual space using a generative AI model and randomly generates multiple artificial intelligence agents within it. These agents are configured to have different attributes and operate autonomously within an environment set by the user.

[0723] The server uses facial recognition software and voice analysis tools to recognize user emotional information in real time. Methods such as OpenCV and speech recognition APIs are used for this purpose. User emotional information (e.g., joy, surprise, concentration) is important for dynamically adjusting the agent's behavior and evolutionary algorithm settings.

[0724] The terminal visually presents the user with simulation results from the server and the agent's evolution process. Available 3D graphics libraries (e.g., Unity) animate the agent's activities to make it easier for the user to understand the changes. It also includes a feature that allows the user to provide feedback on how their emotions are perceived and how they affect the agent.

[0725] Users can adjust the settings of the evolutionary algorithm through the device's interface and turn on or off features that utilize emotional information. This allows users to incorporate their individual feedback into system improvements.

[0726] As a concrete example, consider a scenario where a user utilizes this system on an online education platform. When faced with a difficult problem, the system recognizes the user's confusion through facial expression analysis. The server then adjusts the educational agent's explanation method, evolving the agent to provide clearer hints.

[0727] An example of a prompt is, "Create a program that evolves the agent's optimal response method based on the user's sentiment information." Using this prompt, the system can provide an agent that is more tailored to specific user needs.

[0728] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0729] Step 1:

[0730] The server first generates a virtual space. This virtual space is dynamically constructed using a generative AI model. It receives user environment configuration data as input and generates elements of the virtual space (terrain, weather, object placement, etc.) based on this data. The output is the generated virtual environment information, which is passed on to the next agent generation step.

[0731] Step 2:

[0732] The server generates artificial intelligence agents within a virtual space. The inputs used are the virtual environment information generated in step 1 and the agent's initial configuration data (e.g., characteristics and behavioral patterns). Based on this, each agent is configured to have different characteristics. The output is the generated data for the initialized agents, enabling them to operate in the virtual space. Specifically, the agents prepare to begin executing tasks.

[0733] Step 3:

[0734] The server recognizes user emotions in real time. It takes user facial recognition data and voice data as input, analyzes them with an emotion engine, and identifies emotional states such as joy, surprise, and concentration. Image processing libraries and voice analysis APIs are used to process this data. The output is the recognized user emotion information, which is used to adjust the agent's behavior.

[0735] Step 4:

[0736] The server dynamically adjusts the agent's behavior and evolution algorithm settings based on the user's sentiment information. Inputs include the sentiment information obtained in step 3 and the current agent performance data. Through data calculations, the agent's characteristics and behavioral parameters are updated. The output of the adjusted settings is applied to the next evolutionary cycle and execution. Specifically, the agent changes to behave in a way that better aligns with the user's expectations.

[0737] Step 5:

[0738] The terminal visually presents the user with simulation results and agent evolution status received from the server. It receives agent activity data and evolution parameters from the server as input. Based on this data, an animation is generated using a 3D graphics library and presented to the user. The output is visualized information, allowing the user to understand the progress of the simulation.

[0739] Step 6:

[0740] The user changes system settings through the terminal's interface. This step involves reviewing the basic settings of the evolutionary algorithm and selecting whether to use emotional information (on or off). The user's input, configured via the terminal, is then incorporated. This adjusts the system's behavior, enabling the development of more personalized agents. The output consists of settings based on the user's selections, which are then applied to the next cycle.

[0741] (Application Example 2)

[0742] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0743] In today's online shopping environment, providing personalized experiences that appropriately reflect the user's emotional state is challenging. Furthermore, in the evolution process of intelligent agents, directly incorporating user feedback is necessary to promote the development of more flexible and efficient intelligent agents.

[0744] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0745] In this invention, the server includes means for generating multiple intelligent programs in a virtual space and giving each program different properties; means for having the intelligent programs perform tasks in the virtual space and evaluating the results; means for inheriting and evolving the properties of superior intelligent programs to the next generation using a genetic method based on the evaluation results; means for analyzing the user's mental state and dynamically adjusting the operation of the intelligent programs based on that information; and means for adjusting product recommendations and displays in a virtual store based on the user's mental state. This enables a personalized shopping experience that takes into account the user's emotional state, and makes it possible to realize a more high-performance intelligent agent by utilizing user feedback in the agent's evolution process.

[0746] A "virtual space" is an artificial three-dimensional space created using computer technology, an environment in which users can interact.

[0747] An "intelligent program" is a program designed to solve a specific problem, which operates autonomously and produces results.

[0748] "Nature" refers to the specific characteristics or abilities that an intelligent program possesses, which influence its operation and results.

[0749] "Means of evaluating results" refer to methods for quantitatively or qualitatively measuring the outcomes achieved by an intelligent program and determining its effectiveness.

[0750] A "genetic method" is an algorithm based on the theory of biological evolution, used to select programs with superior traits, pass them on to the next generation, and improve them.

[0751] "Means of analyzing mental state" refers to techniques that analyze emotional indicators such as the user's facial expressions and voice to recognize their psychological state.

[0752] "Dynamic adjustment methods" refer to methods of adaptively changing the behavior and display of a program based on real-time changing circumstances and data.

[0753] "Means of adjusting product recommendations and displays" refers to methods of changing product displays and presentations to provide optimal information according to the user's situation and interests.

[0754] This system provides a personalized user experience through the evolutionary process of intelligent programs operating in a virtual space. The server generates the virtual space and randomly places multiple intelligent programs within it. Each program has different properties and autonomously performs tasks under conditions set by the user.

[0755] The server uses software such as OpenCV and emotion recognition libraries to acquire video and audio from the user's terminal, analyze the data, and recognize the user's mental state in real time. Based on this information, it dynamically adjusts the operation of intelligent programs and the parameters of evolutionary algorithms. For example, if the user shows interest, the products suggested in the virtual store can be adjusted according to that emotion.

[0756] The terminal visually presents the user with the simulation results and evolutionary process transmitted from the server. Furthermore, users can customize the numerical values ​​of the evolutionary method through their interface, thereby further enhancing their individual experience.

[0757] As a concrete example, let's say a user visits a virtual store using their smartphone. In this case, the smartphone's camera captures the user's facial expressions, and an emotion recognition library analyzes this data to recognize feelings of joy or interest. Based on these results, the server can recommend the latest fashion items or other products that might interest the user. An example of a prompt might be, "If the user is happy, recommend the latest fashion trend items."

[0758] In this way, this system uses emotional information to improve the user experience and effectively promotes the evolution of intelligent programs.

[0759] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0760] Step 1:

[0761] The server generates a virtual space and randomly places multiple intelligent programs within it. The input is user-defined environmental conditions, and the output is the initial state of the virtual space. Random properties are assigned to the intelligent programs, and the simulation begins in the virtual space.

[0762] Step 2:

[0763] The user's device captures facial expressions and audio data using its built-in camera and microphone. The input data consists of raw video and audio, while the output is in a format that can be processed. Specifically, the camera acquires video and the microphone collects audio.

[0764] Step 3:

[0765] The server uses an emotion recognition library to analyze the user's mental state from facial expressions and audio data. The input is the video and audio data acquired in step 2, and the output is the user's emotion information. The analysis program performs facial expression template matching and audio tone analysis.

[0766] Step 4:

[0767] The server dynamically adjusts the behavior of the intelligent program and the parameters of its evolutionary algorithm based on the analyzed emotional information. The input is the emotional information obtained in step 3, and the output is the adjusted program's behavioral guidelines. Specifically, the program's behavioral parameters are changed in real time.

[0768] Step 5:

[0769] The terminal visually presents the adjusted simulation results to the user. The input is the result of the intelligent program's operation adjusted in step 4, and the output is the user's visual feedback. Specifically, the latest simulation status is displayed on the screen.

[0770] Step 6:

[0771] Users can customize the numerical values ​​of the evolutionary method through the interface provided by the device. Input is a request for setting changes made by the user, and output is the updated parameters of the evolutionary algorithm. Users adjust the evolutionary parameters by operating the touchscreen or buttons.

[0772] Step 7:

[0773] The server repeats the above process, generating simulation results of the evolved intelligent program. Using the AI ​​model, it creates prompt messages and, if the user is pleased, presents information to the user, for example, "Please recommend the latest fashion trend items."

[0774] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0775] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0776] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0777] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0778] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0779] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0780] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0781] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0782] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0783] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0784] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0785] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0786] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0787] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0788] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0789] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0790] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0791] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0792] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0793] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0794] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0795] The following is further disclosed regarding the embodiments described above.

[0796] (Claim 1)

[0797] This includes methods for generating multiple intelligent agents within a virtual environment and assigning different characteristics to each agent,

[0798] A means for having an intelligent agent perform a task within the virtual environment and for evaluating its performance,

[0799] Based on the evaluation results, a means of inheriting and evolving the characteristics of superior intelligent agents to the next generation using a genetic algorithm,

[0800] A means of providing an interface for users to adjust the parameters of an evolutionary algorithm,

[0801] A means for visualizing the performance of each generation of intelligent agents during the evolutionary process and displaying the evaluation results,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, further comprising means for configuring and creating a virtual environment through multiple tasks and environmental parameters based on user selection.

[0805] (Claim 3)

[0806] The system according to claim 1, characterized in that the generation, evolution, and evaluation of the intelligent agent are performed on a server, and the results are transmitted from the server to the user's terminal.

[0807] "Example 1"

[0808] (Claim 1)

[0809] A means for generating a virtual environment in a computing device, creating multiple intelligent agents within the virtual environment, and assigning different attributes to each agent,

[0810] A means for having each intelligent agent perform a task within the virtual environment and for evaluating the results thereof,

[0811] Based on the evaluation results, a means for inheriting and developing the attributes of superior intelligent agents to the next generation using an evolutionary algorithm,

[0812] A means of providing a display screen for the user to adjust the settings of the evolutionary algorithm,

[0813] A means to visualize the performance of each generation of intelligent agents during the evolutionary process and output the results,

[0814] Based on user observations, a means to analyze the behavioral patterns of a specific intelligent agent and improve efficiency in subsequent simulations,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, further comprising means for defining a virtual environment through the setting of multiple tasks and environments according to the user's choice.

[0818] (Claim 3)

[0819] The system according to claim 1, characterized in that it performs the generation, development, and evaluation of the intelligent agent using a computing device, and transmits the results from the computing device to the user's terminal.

[0820] "Application Example 1"

[0821] (Claim 1)

[0822] A means of generating multiple intelligent agents within a virtual environment and giving each agent different characteristics,

[0823] A means for having an intelligent agent perform a task within the virtual environment and for evaluating its performance,

[0824] Based on the evaluation results, a means of inheriting and evolving the characteristics of superior intelligent agents to the next generation using a genetic algorithm,

[0825] A means of providing an interface for users to adjust the parameters of an evolutionary algorithm,

[0826] A means for visualizing the performance of each generation of intelligent agents during the evolutionary process and displaying the evaluation results,

[0827] A means of simulating traffic patterns in urban environments and providing users with optimization suggestions,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, further comprising means for configuring and creating a virtual environment through multiple tasks and environmental parameters based on user selection.

[0831] (Claim 3)

[0832] The system according to claim 1, characterized in that it performs the generation, evolution, and evaluation of the intelligent agent using an information processing device, and transmits the results from the information processing device to the user's terminal.

[0833] "Example 2 of combining an emotion engine"

[0834] (Claim 1)

[0835] A means of generating multiple artificial intelligence agents in a virtual space and assigning different attributes to each agent,

[0836] A means for having an artificial intelligence agent perform a task within the virtual space and for evaluating its performance,

[0837] Based on the evaluation results, a means to inherit and evolve the attributes of a superior artificial intelligence agent to the next generation using an evolutionary algorithm,

[0838] A means of recognizing user emotional information in real time and dynamically adjusting the agent's behavior and evolutionary algorithm settings,

[0839] A means of providing a user interface for adjusting the settings of the evolutionary algorithm,

[0840] A means for visualizing the results of each generation of artificial intelligence agents during the evolutionary process and displaying the evaluation results,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, further comprising means for setting up and creating a virtual space through multiple tasks and environmental conditions based on user selection.

[0844] (Claim 3)

[0845] The system according to claim 1, characterized in that it performs the generation, evolution, and evaluation of the artificial intelligence agent on a computer and transmits the results from the computer to the user's terminal.

[0846] "Application example 2 when combining with an emotional engine"

[0847] (Claim 1)

[0848] A means of generating multiple intelligent programs in a virtual space and giving each program different properties,

[0849] A means for having an intelligent program perform a task within the virtual space and for evaluating the results thereof,

[0850] Based on the evaluation results, a means to inherit and evolve the properties of excellent intelligent programs to the next generation using genetic methods,

[0851] A means of providing a communication surface for the user to adjust the numerical values ​​of the evolutionary method,

[0852] A means for visualizing the performance of each generation of intelligent programs during the evolutionary process and displaying the evaluation results,

[0853] A means for analyzing the user's mental state and dynamically adjusting the operation of an intelligent program based on that information,

[0854] A means of adjusting product recommendations and displays in a virtual store based on the user's mental state,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, further comprising means for setting and creating a virtual space through multiple tasks and environmental values ​​based on the user's selection.

[0858] (Claim 3)

[0859] The system according to claim 1, characterized in that it performs the generation, evolution, and evaluation of the intelligent program using a computer and transmits the results from the computer to the user's device. [Explanation of Symbols]

[0860] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of generating multiple intelligent agents within a virtual environment and giving each agent different characteristics, A means for having an intelligent agent perform a task within the virtual environment and for evaluating its performance, Based on the evaluation results, a means of inheriting and evolving the characteristics of superior intelligent agents to the next generation using a genetic algorithm, A means of providing an interface for users to adjust the parameters of an evolutionary algorithm, A means for visualizing the performance of each generation of intelligent agents during the evolutionary process and displaying the evaluation results, A means of simulating traffic patterns in urban environments and providing users with optimization suggestions, A system that includes this.

2. The system according to claim 1, further comprising means for setting up and creating a virtual environment through multiple tasks and environmental parameters based on user selection.

3. The system according to claim 1, characterized in that it performs the generation, evolution, and evaluation of the intelligent agent using an information processing device, and transmits the results from the information processing device to the user's terminal.

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