System

Abstract

A system is provided.SOLUTION: A system including means for a user to input specifications of a city model to be used in a simulation to a terminal, means for the terminal to transmit the specification data to a server, means for the server to generate a fictitious city model using a generated AI based on the specification data, means for the server to store the generated city model in a database, means for the server to transmit the generated city model to the terminal, and means for the terminal to execute a simulation based on the generated city model.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Simulations using actual city data in fields such as autonomous driving, map apps, urban development, and disaster prevention pose several challenges. First, each city has its own unique characteristics, making it difficult to obtain average or representative data. This can lead to evaluation results that tend to favor specific cities and lack fairness. Furthermore, in simulations of disasters and environmental issues, using actual cities as models can lead to reputational damage. This can discourage residents and local governments from using simulations. Furthermore, collecting and updating actual city data is costly and time-consuming, making it difficult to update frequently. This invention aims to solve these problems. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems, the present invention provides a system having the following features. The system of the present invention includes a means for a user to input specifications of a city model to be used in a simulation into a terminal, a means for the terminal to transmit the specification data to a server, a means for the server to generate a fictitious city model based on the specification data using a generation AI, a means for the server to store the generated city model in a database, a means for the server to transmit the generated city model to the terminal, and a means for the terminal to execute a simulation based on the generated city model. This makes it possible to generate a large number of fictitious city models quickly, allowing for diverse and fair simulations to be executed. Furthermore, it is possible to avoid reputational damage and update city data with reduced cost and time.

[0006] "Device" refers to the computer or mobile device used by a user to submit input data, view a city model, or run a simulation.

[0007] A "server" refers to a remote computer or system that generates a fictional city model using generative AI, stores it in a database, and sends it back to the terminal.

[0008] "User" refers to a person or organization that inputs the specifications of the city model to be used in the simulation and operates the terminal to run the simulation.

[0009] "Specification data" refers to information that indicates the conditions and characteristics of the fictional city model that the user needs for the simulation, and is data that is sent from the terminal to the server.

[0010] "Generative AI" refers to artificial intelligence technology that runs on a server and generates fictional city models based on specification data.

[0011] A "fictional city model" refers to a fictitious city model created by generative AI that includes detailed population, climate, geographic, and resident data, even though it does not actually exist.

[0012] "Database" refers to an information storage system for storing the generated fictional city model and its associated data and retrieving them as needed.

[0013] "Urban model specifications" refer to the specific requirements, such as population, climate, and geographical characteristics, that the user sets for the fictional city during the simulation.

[0014] "Simulation" refers to the computational process of virtually recreating a specific product or situation (e.g., autonomous driving, disaster risk assessment) using a generated fictional city model to evaluate its performance and impact. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0029] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention is a system for generating and simulating fictitious city models that can be used in fields such as autonomous driving, map applications, urban development, and disaster prevention. This system generates a large number of fictitious city models at high speed based on the specifications of the city model entered by the user, and enables various simulations to be performed using the models.

[0037] System configuration

[0038] 1. User Input

[0039] The user inputs the specifications of the city model to be used in the simulation into the terminal. For example, the user specifies "a city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure."

[0040] 2. Sending specification data

[0041] The device sends the specification data entered by the user to the server, packaged in JSON or XML format, and sent as an HTTP request.

[0042] 3. Data Generation

[0043] The server analyzes the received specification data and generates a fictional city model using a generative AI. The generated data includes the following information:

[0044] Population data: population, age structure, occupational distribution

[0045] Climate data: average annual temperature, precipitation

[0046] Geographic data: road networks, public transport

[0047] Resident data: Resident profile, lifestyle

[0048] 4. Save the city model

[0049] The server saves the generated city model in a database, where it can be referenced for later use in simulations.

[0050] 5. Submitting the Model

[0051] The server sends the generated city model back to the device, where the data is converted back into JSON or XML format and sent as an HTTP response.

[0052] 6. Running the Simulation

[0053] The device interprets the city model data received from the server and displays it on the user interface. The user can then configure the simulation settings based on this data and start the simulation engine to run simulations of various scenarios (e.g., performance evaluation of autonomous vehicles, disaster risk assessment, etc.).

[0054] Specific examples

[0055] For example, suppose a user uses the system to evaluate a new self-driving car algorithm. In this case, the user inputs the following specifications into their device: "A city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure." The specifications are then sent to the server. Based on this, the server uses generative AI to generate a fictitious city model and stores it in a database. The generated city model is then sent to the device, and the user runs a self-driving car simulation based on this city model. By analyzing the results of the simulation, the user can evaluate the performance of the new algorithm under fair and diverse conditions.

[0056] This system makes it possible to quickly run fair and diverse simulations without relying on real city data. Furthermore, it can avoid the reputational damage that can occur when using real cities in simulations such as disaster risk assessments. This makes it possible to provide a practical and ethical simulation environment.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The user inputs the specifications of the fictional city model to be used in the simulation into the device, such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" into an application on the device or a web browser input form.

[0060] Step 2:

[0061] The terminal packages the input specification data, organizes it in JSON or XML format, and converts it into a format that is easy to use for subsequent server processing.

[0062] Step 3:

[0063] The terminal sends the packaged specification data to the server as an HTTP request. Specifically, when the send button is clicked, the terminal sends the data to the specified URL of the server as a POST request.

[0064] Step 4:

[0065] The server receives the HTTP request and parses the requested specification data. Specifically, it deserializes the received data and stores it on the server as a JSON object or XML document.

[0066] Step 5:

[0067] The server launches a generation AI based on the analyzed specification data, which then begins the process of generating a fictional city model based on the specified population size, regional characteristics, and climatic conditions.

[0068] Step 6:

[0069] The server-based generation AI generates detailed population data (population count, age structure, occupational distribution), climate data (average annual temperature, precipitation), geographic data (road network, public transportation), and resident data (resident profiles, lifestyles).The generation AI uses existing datasets and statistical models to perform probabilistic and rule-based generation.

[0070] Step 7:

[0071] The server integrates the various data generated and compiles a fictional city model into a single dataset. Specifically, it combines each individual data set to create a consistent city model.

[0072] Step 8:

[0073] The server stores the integrated fictional city model in a database, where the data is indexed for easy search and access later.

[0074] Step 9:

[0075] The server sends the saved fictional city model to the terminal. Specifically, it reconverts the city model into JSON or XML format and sends it to the terminal as an HTTP response.

[0076] Step 10:

[0077] The terminal receives the transmitted city model data and displays it on the user interface. Specifically, it interprets the received data and displays it on the GUI (Graphical User Interface) as data for display.

[0078] Step 11:

[0079] The user checks the displayed city model and configures the simulation, for example, entering parameters to start a simulation of an autonomous vehicle.

[0080] Step 12:

[0081] When the user presses the simulation start button, the device starts the simulation engine, which then begins calculations based on the generated city model.

[0082] Step 13:

[0083] The device runs the simulation and generates results, which are calculated and visualized as performance data and risk assessments.

[0084] Step 14:

[0085] The terminal displays the calculation results to the user, who can then evaluate and analyze them.

[0086] Example 1

[0087] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0088] There is a need for a system that allows users to easily input specifications for the city model to be used in the simulation and quickly generate a large number of fictitious city models. The generated city models must also contain realistic data, which will improve the accuracy of various simulations. Furthermore, there is a need for a system that allows users to easily run simulations based on the generated city models and use the results for evaluation and analysis.

[0089] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0090] In this invention, the server includes means for a user to input specifications of a city model to be used in a simulation into a user device, means for the user device to transmit the specification data to the server, means for the server to generate a fictitious city model using a generative model based on the specification data, means for the server to save the generated city model in a storage device, means for the server to transmit the generated city model to the user device, and means for the user device to execute a simulation based on the generated city model. This makes it possible to quickly generate a variety of city models tailored to each user and execute realistic simulations.

[0091] A "user device" is a device through which a user inputs specifications for a city model to be used in a simulation, and is an electronic device such as a computer or smartphone.

[0092] "Specification data" is data that describes the characteristics and conditions of a city model entered by the user, and specifically includes information such as population, climate, and geographical conditions.

[0093] A "server" is a computer system that receives specification data sent from user devices and generates, stores, and transmits analysis and city models.

[0094] A "generative model" is an algorithm or AI technology that generates a fictional city model based on specified specification data.

[0095] A "city model" is a digital representation of a fictional city generated based on specification data, and is a dataset that includes population data, climate data, geographic data, resident data, etc.

[0096] A "storage device" is a database or storage system for saving the city model generated by the server.

[0097] "Simulation" is the process of conducting virtual experiments and analyses based on the generated urban model.

[0098] This invention is a system for generating and simulating fictional city models that can be used in fields such as autonomous driving, map applications, urban development, and disaster prevention. The system operates by combining hardware and software, including user devices, servers, generative models, and storage devices.

[0099] To use the system, a user first accesses their device and logs in. The user then fills in the specifications for the city model in an input form provided by the system. For example, the user specifies a city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure. This specification data is converted into JSON or XML format and sent to the server via an HTTP request.

[0100] The server receives the HTTP request and analyzes the specification data. Based on this analyzed data, the server uses a generative model to generate a fictional city model. For example, OpenAI GPT-3 is used as the generative model. The generative model prompt uses text such as "Generate a city model with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure."

[0101] The generated city model includes various information such as population data (number of people, age structure, occupational distribution), climate data (average annual temperature, precipitation), geographic data (road network, public transportation), and resident data (resident profiles, lifestyles). This data is stored in the server's storage device.

[0102] The saved city model data is converted back to JSON or XML format and sent to the user device as an HTTP response. The user device receives this data, interprets it, and displays it on the user interface, allowing the user to visually check an overview of the generated city model.

[0103] After the city model is displayed on the user device, the user configures the simulation settings, for example, entering conditions for evaluating the algorithms of a self-driving car. When the user clicks the "Start Simulation" button, the simulation engine is launched and the specified scenario is simulated. The simulation results are reflected in the user interface, allowing the user to evaluate and analyze them.

[0104] As a concrete example, consider the procedure for a user evaluating a new autonomous vehicle algorithm. The user inputs the specifications for a city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure, and sends them to a server. The server uses the generative model to generate a fictitious city model and saves it to a storage device. The generated city model is then sent back to the user's device, and the user runs a simulation based on this city model.

[0105] This system allows users to quickly run fair and diverse simulations without relying on real city data. It also helps to avoid reputational damage that can occur when using real cities for disaster risk assessments, and provides an ethical simulation environment.

[0106] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0107] Step 1:

[0108] The user accesses a terminal and logs in. The user enters the specifications of the city model for the simulation into an input form. For example, the user enters the specifications as "a city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure." Input data: City model specifications. Output data: Specification data converted into JSON or XML format.

[0109] Step 2:

[0110] The terminal converts the specification data entered by the user into JSON or XML format. The terminal generates an HTTP request and sends this specification data to the server. Input data: City model specifications entered by the user. Output data: Specification data in JSON or XML format sent to the server.

[0111] Step 3:

[0112] The server receives the HTTP request and extracts the specification data. The server parses the specification data to detect the required parameters. Input data: Specification data in JSON or XML format sent as the HTTP request. Output data: Parsed parameter set.

[0113] Step 4:

[0114] The server calls the API of the generating AI (e.g., OpenAI GPT-3). The prompt states, "Generate a city model with a population of 500,000, a hot and humid climate, and extensive public transportation infrastructure." Input data: The analyzed parameter set. Output data: The fictitious city model data returned by the generating AI.

[0115] Step 5:

[0116] The server verifies the generated city model data and checks for errors. If there are no errors, it saves it to a database. For example, PostgreSQL or MongoDB is used. Input data: City model data provided by the generation AI. Output data: City model data saved in the database or an error log.

[0117] Step 6:

[0118] The server converts the saved city model data back into JSON or XML format and packages it as an HTTP response. The server sends the HTTP response to the user's device. Input data: City model data saved in the database. Output data: City model data in JSON or XML format sent to the user's device.

[0119] Step 7:

[0120] The terminal receives the HTTP response from the server and interprets the city model data. The terminal displays an overview of the city model in the user interface. Input data: City model data in JSON or XML format sent from the server. Output data: An overview of the city model displayed in the user interface.

[0121] Step 8:

[0122] The user sets up the simulation, for example by filling out a form to evaluate an autonomous vehicle algorithm. The user clicks the "Start Simulation" button. Input data: The city model displayed in the user interface and additional simulation settings. Output data: Simulation parameters sent to the simulation engine.

[0123] Step 9:

[0124] The terminal starts the simulation engine and runs a simulation of the specified scenario. The results of the simulation are displayed on the user interface. For example, data such as traffic volume and accident occurrence rate are displayed. Input data: Simulation parameters. Output data: Simulation results displayed on the user interface.

[0125] (Application example 1)

[0126] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0127] Evaluating autonomous vehicle algorithms fairly and efficiently under diverse environmental conditions is challenging. Using real-world urban data can lead to ethical issues and misinterpretations. Furthermore, relying on real urban environments limits the flexibility of simulations.

[0128] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0129] In this invention, the server includes: [means for a user to input specifications of the city model to be used in the simulation into a terminal;] [means for the terminal to transmit the specification data to the server; and] [means for the server to generate a fictitious city model using a generation AI based on the specification data.] This makes it possible to simulate autonomous vehicles using a fictitious city model. This makes it possible to simulate under a variety of environmental conditions, enabling fair and efficient evaluation without relying on real city data.

[0130] A "user" is a person or organization that operates the system and inputs specifications for the city model.

[0131] A "simulation" is an experiment or trial conducted in a virtual environment under specific conditions or settings.

[0132] A "city model" is a dataset of a virtual city that includes attributes such as population, climate, geography, and transportation infrastructure.

[0133] "Specification data" is data entered by the user that describes the conditions and characteristics of the city model.

[0134] A "terminal" is a device that allows a user to input specifications for a city model and run a simulation.

[0135] "Server" means a central processing unit that receives specification data, processes the data, and stores and transmits the generated city model.

[0136] "Generative AI" is an artificial intelligence technology that generates fictional city models based on specified specification data.

[0137] A "database" is an information system that stores the generated city models and makes them accessible as needed.

[0138] "3D display" is the process of displaying the generated city model in a 3D visual format.

[0139] An "autonomous vehicle" is a vehicle that has the ability to drive autonomously without human operation.

[0140] "Transportation Data" means information about roads, public transportation, traffic volumes and patterns within a city.

[0141] "Infrastructure data" is information about a city's basic public facilities and systems (e.g., transportation, electricity, water supply).

[0142] "Fictional resident profiles" are data describing the characteristics and attributes of residents living in a virtual city.

[0143] "Traffic simulation data" refers to data that represents the results of a simulation based on traffic conditions and scenarios.

[0144] This invention is a system that generates a fictitious city model based on specifications for the city model entered by a user and uses the model to simulate an autonomous vehicle. The system includes a terminal operated by the user, a server that sends and receives data, a generation AI that generates the city model, and a database that stores and manages the generated city model.

[0145] System configuration

[0146] 1. User Input

[0147] Users input specifications for the city model they want to use in the simulation into the device, including attributes such as population, climate, and transportation infrastructure. For example, they might input, "Please generate a hot, humid city with a population of 500,000. I want to simulate a scenario with autonomous vehicles in an environment with extensive public transportation infrastructure."

[0148] 2. Sending specification data

[0149] The terminal sends the entered specification data to the server, which packages the data in JSON or XML format and sends it as an HTTP request. The server receives it and analyzes it.

[0150] 3. City model generation

[0151] The server generates a fictional city model using generative AI based on the received specification data. This generative AI is implemented using TensorFlow and Python. The generated city model includes population data, climate data, geographic data, resident data, traffic data, and infrastructure data.

[0152] 4. Data storage

[0153] The generated city model is saved in a database such as PostgreSQL, making it reusable for future simulations.

[0154] 5. Submitting the Model

[0155] The server sends the saved city model to the device, where it is converted back into JSON or XML format and sent as an HTTP response. The device receives and interprets this data.

[0156] 6. Running the Simulation

[0157] The device then runs a simulation based on the received city model. Specifically, it displays a city model generated using Three.js or similar software in three dimensions, and then simulates autonomous vehicles from that. The simulation includes scenarios such as traffic congestion assessment, accident risk assessment, and route optimization.

[0158] Specific examples

[0159] For example, if a user inputs a specification such as "a city with a population of 1 million, a dry climate, and an advanced public transportation infrastructure," the system can generate a fictional city model based on that specification and run simulations of autonomous vehicles in this city. This simulation allows the performance of new autonomous driving algorithms to be evaluated under a variety of conditions.

[0160] An example of a prompt to input to a generative AI model is as follows:

[0161] "Generate a hot, humid city with a population of 500,000. Simulate autonomous vehicle scenarios in an environment with extensive public transport infrastructure."

[0162] By implementing this invention, it is possible to quickly perform fair and diverse simulations without relying on real city data. Furthermore, it is possible to avoid reputational damage that can occur when using real cities in simulations such as disaster risk assessment. This makes it possible to provide a fair and practical simulation environment.

[0163] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0164] Step 1:

[0165] User input of specification data

[0166] The user inputs the specifications of the city model to be used in the simulation into the terminal, such as the population, climate, public transportation infrastructure, etc. At this time, the input data is collected through a form on the terminal.

[0167] Input: User-supplied specification data (e.g., population 500,000, high temperature and humidity, extensive public transport infrastructure)

[0168] Output: Specification data (e.g., data packaged in JSON format)

[0169] Step 2:

[0170] Sending specification data to the server

[0171] The terminal sends the specification data entered by the user to the server as an HTTP request.

[0172] Input: Specification data (JSON format)

[0173] Output: Specification data received by the server

[0174] Step 3:

[0175] The server analyzes the specification data and starts the generation AI

[0176] The server analyzes the received specification data and generates a fictional city model using generative AI, which uses Python and TensorFlow. Based on the specification data, the generative AI generates population data, climate data, geographic data, resident data, traffic data, and infrastructure data.

[0177] Input: Received specification data

[0178] Output: Generated city model (including various data)

[0179] Step 4:

[0180] Saving the generated city model

[0181] The server stores the generated city model in a database such as PostgreSQL.

[0182] Input: Generated city model

[0183] Output: City model stored in a database

[0184] Step 5:

[0185] Providing the generated city model

[0186] The server then sends the saved city model to the device, again as an HTTP response.

[0187] Input: City model stored in a database

[0188] Output: The city model received by the device

[0189] Step 6:

[0190] 3D display of city models on terminals

[0191] The device then runs a simulation based on the received city model, displaying the city model in three dimensions using Three.js.

[0192] Input: Received city model

[0193] Output: 3D city model

[0194] Step 7:

[0195] Running simulations of autonomous vehicles

[0196] The device simulates autonomous vehicles within a 3D city model, including traffic congestion assessment, accident risk assessment, and route optimization.

[0197] Input: 3D city model, simulation settings (starting point, destination, etc.)

[0198] Output: Simulation results (e.g. traffic congestion map, accident risk assessment results, etc.)

[0199] This allows users to run simulations of autonomous vehicles under a variety of conditions. The entire system works in concert to achieve fair and efficient simulations that do not rely on real-world urban data.

[0200] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0201] This invention combines an emotion engine that recognizes the user's emotions with a system for generating and simulating fictional city models that can be used in fields such as autonomous driving, map applications, urban development, and disaster prevention. This system generates a fictional city model based on the specifications of the city model entered by the user and their emotional state at the time, and enables various simulations to be performed using that model.

[0202] System configuration

[0203] 1. User Input

[0204] The user inputs the specifications of the city model to be used in the simulation into the device. For example, the user inputs information such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" into an application on the device or an input form in a web browser. During this input, the emotion engine detects the user's emotional state through facial recognition and voice analysis.

[0205] 2. Sending specification data and emotion data

[0206] The device packages the specification data and emotion data entered by the user. The specification data and emotion data are organized in JSON or XML format and converted into a format that is easy to use for subsequent server processing. The device then sends the packaged data to the server as an HTTP request.

[0207] 3. Data Generation

[0208] The server receives the HTTP request and analyzes the requested specification data and emotion data. The analyzed data is passed to the generation AI, which then generates a fictional city model based on the specified population size, regional characteristics, and climatic conditions. During this process, emotion data is also taken into account, and the city model desired by the user is generated.

[0209] 4. Saving city models and emotion data

[0210] The server stores the generated city model and associated emotion data in a database, where the model and emotion data can be referenced for later use in simulations.

[0211] 5. Submitting the Model

[0212] The server sends the generated city model back to the device, where the data is again converted into JSON or XML format and sent as an HTTP response.

[0213] 6. Running the Simulation

[0214] The device interprets the city model data and emotion data received from the server and displays them on the user interface. The user then configures the simulation settings based on this information, launches the simulation engine, and runs simulations of various scenarios (e.g., performance evaluation of self-driving cars, disaster risk assessment, etc.). During this process, emotion data is also taken into account and reflected in the simulation results.

[0215] Specific examples

[0216] For example, suppose a user uses the system to evaluate a new self-driving car algorithm. In this case, the user inputs the following specifications into their device: "Population 500,000, hot and humid climate, extensive public transportation infrastructure" and sends them to the server. Based on this, the server uses generative AI to generate a fictional city model and stores it in a database. At the same time, the emotion engine collects and stores the user's emotional data at the time of input. The generated city model and emotional data are then sent to the device, and the user runs a self-driving car simulation based on this city model and emotional data. The results of this simulation are visualized as an evaluation result that takes into account the user's emotional state.

[0217] This system makes it possible to quickly perform fair and diverse simulations without relying on real-world city data. Furthermore, by combining it with an emotion engine, it is possible to obtain more realistic simulation results that take into account the user's emotional state, providing users with more intuitive and convincing results.

[0218] The processing flow will be explained below.

[0219] Step 1:

[0220] The user inputs the specifications of the fictional city model to be used in the simulation into the device, such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" into an application on the device or a form in a web browser.

[0221] Step 2:

[0222] When the device packages the input specification data, it activates the emotion engine, which uses the camera and microphone to recognize the user's face and analyze their voice to detect their emotional state (e.g., joy, sadness, excitement, etc.).

[0223] Step 3:

[0224] The device integrates the detected emotion data with the input specification data and packages it in JSON or XML format, allowing the specification data and emotion data to be treated as a single dataset.

[0225] Step 4:

[0226] The device sends the packaged data to the server as an HTTP request, and the sent data is sent as a POST request to the specified URL on the server.

[0227] Step 5:

[0228] The server receives the HTTP request and deserializes the sent data. The deserialized data is expanded within the server as specification data and emotion data.

[0229] Step 6:

[0230] The server receives the expanded specification data and emotion data and activates the generation AI. The generation AI generates the following data based on the specified population size, regional characteristics, and climatic conditions:

[0231] Population data (e.g., population numbers, age distribution, occupational distribution)

[0232] Climate data (e.g., average annual temperature, precipitation)

[0233] Geographic data (e.g. road networks, public transport)

[0234] Resident data (e.g., resident profiles, lifestyles)

[0235] Step 7:

[0236] The generation AI in the server uses the detected emotion data to adjust the city model to fit the user's emotional state. This process ensures that the generated city model is more in line with the user's intentions and emotions.

[0237] Step 8:

[0238] The server integrates the various data generated and compiles it into a single fictional city model, which is then organized into a coherent city model.

[0239] Step 9:

[0240] The server stores the integrated fictional city model and associated emotion data in a database, which is indexed for easy access and reference later.

[0241] Step 10:

[0242] The server sends the saved city model and emotion data to the device, where it is converted back into JSON or XML format and sent as an HTTP response.

[0243] Step 11:

[0244] The terminal receives the transmitted city model data and emotion data and displays them on the user interface. Based on this, the user can check the city model and set up the simulation.

[0245] Step 12:

[0246] When the user sets the simulation conditions and presses the start button, the device starts the simulation engine, which then begins calculation processing based on the generated city model and emotion data.

[0247] Step 13:

[0248] The device runs the simulation and generates results, which are calculated as performance data and risk assessments and visualized in a way that is intuitive to the user.

[0249] Step 14:

[0250] The device displays the calculation results to the user, who can then evaluate and analyze them. Emotional data is also reflected in the results, which are displayed based on the user's emotional state.

[0251] Example 2

[0252] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0253] When conducting simulations using virtual city models, it is necessary to quickly obtain highly realistic simulation results that take into account the user's emotional state. However, current technology makes it difficult to reflect the user's emotional data in the simulation. Therefore, it is necessary to realize a city model generation and simulation system that includes emotional data.

[0254] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0255] In this invention, the server includes: [means for generating a fictional city model using a generation AI based on the specification data and emotion data]; [means for storing the generated city model and emotion data in a database]; and [means for transmitting the generated city model and emotion data to the terminal.] This enables [the generation and simulation of a highly realistic city model that takes into account the user's emotion data].

[0256] "User" refers to a person or organization that uses this system to input specifications for a city model and run a simulation.

[0257] "Terminal" refers to a device used by a user to input specifications for a city model and communicate with the server, and includes computers, smartphones, tablets, etc.

[0258] "Specification data" refers to information about the city model that the user inputs into the terminal, and includes population data, climate data, geographical data, resident data, and the like.

[0259] "Emotion data" is data collected by the terminal's emotion engine from the user's facial expressions and voice, and includes information indicating the user's emotional state.

[0260] "Server" refers to the computer system that receives specification data and emotion data, generates a city model using generative AI, and stores and transmits that data.

[0261] "Generative AI" refers to artificial intelligence algorithms that generate fictional city models based on specification and emotion data.

[0262] A "city model" refers to a dataset that represents the structure and characteristics of a fictional city generated by generative AI based on specification data and emotion data.

[0263] "Database" refers to an information system for storing the generated city model and associated emotion data and retrieving them as needed.

[0264] "Simulation Engine" refers to the software or algorithms used to simulate various scenarios based on the generated city model and emotion data.

[0265] "JSON format" refers to a lightweight data exchange format for storing and transferring specification and sentiment data in a structured manner.

[0266] "HTTP request" refers to the communication protocol used when a terminal sends data to a server.

[0267] "HTTP response" refers to the communication protocol used when a server sends data to a terminal.

[0268] This invention is a system that generates a fictional city model based on the specifications of the city model entered by the user and the emotional state at the time, and enables various simulations to be performed using that model. By incorporating an emotion engine, this system can collect the user's emotional state in real time and provide more realistic simulations that reflect this.

[0269] Hardware and Software Configuration

[0270] 1. Terminal

[0271] The terminal is a device used by users to input specifications for the city model. Typical hardware includes computers, smartphones, and tablets. This terminal includes applications that implement various input forms and software that runs on a web browser. It also incorporates an emotion engine that collects emotion data from users through facial recognition and voice analysis.

[0272] 2. Server

[0273] The server receives the specification data and emotion data sent from the device and generates a fictional city model using generative AI based on this data. The server stores the generated city model and emotion data in a database and transmits them to the device as needed. The server requires a high-performance processing unit and large-capacity storage. Specific software includes a data analysis algorithm, generative AI model, and database management system.

[0274] Specific examples

[0275] For example, a user might use this system to evaluate a new self-driving car algorithm. The user would enter specifications for a city model, such as a population of 500,000, a hot and humid climate, and extensive public transportation infrastructure, into an application on the device or an input form in a web browser. At the same time, the device's emotion engine would perform facial recognition and voice analysis to collect emotion data such as "happiness" and "expectation."

[0276] The device then converts the specification data and emotion data into JSON format and sends it to the server as an HTTP request. The server then analyzes the received data and passes it to a generation AI to generate a fictional city model. The device also takes into account the user's emotion data, resulting in a city model that reflects more positive elements.

[0277] The generated city model and emotion data are stored in a database by the server. The server then transmits the stored data to the device, which interprets it and displays it on the user interface. The user uses this data to configure the simulation settings for evaluating the performance of the autonomous vehicle and launch the simulation engine. The simulation results also reflect the user's emotional state, making the evaluation more intuitive and convincing.

[0278] Prompt Sentence Examples

[0279] Urban specifications: 500,000 people, hot and humid climate, extensive public transport infrastructure

[0280] Emotion data: joy, anticipation

[0281] This system enables users to quickly run diverse and fair simulations without relying on real-world city data. Furthermore, the emotion engine provides simulation results that reflect the user's emotional state in real time, resulting in more intuitive and convincing results.

[0282] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0283] Step 1: User Input

[0284] The user inputs specifications for a city model using a device application or web browser. For example, they input information such as "population 500,000, hot and humid climate, extensive public transportation infrastructure." The device's built-in emotion engine also collects emotional data through facial recognition and voice analysis. The input is entered into a form on the device and then saved as specification data on the device.

[0285] input:

[0286] City model specifications (e.g., population 500,000, hot and humid climate, extensive public transport infrastructure)

[0287] The user's emotional state (e.g., joy, anticipation)

[0288] output:

[0289] Specification data and sentiment data in JSON or XML format

[0290] Step 2: Sending specification data and emotion data

[0291] The device packages the user's input specifications and emotion data and sends it to the server as an HTTP request, organized in JSON or XML format.

[0292] input:

[0293] Specification data and sentiment data in JSON or XML format

[0294] output:

[0295] HTTP request sent to the server

[0296] Step 3: Data generation

[0297] The server receives the HTTP request and analyzes the specification data and emotion data. The analyzed data is passed to the generation AI, which generates a fictional city model based on the specified population size, regional characteristics, and climatic conditions. Emotion data is also taken into account during this process, and a city model that matches the user's emotions is generated.

[0298] input:

[0299] Specification and sentiment data in HTTP requests

[0300] output:

[0301] Generated city model

[0302] Specific behavior:

[0303] The server parses the data using a JSON parser

[0304] Input the analyzed data into the generative AI

[0305] Generative AI generates fictional city models

[0306] Step 4: Saving the city model and emotion data

[0307] The server stores the generated city model and emotion data in a database, where it can be quickly accessed for later use in simulations.

[0308] input:

[0309] Generated city model and emotion data

[0310] output:

[0311] City models and emotion data stored in a database

[0312] Specific behavior:

[0313] The server establishes a database connection and stores the data via SQL or NoSQL

[0314] Step 5: Serve the model

[0315] The server sends the generated city model to the terminal as an HTTP response, which is again converted to JSON or XML format.

[0316] input:

[0317] City models and emotion data stored in a database

[0318] output:

[0319] HTTP response sent to the device

[0320] Specific behavior:

[0321] The server retrieves the data from the database

[0322] Convert data to JSON or XML format

[0323] Send to the terminal as an HTTP response

[0324] Step 6: Run the simulation

[0325] The device interprets the received city model data and emotion data and displays them on the user interface. The user configures the simulation settings, starts the simulation engine, and executes various simulations. The simulation results, which reflect the user's emotional state, are displayed intuitively.

[0326] input:

[0327] City model and emotion data received as an HTTP response

[0328] output:

[0329] Simulation results

[0330] Specific behavior:

[0331] The device analyzes the city model data and visualizes it on the user interface.

[0332] The user configures the simulation settings and inputs the settings data into the simulation engine.

[0333] The simulation engine runs various scenarios and displays the results in the user interface.

[0334] The above is a detailed description of each processing step. This system allows users to quickly generate and simulate realistic city models that reflect their emotional states.

[0335] (Application example 2)

[0336] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0337] Conventional simulation systems do not take into account the user's emotional state, and therefore are unable to provide highly accurate simulation results that meet the user's intuition and desires. Furthermore, because they rely on specific city data, the simulation lacks fairness and diversity. Therefore, there is a need to provide simulation results that are more convincing to users.

[0338] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0339] In this invention, the server includes: [means for generating a fictional city model using a generation AI based on the specification data and emotion data]; [means for storing the generated city model and emotion data in a database]; and [means for transmitting the generated city model and emotion data to the terminal.] This makes it possible to [generate fair and diverse city models that reflect the user's emotions and provide highly accurate simulation results].

[0340] A "user" is a person or organization that inputs specification data and emotion data to generate a fictional city model using the simulation system.

[0341] A "terminal" is a device that allows a user to input specification data and emotion data and exchange data with the server. This includes smartphones, PCs, tablets, etc.

[0342] "Specification data" is information that indicates the characteristics and conditions of the city model used in the simulation, and includes data on population, climate, transportation infrastructure, etc.

[0343] "Emotional data" refers to information that reflects the user's emotional state, and includes data obtained through facial recognition and voice analysis.

[0344] A "server" is a device or system that receives specification data and emotion data sent from a terminal, generates a fictional city model, stores it in a database, and then returns the data to the terminal.

[0345] "Generative AI" includes artificial intelligence models for generating fictional city models based on specification data and emotion data.

[0346] A "fictional city model" is not a real city, but is digital data that virtually represents the structure, population, climate, etc. of a city created for simulation purposes.

[0347] The "database" is a system that systematically stores the generated city models and emotion data, making them available for reference as needed.

[0348] "Simulation" is the process of predicting movements and situations under specific conditions based on a generated fictional city model and analyzing the results.

[0349] A "simulation scenario" is generated based on the user's emotional data and specification data, and indicates the assumptions and specific situations used in the simulation.

[0350] MODE FOR CARRYING OUT THE INVENTION

[0351] To put this invention into practice, it is necessary to build a city model generation system based on emotion data. A system is realized in which users, terminals, and servers work together to generate a fictional city model that reflects the user's emotions and perform a simulation.

[0352] System Configuration

[0353] 1. User Input

[0354] The user inputs the specifications of the city model to be used in the simulation and the emotional data at the time of input into the device. For example, the user can input specification data such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" via an application on the device or a web browser.

[0355] 2. Collecting Emotional Data

[0356] The device collects the user's emotional data along with the device's specifications. Specifically, it uses facial recognition and speech analysis APIs to detect the user's emotional state and captures this data. Examples of software used include the Facial Recognition API and the Speech Analysis API.

[0357] 3. Sending specification data and emotion data

[0358] The device packages the specification data and emotion data, converts it into JSON or XML format, and sends it to the server using an HTTP request.

[0359] 4. Data analysis and urban model generation

[0360] The server receives the HTTP request and analyzes the specification data and sentiment data. This data analysis is performed using a generative AI, which uses a model trained using TensorFlow or PyTorch to generate a fictional city model.

[0361] 5. Saving city models and emotion data

[0362] The server stores the generated city model and related emotion data in a database, such as PostgreSQL or MongoDB. The stored data is organized in a referenceable state so that it can be used later in the simulation.

[0363] 6. Providing city models

[0364] The server returns the generated city model and emotion data to the device, where the data is converted back to JSON or XML format and sent as an HTTP response.

[0365] 7. Running the Simulation

[0366] The device interprets the city model data and emotion data received from the server and displays them on the user interface. The user then configures the simulation settings based on this information, launches the simulation engine, and runs simulations of various scenarios (e.g., performance evaluation of self-driving cars, disaster risk assessment, etc.). During this process, the emotion data is also taken into account and reflected in the simulation results.

[0367] Specific examples

[0368] For example, consider a case where a user uses the system to evaluate a new self-driving car algorithm. The user inputs the following specifications into the device: "population 500,000, hot and humid climate, extensive public transportation infrastructure," and sends them to the server. At the same time, facial recognition and voice analysis detect the emotional state of "anxiety." The server uses generative AI based on this data to generate a fictional city model. This city model incorporates specific scenarios that take the user's anxieties into account. The generated data is stored in a database and sent to the device. The user runs a simulation based on this city model, and the results are displayed with the emotional data reflected.

[0369] Prompt Sentence Examples

[0370] "We generate a city model with a population of 500,000, a hot and humid climate, and an extensive public transport infrastructure, and create scenarios for simulating autonomous vehicles, taking into account user concerns."

[0371] This invention generates a fictional city model that reflects the user's emotions, making it possible to provide fair and diverse simulation results, which provide intuitive and convincing results for the user.

[0372] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0373] Step 1:

[0374] The user inputs the specifications of the city model to be used in the simulation into the terminal.

[0375] Input: A user enters city specifications such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" into a form in a device application or web browser.

[0376] Output: Specification data entered into the terminal.

[0377] Specific actions: The user uses the keyboard or touchscreen to enter data about the city's characteristics into the terminal and presses the done button.

[0378] Step 2:

[0379] The device collects emotional data.

[0380] Input: Emotional data such as the user's facial expressions and voice.

[0381] Output: Emotion data processed by the emotion engine.

[0382] Specific operation: The device uses the camera and microphone to obtain the user's emotional data using facial recognition and speech analysis APIs (e.g., Facial Recognition API and Speech Analysis API). The emotional data is output in the form of "anxiety" or "happiness."

[0383] Step 3:

[0384] The terminal packages the specification data and emotion data and transmits them to the server.

[0385] Input: User-entered specification data and device-collected emotion data.

[0386] Output: Packaged data in JSON or XML format that is sent to the server.

[0387] Specific operation: The device converts the specification data and emotion data into JSON / XML format and sends it to the server using an HTTP request.

[0388] Step 4:

[0389] The server analyzes the received specification data and emotion data and generates a fictional city model using a generative AI model.

[0390] Input: Specification data and emotion data received by the server in JSON / XML format.

[0391] Output: The generated fictional city model.

[0392] Specific operation: The server parses and analyzes the received data, and then uses that data to generate a city model that meets the criteria using a generative AI model (for example, using a framework such as TensorFlow or PyTorch).

[0393] Step 5:

[0394] The server stores the generated city model and emotion data in a database.

[0395] Input: The generated city model and associated sentiment data.

[0396] Output: City model and emotion data stored in a database.

[0397] Specific operation: The server uses a database management system (e.g., PostgreSQL or MongoDB) to systematically store the generated city model and emotion data.

[0398] Step 6:

[0399] The server transmits the generated city model and emotion data to the terminal.

[0400] Input: City model and emotion data stored in a database.

[0401] Output: City model and emotion data in JSON or XML format sent to the device.

[0402] Specific operation: The server reads the necessary data from the database, converts it back into JSON / XML format, and sends it to the terminal as an HTTP response.

[0403] Step 7:

[0404] The device runs a simulation based on the generated city model and emotion data.

[0405] Input: City model data and emotion data sent from the server.

[0406] Output: Simulation results.

[0407] Specific operation: The device displays the received data on the user interface, and the user configures the simulation. The simulation engine then executes various scenarios and visualizes the results, taking into account the emotion data.

[0408] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0409] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0410] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0411] [Second embodiment]

[0412] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0413] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0414] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0415] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0416] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0417] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0418] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0419] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0420] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0421] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0422] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0423] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0424] This invention is a system for generating and simulating fictitious city models that can be used in fields such as autonomous driving, map applications, urban development, and disaster prevention. This system generates a large number of fictitious city models at high speed based on the specifications of the city model entered by the user, and enables various simulations to be performed using the models.

[0425] System configuration

[0426] 1. User Input

[0427] The user inputs the specifications of the city model to be used in the simulation into the terminal. For example, the user specifies "a city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure."

[0428] 2. Sending specification data

[0429] The device sends the specification data entered by the user to the server, packaged in JSON or XML format, and sent as an HTTP request.

[0430] 3. Data Generation

[0431] The server analyzes the received specification data and generates a fictional city model using a generative AI. The generated data includes the following information:

[0432] Population data: population, age structure, occupational distribution

[0433] Climate data: average annual temperature, precipitation

[0434] Geographic data: road networks, public transport

[0435] Resident data: Resident profile, lifestyle

[0436] 4. Save the city model

[0437] The server saves the generated city model in a database, where it can be referenced for later use in simulations.

[0438] 5. Submitting the Model

[0439] The server sends the generated city model back to the device, where the data is converted back into JSON or XML format and sent as an HTTP response.

[0440] 6. Running the Simulation

[0441] The device interprets the city model data received from the server and displays it on the user interface. The user can then configure the simulation settings based on this data and start the simulation engine to run simulations of various scenarios (e.g., performance evaluation of autonomous vehicles, disaster risk assessment, etc.).

[0442] Specific examples

[0443] For example, suppose a user uses the system to evaluate a new self-driving car algorithm. In this case, the user inputs the following specifications into their device: "A city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure." The specifications are then sent to the server. Based on this, the server uses generative AI to generate a fictitious city model and stores it in a database. The generated city model is then sent to the device, and the user runs a self-driving car simulation based on this city model. By analyzing the results of the simulation, the user can evaluate the performance of the new algorithm under fair and diverse conditions.

[0444] This system makes it possible to quickly run fair and diverse simulations without relying on real city data. Furthermore, it can avoid the reputational damage that can occur when using real cities in simulations such as disaster risk assessments. This makes it possible to provide a practical and ethical simulation environment.

[0445] The processing flow will be explained below.

[0446] Step 1:

[0447] The user inputs the specifications of the fictional city model to be used in the simulation into the device, such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" into an application on the device or a web browser input form.

[0448] Step 2:

[0449] The terminal packages the input specification data, organizes it in JSON or XML format, and converts it into a format that is easy to use for subsequent server processing.

[0450] Step 3:

[0451] The terminal sends the packaged specification data to the server as an HTTP request. Specifically, when the send button is clicked, the terminal sends the data to the specified URL of the server as a POST request.

[0452] Step 4:

[0453] The server receives the HTTP request and parses the requested specification data. Specifically, it deserializes the received data and stores it on the server as a JSON object or XML document.

[0454] Step 5:

[0455] The server launches a generation AI based on the analyzed specification data, which then begins the process of generating a fictional city model based on the specified population size, regional characteristics, and climatic conditions.

[0456] Step 6:

[0457] The server-based generation AI generates detailed population data (population count, age structure, occupational distribution), climate data (average annual temperature, precipitation), geographic data (road network, public transportation), and resident data (resident profiles, lifestyles).The generation AI uses existing datasets and statistical models to perform probabilistic and rule-based generation.

[0458] Step 7:

[0459] The server integrates the various data generated and compiles a fictional city model into a single dataset. Specifically, it combines each individual data set to create a consistent city model.

[0460] Step 8:

[0461] The server stores the integrated fictional city model in a database, where the data is indexed for easy search and access later.

[0462] Step 9:

[0463] The server sends the saved fictional city model to the terminal. Specifically, it reconverts the city model into JSON or XML format and sends it to the terminal as an HTTP response.

[0464] Step 10:

[0465] The terminal receives the transmitted city model data and displays it on the user interface. Specifically, it interprets the received data and displays it on the GUI (Graphical User Interface) as data for display.

[0466] Step 11:

[0467] The user checks the displayed city model and configures the simulation, for example, entering parameters to start a simulation of an autonomous vehicle.

[0468] Step 12:

[0469] When the user presses the simulation start button, the device starts the simulation engine, which then begins calculations based on the generated city model.

[0470] Step 13:

[0471] The device runs the simulation and generates results, which are calculated and visualized as performance data and risk assessments.

[0472] Step 14:

[0473] The terminal displays the calculation results to the user, who can then evaluate and analyze them.

[0474] Example 1

[0475] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0476] There is a need for a system that allows users to easily input specifications for the city model to be used in the simulation and quickly generate a large number of fictitious city models. The generated city models must also contain realistic data, which will improve the accuracy of various simulations. Furthermore, there is a need for a system that allows users to easily run simulations based on the generated city models and use the results for evaluation and analysis.

[0477] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0478] In this invention, the server includes means for a user to input specifications of a city model to be used in a simulation into a user device, means for the user device to transmit the specification data to the server, means for the server to generate a fictitious city model using a generative model based on the specification data, means for the server to save the generated city model in a storage device, means for the server to transmit the generated city model to the user device, and means for the user device to execute a simulation based on the generated city model. This makes it possible to quickly generate a variety of city models tailored to each user and execute realistic simulations.

[0479] A "user device" is a device through which a user inputs specifications for a city model to be used in a simulation, and is an electronic device such as a computer or smartphone.

[0480] "Specification data" is data that describes the characteristics and conditions of a city model entered by the user, and specifically includes information such as population, climate, and geographical conditions.

[0481] A "server" is a computer system that receives specification data sent from user devices and generates, stores, and transmits analysis and city models.

[0482] A "generative model" is an algorithm or AI technology that generates a fictional city model based on specified specification data.

[0483] A "city model" is a digital representation of a fictional city generated based on specification data, and is a dataset that includes population data, climate data, geographic data, resident data, etc.

[0484] A "storage device" is a database or storage system for saving the city model generated by the server.

[0485] "Simulation" is the process of conducting virtual experiments and analyses based on the generated urban model.

[0486] This invention is a system for generating and simulating fictional city models that can be used in fields such as autonomous driving, map applications, urban development, and disaster prevention. The system operates by combining hardware and software, including user devices, servers, generative models, and storage devices.

[0487] To use the system, a user first accesses their device and logs in. The user then fills in the specifications for the city model in an input form provided by the system. For example, the user specifies a city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure. This specification data is converted into JSON or XML format and sent to the server via an HTTP request.

[0488] The server receives the HTTP request and analyzes the specification data. Based on this analyzed data, the server uses a generative model to generate a fictional city model. For example, OpenAI GPT-3 is used as the generative model. The generative model prompt uses text such as "Generate a city model with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure."

[0489] The generated city model includes various information such as population data (number of people, age structure, occupational distribution), climate data (average annual temperature, precipitation), geographic data (road network, public transportation), and resident data (resident profiles, lifestyles). This data is stored in the server's storage device.

[0490] The saved city model data is converted back to JSON or XML format and sent to the user device as an HTTP response. The user device receives this data, interprets it, and displays it on the user interface, allowing the user to visually check an overview of the generated city model.

[0491] After the city model is displayed on the user device, the user configures the simulation settings, for example, entering conditions for evaluating the algorithms of a self-driving car. When the user clicks the "Start Simulation" button, the simulation engine is launched and the specified scenario is simulated. The simulation results are reflected in the user interface, allowing the user to evaluate and analyze them.

[0492] As a concrete example, consider the procedure for a user evaluating a new autonomous vehicle algorithm. The user inputs the specifications for a city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure, and sends them to a server. The server uses the generative model to generate a fictitious city model and saves it to a storage device. The generated city model is then sent back to the user's device, and the user runs a simulation based on this city model.

[0493] This system allows users to quickly run fair and diverse simulations without relying on real city data. It also helps to avoid reputational damage that can occur when using real cities for disaster risk assessments, and provides an ethical simulation environment.

[0494] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0495] Step 1:

[0496] The user accesses a terminal and logs in. The user enters the specifications of the city model for the simulation into an input form. For example, the user enters the specifications as "a city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure." Input data: City model specifications. Output data: Specification data converted into JSON or XML format.

[0497] Step 2:

[0498] The terminal converts the specification data entered by the user into JSON or XML format. The terminal generates an HTTP request and sends this specification data to the server. Input data: City model specifications entered by the user. Output data: Specification data in JSON or XML format sent to the server.

[0499] Step 3:

[0500] The server receives the HTTP request and extracts the specification data. The server parses the specification data to detect the required parameters. Input data: Specification data in JSON or XML format sent as the HTTP request. Output data: Parsed parameter set.

[0501] Step 4:

[0502] The server calls the API of the generating AI (e.g., OpenAI GPT-3). The prompt states, "Generate a city model with a population of 500,000, a hot and humid climate, and extensive public transportation infrastructure." Input data: The analyzed parameter set. Output data: The fictitious city model data returned by the generating AI.

[0503] Step 5:

[0504] The server verifies the generated city model data and checks for errors. If there are no errors, it saves it to a database. For example, PostgreSQL or MongoDB is used. Input data: City model data provided by the generation AI. Output data: City model data saved in the database or an error log.

[0505] Step 6:

[0506] The server converts the saved city model data back into JSON or XML format and packages it as an HTTP response. The server sends the HTTP response to the user's device. Input data: City model data saved in the database. Output data: City model data in JSON or XML format sent to the user's device.

[0507] Step 7:

[0508] The terminal receives the HTTP response from the server and interprets the city model data. The terminal displays an overview of the city model in the user interface. Input data: City model data in JSON or XML format sent from the server. Output data: An overview of the city model displayed in the user interface.

[0509] Step 8:

[0510] The user sets up the simulation, for example by filling out a form to evaluate an autonomous vehicle algorithm. The user clicks the "Start Simulation" button. Input data: The city model displayed in the user interface and additional simulation settings. Output data: Simulation parameters sent to the simulation engine.

[0511] Step 9:

[0512] The terminal starts the simulation engine and runs a simulation of the specified scenario. The results of the simulation are displayed on the user interface. For example, data such as traffic volume and accident occurrence rate are displayed. Input data: Simulation parameters. Output data: Simulation results displayed on the user interface.

[0513] (Application example 1)

[0514] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0515] Evaluating autonomous vehicle algorithms fairly and efficiently under diverse environmental conditions is challenging. Using real-world urban data can lead to ethical issues and misinterpretations. Furthermore, relying on real urban environments limits the flexibility of simulations.

[0516] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0517] In this invention, the server includes: [means for a user to input specifications of the city model to be used in the simulation into a terminal;] [means for the terminal to transmit the specification data to the server; and] [means for the server to generate a fictitious city model using a generation AI based on the specification data.] This makes it possible to simulate autonomous vehicles using a fictitious city model. This makes it possible to simulate under a variety of environmental conditions, enabling fair and efficient evaluation without relying on real city data.

[0518] A "user" is a person or organization that operates the system and inputs specifications for the city model.

[0519] A "simulation" is an experiment or trial conducted in a virtual environment under specific conditions or settings.

[0520] A "city model" is a dataset of a virtual city that includes attributes such as population, climate, geography, and transportation infrastructure.

[0521] "Specification data" is data entered by the user that describes the conditions and characteristics of the city model.

[0522] A "terminal" is a device that allows a user to input specifications for a city model and run a simulation.

[0523] "Server" means a central processing unit that receives specification data, processes the data, and stores and transmits the generated city model.

[0524] "Generative AI" is an artificial intelligence technology that generates fictional city models based on specified specification data.

[0525] A "database" is an information system that stores the generated city models and makes them accessible as needed.

[0526] "3D display" is the process of displaying the generated city model in a 3D visual format.

[0527] An "autonomous vehicle" is a vehicle that has the ability to drive autonomously without human operation.

[0528] "Transportation Data" means information about roads, public transportation, traffic volumes and patterns within a city.

[0529] "Infrastructure data" is information about a city's basic public facilities and systems (e.g., transportation, electricity, water supply).

[0530] "Fictional resident profiles" are data describing the characteristics and attributes of residents living in a virtual city.

[0531] "Traffic simulation data" refers to data that represents the results of a simulation based on traffic conditions and scenarios.

[0532] This invention is a system that generates a fictitious city model based on specifications for the city model entered by a user and uses the model to simulate an autonomous vehicle. The system includes a terminal operated by the user, a server that sends and receives data, a generation AI that generates the city model, and a database that stores and manages the generated city model.

[0533] System configuration

[0534] 1. User Input

[0535] Users input specifications for the city model they want to use in the simulation into the device, including attributes such as population, climate, and transportation infrastructure. For example, they might input, "Please generate a hot, humid city with a population of 500,000. I want to simulate a scenario with autonomous vehicles in an environment with extensive public transportation infrastructure."

[0536] 2. Sending specification data

[0537] The terminal sends the entered specification data to the server, which packages the data in JSON or XML format and sends it as an HTTP request. The server receives it and analyzes it.

[0538] 3. City model generation

[0539] The server generates a fictional city model using generative AI based on the received specification data. This generative AI is implemented using TensorFlow and Python. The generated city model includes population data, climate data, geographic data, resident data, traffic data, and infrastructure data.

[0540] 4. Data storage

[0541] The generated city model is saved in a database such as PostgreSQL, making it reusable for future simulations.

[0542] 5. Submitting the Model

[0543] The server sends the saved city model to the device, where it is converted back into JSON or XML format and sent as an HTTP response. The device receives and interprets this data.

[0544] 6. Running the Simulation

[0545] The device then runs a simulation based on the received city model. Specifically, it displays a city model generated using Three.js or similar software in three dimensions, and then simulates autonomous vehicles from that. The simulation includes scenarios such as traffic congestion assessment, accident risk assessment, and route optimization.

[0546] Specific examples

[0547] For example, if a user inputs a specification such as "a city with a population of 1 million, a dry climate, and an advanced public transportation infrastructure," the system can generate a fictional city model based on that specification and run simulations of autonomous vehicles in this city. This simulation allows the performance of new autonomous driving algorithms to be evaluated under a variety of conditions.

[0548] An example of a prompt to input to a generative AI model is as follows:

[0549] "Generate a hot, humid city with a population of 500,000. Simulate autonomous vehicle scenarios in an environment with extensive public transport infrastructure."

[0550] By implementing this invention, it is possible to quickly perform fair and diverse simulations without relying on real city data. Furthermore, it is possible to avoid reputational damage that can occur when using real cities in simulations such as disaster risk assessment. This makes it possible to provide a fair and practical simulation environment.

[0551] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0552] Step 1:

[0553] User input of specification data

[0554] The user inputs the specifications of the city model to be used in the simulation into the terminal, such as the population, climate, public transportation infrastructure, etc. At this time, the input data is collected through a form on the terminal.

[0555] Input: User-supplied specification data (e.g., population 500,000, high temperature and humidity, extensive public transport infrastructure)

[0556] Output: Specification data (e.g., data packaged in JSON format)

[0557] Step 2:

[0558] Sending specification data to the server

[0559] The terminal sends the specification data entered by the user to the server as an HTTP request.

[0560] Input: Specification data (JSON format)

[0561] Output: Specification data received by the server

[0562] Step 3:

[0563] The server analyzes the specification data and starts the generation AI

[0564] The server analyzes the received specification data and generates a fictional city model using generative AI, which uses Python and TensorFlow. Based on the specification data, the generative AI generates population data, climate data, geographic data, resident data, traffic data, and infrastructure data.

[0565] Input: Received specification data

[0566] Output: Generated city model (including various data)

[0567] Step 4:

[0568] Saving the generated city model

[0569] The server stores the generated city model in a database such as PostgreSQL.

[0570] Input: Generated city model

[0571] Output: City model stored in a database

[0572] Step 5:

[0573] Providing the generated city model

[0574] The server then sends the saved city model to the device, again as an HTTP response.

[0575] Input: City model stored in a database

[0576] Output: The city model received by the device

[0577] Step 6:

[0578] 3D display of city models on terminals

[0579] The device then runs a simulation based on the received city model, displaying the city model in three dimensions using Three.js.

[0580] Input: Received city model

[0581] Output: 3D city model

[0582] Step 7:

[0583] Running simulations of autonomous vehicles

[0584] The device simulates autonomous vehicles within a 3D city model, including traffic congestion assessment, accident risk assessment, and route optimization.

[0585] Input: 3D city model, simulation settings (starting point, destination, etc.)

[0586] Output: Simulation results (e.g. traffic congestion map, accident risk assessment results, etc.)

[0587] This allows users to run simulations of autonomous vehicles under a variety of conditions. The entire system works in concert to achieve fair and efficient simulations that do not rely on real-world urban data.

[0588] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0589] This invention combines an emotion engine that recognizes the user's emotions with a system for generating and simulating fictional city models that can be used in fields such as autonomous driving, map applications, urban development, and disaster prevention. This system generates a fictional city model based on the specifications of the city model entered by the user and their emotional state at the time, and enables various simulations to be performed using that model.

[0590] System configuration

[0591] 1. User Input

[0592] The user inputs the specifications of the city model to be used in the simulation into the device. For example, the user inputs information such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" into an application on the device or an input form in a web browser. During this input, the emotion engine detects the user's emotional state through facial recognition and voice analysis.

[0593] 2. Sending specification data and emotion data

[0594] The device packages the specification data and emotion data entered by the user. The specification data and emotion data are organized in JSON or XML format and converted into a format that is easy to use for subsequent server processing. The device then sends the packaged data to the server as an HTTP request.

[0595] 3. Data Generation

[0596] The server receives the HTTP request and analyzes the requested specification data and emotion data. The analyzed data is passed to the generation AI, which then generates a fictional city model based on the specified population size, regional characteristics, and climatic conditions. During this process, emotion data is also taken into account, and the city model desired by the user is generated.

[0597] 4. Saving city models and emotion data

[0598] The server stores the generated city model and associated emotion data in a database, where the model and emotion data can be referenced for later use in simulations.

[0599] 5. Submitting the Model

[0600] The server sends the generated city model back to the device, where the data is again converted into JSON or XML format and sent as an HTTP response.

[0601] 6. Running the Simulation

[0602] The device interprets the city model data and emotion data received from the server and displays them on the user interface. The user then configures the simulation settings based on this information, launches the simulation engine, and runs simulations of various scenarios (e.g., performance evaluation of self-driving cars, disaster risk assessment, etc.). During this process, emotion data is also taken into account and reflected in the simulation results.

[0603] Specific examples

[0604] For example, suppose a user uses the system to evaluate a new self-driving car algorithm. In this case, the user inputs the following specifications into their device: "Population 500,000, hot and humid climate, extensive public transportation infrastructure" and sends them to the server. Based on this, the server uses generative AI to generate a fictional city model and stores it in a database. At the same time, the emotion engine collects and stores the user's emotional data at the time of input. The generated city model and emotional data are then sent to the device, and the user runs a self-driving car simulation based on this city model and emotional data. The results of this simulation are visualized as an evaluation result that takes into account the user's emotional state.

[0605] This system makes it possible to quickly perform fair and diverse simulations without relying on real-world city data. Furthermore, by combining it with an emotion engine, it is possible to obtain more realistic simulation results that take into account the user's emotional state, providing users with more intuitive and convincing results.

[0606] The processing flow will be explained below.

[0607] Step 1:

[0608] The user inputs the specifications of the fictional city model to be used in the simulation into the device, such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" into an application on the device or a form in a web browser.

[0609] Step 2:

[0610] When the device packages the input specification data, it activates the emotion engine, which uses the camera and microphone to recognize the user's face and analyze their voice to detect their emotional state (e.g., joy, sadness, excitement, etc.).

[0611] Step 3:

[0612] The device integrates the detected emotion data with the input specification data and packages it in JSON or XML format, allowing the specification data and emotion data to be treated as a single dataset.

[0613] Step 4:

[0614] The device sends the packaged data to the server as an HTTP request, and the sent data is sent as a POST request to the specified URL on the server.

[0615] Step 5:

[0616] The server receives the HTTP request and deserializes the sent data. The deserialized data is expanded within the server as specification data and emotion data.

[0617] Step 6:

[0618] The server receives the expanded specification data and emotion data and activates the generation AI. The generation AI generates the following data based on the specified population size, regional characteristics, and climatic conditions:

[0619] Population data (e.g., population numbers, age distribution, occupational distribution)

[0620] Climate data (e.g., average annual temperature, precipitation)

[0621] Geographic data (e.g. road networks, public transport)

[0622] Resident data (e.g., resident profiles, lifestyles)

[0623] Step 7:

[0624] The generation AI in the server uses the detected emotion data to adjust the city model to fit the user's emotional state. This process ensures that the generated city model is more in line with the user's intentions and emotions.

[0625] Step 8:

[0626] The server integrates the various data generated and compiles it into a single fictional city model, which is then organized into a coherent city model.

[0627] Step 9:

[0628] The server stores the integrated fictional city model and associated emotion data in a database, which is indexed for easy access and reference later.

[0629] Step 10:

[0630] The server sends the saved city model and emotion data to the device, where it is converted back into JSON or XML format and sent as an HTTP response.

[0631] Step 11:

[0632] The terminal receives the transmitted city model data and emotion data and displays them on the user interface. Based on this, the user can check the city model and set up the simulation.

[0633] Step 12:

[0634] When the user sets the simulation conditions and presses the start button, the device starts the simulation engine, which then begins calculation processing based on the generated city model and emotion data.

[0635] Step 13:

[0636] The device runs the simulation and generates results, which are calculated as performance data and risk assessments and visualized in a way that is intuitive to the user.

[0637] Step 14:

[0638] The device displays the calculation results to the user, who can then evaluate and analyze them. Emotional data is also reflected in the results, which are displayed based on the user's emotional state.

[0639] Example 2

[0640] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0641] When conducting simulations using virtual city models, it is necessary to quickly obtain highly realistic simulation results that take into account the user's emotional state. However, current technology makes it difficult to reflect the user's emotional data in the simulation. Therefore, it is necessary to realize a city model generation and simulation system that includes emotional data.

[0642] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0643] In this invention, the server includes: [means for generating a fictional city model using a generation AI based on the specification data and emotion data]; [means for storing the generated city model and emotion data in a database]; and [means for transmitting the generated city model and emotion data to the terminal.] This enables [the generation and simulation of a highly realistic city model that takes into account the user's emotion data].

[0644] "User" refers to a person or organization that uses this system to input specifications for a city model and run a simulation.

[0645] "Terminal" refers to a device used by a user to input specifications for a city model and communicate with the server, and includes computers, smartphones, tablets, etc.

[0646] "Specification data" refers to information about the city model that the user inputs into the terminal, and includes population data, climate data, geographical data, resident data, and the like.

[0647] "Emotion data" is data collected by the terminal's emotion engine from the user's facial expressions and voice, and includes information indicating the user's emotional state.

[0648] "Server" refers to the computer system that receives specification data and emotion data, generates a city model using generative AI, and stores and transmits that data.

[0649] "Generative AI" refers to artificial intelligence algorithms that generate fictional city models based on specification and emotion data.

[0650] A "city model" refers to a dataset that represents the structure and characteristics of a fictional city generated by generative AI based on specification data and emotion data.

[0651] "Database" refers to an information system for storing the generated city model and associated emotion data and retrieving them as needed.

[0652] "Simulation Engine" refers to the software or algorithms used to simulate various scenarios based on the generated city model and emotion data.

[0653] "JSON format" refers to a lightweight data exchange format for storing and transferring specification and sentiment data in a structured manner.

[0654] "HTTP request" refers to the communication protocol used when a terminal sends data to a server.

[0655] "HTTP response" refers to the communication protocol used when a server sends data to a terminal.

[0656] This invention is a system that generates a fictional city model based on the specifications of the city model entered by the user and the emotional state at the time, and enables various simulations to be performed using that model. By incorporating an emotion engine, this system can collect the user's emotional state in real time and provide more realistic simulations that reflect this.

[0657] Hardware and Software Configuration

[0658] 1. Terminal

[0659] The terminal is a device used by users to input specifications for the city model. Typical hardware includes computers, smartphones, and tablets. This terminal includes applications that implement various input forms and software that runs on a web browser. It also incorporates an emotion engine that collects emotion data from users through facial recognition and voice analysis.

[0660] 2. Server

[0661] The server receives the specification data and emotion data sent from the device and generates a fictional city model using generative AI based on this data. The server stores the generated city model and emotion data in a database and transmits them to the device as needed. The server requires a high-performance processing unit and large-capacity storage. Specific software includes a data analysis algorithm, generative AI model, and database management system.

[0662] Specific examples

[0663] For example, a user might use this system to evaluate a new self-driving car algorithm. The user would enter specifications for a city model, such as a population of 500,000, a hot and humid climate, and extensive public transportation infrastructure, into an application on the device or an input form in a web browser. At the same time, the device's emotion engine would perform facial recognition and voice analysis to collect emotion data such as "happiness" and "expectation."

[0664] The device then converts the specification data and emotion data into JSON format and sends it to the server as an HTTP request. The server then analyzes the received data and passes it to a generation AI to generate a fictional city model. The device also takes into account the user's emotion data, resulting in a city model that reflects more positive elements.

[0665] The generated city model and emotion data are stored in a database by the server. The server then transmits the stored data to the device, which interprets it and displays it on the user interface. The user uses this data to configure the simulation settings for evaluating the performance of the autonomous vehicle and launch the simulation engine. The simulation results also reflect the user's emotional state, making the evaluation more intuitive and convincing.

[0666] Prompt Sentence Examples

[0667] Urban specifications: 500,000 people, hot and humid climate, extensive public transport infrastructure

[0668] Emotion data: joy, anticipation

[0669] This system enables users to quickly run diverse and fair simulations without relying on real-world city data. Furthermore, the emotion engine provides simulation results that reflect the user's emotional state in real time, resulting in more intuitive and convincing results.

[0670] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0671] Step 1: User Input

[0672] The user inputs specifications for a city model using a device application or web browser. For example, they input information such as "population 500,000, hot and humid climate, extensive public transportation infrastructure." The device's built-in emotion engine also collects emotional data through facial recognition and voice analysis. The input is entered into a form on the device and then saved as specification data on the device.

[0673] input:

[0674] City model specifications (e.g., population 500,000, hot and humid climate, extensive public transport infrastructure)

[0675] The user's emotional state (e.g., joy, anticipation)

[0676] output:

[0677] Specification data and sentiment data in JSON or XML format

[0678] Step 2: Sending specification data and emotion data

[0679] The device packages the user's input specifications and emotion data and sends it to the server as an HTTP request, organized in JSON or XML format.

[0680] input:

[0681] Specification data and sentiment data in JSON or XML format

[0682] output:

[0683] HTTP request sent to the server

[0684] Step 3: Data generation

[0685] The server receives the HTTP request and analyzes the specification data and emotion data. The analyzed data is passed to the generation AI, which generates a fictional city model based on the specified population size, regional characteristics, and climatic conditions. Emotion data is also taken into account during this process, and a city model that matches the user's emotions is generated.

[0686] input:

[0687] Specification and sentiment data in HTTP requests

[0688] output:

[0689] Generated city model

[0690] Specific behavior:

[0691] The server parses the data using a JSON parser

[0692] Input the analyzed data into the generative AI

[0693] Generative AI generates fictional city models

[0694] Step 4: Saving the city model and emotion data

[0695] The server stores the generated city model and emotion data in a database, where it can be quickly accessed for later use in simulations.

[0696] input:

[0697] Generated city model and emotion data

[0698] output:

[0699] City models and emotion data stored in a database

[0700] Specific behavior:

[0701] The server establishes a database connection and stores the data via SQL or NoSQL

[0702] Step 5: Serve the model

[0703] The server sends the generated city model to the terminal as an HTTP response, which is again converted to JSON or XML format.

[0704] input:

[0705] City models and emotion data stored in a database

[0706] output:

[0707] HTTP response sent to the device

[0708] Specific behavior:

[0709] The server retrieves the data from the database

[0710] Convert data to JSON or XML format

[0711] Send to the terminal as an HTTP response

[0712] Step 6: Run the simulation

[0713] The device interprets the received city model data and emotion data and displays them on the user interface. The user configures the simulation settings, starts the simulation engine, and executes various simulations. The simulation results, which reflect the user's emotional state, are displayed intuitively.

[0714] input:

[0715] City model and emotion data received as an HTTP response

[0716] output:

[0717] Simulation results

[0718] Specific behavior:

[0719] The device analyzes the city model data and visualizes it on the user interface.

[0720] The user configures the simulation settings and inputs the settings data into the simulation engine.

[0721] The simulation engine runs various scenarios and displays the results in the user interface.

[0722] The above is a detailed description of each processing step. This system allows users to quickly generate and simulate realistic city models that reflect their emotional states.

[0723] (Application example 2)

[0724] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0725] Conventional simulation systems do not take into account the user's emotional state, and therefore are unable to provide highly accurate simulation results that meet the user's intuition and desires. Furthermore, because they rely on specific city data, the simulation lacks fairness and diversity. Therefore, there is a need to provide simulation results that are more convincing to users.

[0726] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0727] In this invention, the server includes: [means for generating a fictional city model using a generation AI based on the specification data and emotion data]; [means for storing the generated city model and emotion data in a database]; and [means for transmitting the generated city model and emotion data to the terminal.] This makes it possible to [generate fair and diverse city models that reflect the user's emotions and provide highly accurate simulation results].

[0728] A "user" is a person or organization that inputs specification data and emotion data to generate a fictional city model using the simulation system.

[0729] A "terminal" is a device that allows a user to input specification data and emotion data and exchange data with the server. This includes smartphones, PCs, tablets, etc.

[0730] "Specification data" is information that indicates the characteristics and conditions of the city model used in the simulation, and includes data on population, climate, transportation infrastructure, etc.

[0731] "Emotional data" refers to information that reflects the user's emotional state, and includes data obtained through facial recognition and voice analysis.

[0732] A "server" is a device or system that receives specification data and emotion data sent from a terminal, generates a fictional city model, stores it in a database, and then returns the data to the terminal.

[0733] "Generative AI" includes artificial intelligence models for generating fictional city models based on specification data and emotion data.

[0734] A "fictional city model" is not a real city, but is digital data that virtually represents the structure, population, climate, etc. of a city created for simulation purposes.

[0735] The "database" is a system that systematically stores the generated city models and emotion data, making them available for reference as needed.

[0736] "Simulation" is the process of predicting movements and situations under specific conditions based on a generated fictional city model and analyzing the results.

[0737] A "simulation scenario" is generated based on the user's emotional data and specification data, and indicates the assumptions and specific situations used in the simulation.

[0738] MODE FOR CARRYING OUT THE INVENTION

[0739] To put this invention into practice, it is necessary to build a city model generation system based on emotion data. A system is realized in which users, terminals, and servers work together to generate a fictional city model that reflects the user's emotions and perform a simulation.

[0740] System Configuration

[0741] 1. User Input

[0742] The user inputs the specifications of the city model to be used in the simulation and the emotional data at the time of input into the device. For example, the user can input specification data such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" via an application on the device or a web browser.

[0743] 2. Collecting Emotional Data

[0744] The device collects the user's emotional data along with the device's specifications. Specifically, it uses facial recognition and speech analysis APIs to detect the user's emotional state and captures this data. Examples of software used include the Facial Recognition API and the Speech Analysis API.

[0745] 3. Sending specification data and emotion data

[0746] The device packages the specification data and emotion data, converts it into JSON or XML format, and sends it to the server using an HTTP request.

[0747] 4. Data analysis and urban model generation

[0748] The server receives the HTTP request and analyzes the specification data and sentiment data. This data analysis is performed using a generative AI, which uses a model trained using TensorFlow or PyTorch to generate a fictional city model.

[0749] 5. Saving city models and emotion data

[0750] The server stores the generated city model and related emotion data in a database, such as PostgreSQL or MongoDB. The stored data is organized in a referenceable state so that it can be used later in the simulation.

[0751] 6. Providing city models

[0752] The server returns the generated city model and emotion data to the device, where the data is converted back to JSON or XML format and sent as an HTTP response.

[0753] 7. Running the Simulation

[0754] The device interprets the city model data and emotion data received from the server and displays them on the user interface. The user then configures the simulation settings based on this information, launches the simulation engine, and runs simulations of various scenarios (e.g., performance evaluation of self-driving cars, disaster risk assessment, etc.). During this process, the emotion data is also taken into account and reflected in the simulation results.

[0755] Specific examples

[0756] For example, consider a case where a user uses the system to evaluate a new self-driving car algorithm. The user inputs the following specifications into the device: "population 500,000, hot and humid climate, extensive public transportation infrastructure," and sends them to the server. At the same time, facial recognition and voice analysis detect the emotional state of "anxiety." The server uses generative AI based on this data to generate a fictional city model. This city model incorporates specific scenarios that take the user's anxieties into account. The generated data is stored in a database and sent to the device. The user runs a simulation based on this city model, and the results are displayed with the emotional data reflected.

[0757] Prompt Sentence Examples

[0758] "We generate a city model with a population of 500,000, a hot and humid climate, and an extensive public transport infrastructure, and create scenarios for simulating autonomous vehicles, taking into account user concerns."

[0759] This invention generates a fictional city model that reflects the user's emotions, making it possible to provide fair and diverse simulation results, which provide intuitive and convincing results for the user.

[0760] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0761] Step 1:

[0762] The user inputs the specifications of the city model to be used in the simulation into the terminal.

[0763] Input: A user enters city specifications such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" into a form in a device application or web browser.

[0764] Output: Specification data entered into the terminal.

[0765] Specific actions: The user uses the keyboard or touchscreen to enter data about the city's characteristics into the terminal and presses the done button.

[0766] Step 2:

[0767] The device collects emotional data.

[0768] Input: Emotional data such as the user's facial expressions and voice.

[0769] Output: Emotion data processed by the emotion engine.

[0770] Specific operation: The device uses the camera and microphone to obtain the user's emotional data using facial recognition and speech analysis APIs (e.g., Facial Recognition API and Speech Analysis API). The emotional data is output in the form of "anxiety" or "happiness."

[0771] Step 3:

[0772] The terminal packages the specification data and emotion data and transmits them to the server.

[0773] Input: User-entered specification data and device-collected emotion data.

[0774] Output: Packaged data in JSON or XML format that is sent to the server.

[0775] Specific operation: The device converts the specification data and emotion data into JSON / XML format and sends it to the server using an HTTP request.

[0776] Step 4:

[0777] The server analyzes the received specification data and emotion data and generates a fictional city model using a generative AI model.

[0778] Input: Specification data and emotion data received by the server in JSON / XML format.

[0779] Output: The generated fictional city model.

[0780] Specific operation: The server parses and analyzes the received data, and then uses that data to generate a city model that meets the criteria using a generative AI model (for example, using a framework such as TensorFlow or PyTorch).

[0781] Step 5:

[0782] The server stores the generated city model and emotion data in a database.

[0783] Input: The generated city model and associated sentiment data.

[0784] Output: City model and emotion data stored in a database.

[0785] Specific operation: The server uses a database management system (e.g., PostgreSQL or MongoDB) to systematically store the generated city model and emotion data.

[0786] Step 6:

[0787] The server transmits the generated city model and emotion data to the terminal.

[0788] Input: City model and emotion data stored in a database.

[0789] Output: City model and emotion data in JSON or XML format sent to the device.

[0790] Specific operation: The server reads the necessary data from the database, converts it back into JSON / XML format, and sends it to the terminal as an HTTP response.

[0791] Step 7:

[0792] The device runs a simulation based on the generated city model and emotion data.

[0793] Input: City model data and emotion data sent from the server.

[0794] Output: Simulation results.

[0795] Specific operation: The device displays the received data on the user interface, and the user configures the simulation. The simulation engine then executes various scenarios and visualizes the results, taking into account the emotion data.

[0796] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0797] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0798] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0799] [Third embodiment]

[0800] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0801] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0802] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0803] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0804] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0805] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0806] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0807] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0808] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0809] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0810] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0811] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0812] This invention is a system for generating and simulating fictitious city models that can be used in fields such as autonomous driving, map applications, urban development, and disaster prevention. This system generates a large number of fictitious city models at high speed based on the specifications of the city model entered by the user, and enables various simulations to be performed using the models.

[0813] System configuration

[0814] 1. User Input

[0815] The user inputs the specifications of the city model to be used in the simulation into the terminal. For example, the user specifies "a city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure."

[0816] 2. Sending specification data

[0817] The device sends the specification data entered by the user to the server, packaged in JSON or XML format, and sent as an HTTP request.

[0818] 3. Data Generation

[0819] The server analyzes the received specification data and generates a fictional city model using a generative AI. The generated data includes the following information:

[0820] Population data: population, age structure, occupational distribution

[0821] Climate data: average annual temperature, precipitation

[0822] Geographic data: road networks, public transport

[0823] Resident data: Resident profile, lifestyle

[0824] 4. Save the city model

[0825] The server saves the generated city model in a database, where it can be referenced for later use in simulations.

[0826] 5. Submitting the Model

[0827] The server sends the generated city model back to the device, where the data is converted back into JSON or XML format and sent as an HTTP response.

[0828] 6. Running the Simulation

[0829] The device interprets the city model data received from the server and displays it on the user interface. The user can then configure the simulation settings based on this data and start the simulation engine to run simulations of various scenarios (e.g., performance evaluation of autonomous vehicles, disaster risk assessment, etc.).

[0830] Specific examples

[0831] For example, suppose a user uses the system to evaluate a new self-driving car algorithm. In this case, the user inputs the following specifications into their device: "A city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure." The specifications are then sent to the server. Based on this, the server uses generative AI to generate a fictitious city model and stores it in a database. The generated city model is then sent to the device, and the user runs a self-driving car simulation based on this city model. By analyzing the results of the simulation, the user can evaluate the performance of the new algorithm under fair and diverse conditions.

[0832] This system makes it possible to quickly run fair and diverse simulations without relying on real city data. Furthermore, it can avoid the reputational damage that can occur when using real cities in simulations such as disaster risk assessments. This makes it possible to provide a practical and ethical simulation environment.

[0833] The processing flow will be explained below.

[0834] Step 1:

[0835] The user inputs the specifications of the fictional city model to be used in the simulation into the device, such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" into an application on the device or a web browser input form.

[0836] Step 2:

[0837] The terminal packages the input specification data, organizes it in JSON or XML format, and converts it into a format that is easy to use for subsequent server processing.

[0838] Step 3:

[0839] The terminal sends the packaged specification data to the server as an HTTP request. Specifically, when the send button is clicked, the terminal sends the data to the specified URL of the server as a POST request.

[0840] Step 4:

[0841] The server receives the HTTP request and parses the requested specification data. Specifically, it deserializes the received data and stores it on the server as a JSON object or XML document.

[0842] Step 5:

[0843] The server launches a generation AI based on the analyzed specification data, which then begins the process of generating a fictional city model based on the specified population size, regional characteristics, and climatic conditions.

[0844] Step 6:

[0845] The server-based generation AI generates detailed population data (population count, age structure, occupational distribution), climate data (average annual temperature, precipitation), geographic data (road network, public transportation), and resident data (resident profiles, lifestyles).The generation AI uses existing datasets and statistical models to perform probabilistic and rule-based generation.

[0846] Step 7:

[0847] The server integrates the various data generated and compiles a fictional city model into a single dataset. Specifically, it combines each individual data set to create a consistent city model.

[0848] Step 8:

[0849] The server stores the integrated fictional city model in a database, where the data is indexed for easy search and access later.

[0850] Step 9:

[0851] The server sends the saved fictional city model to the terminal. Specifically, it reconverts the city model into JSON or XML format and sends it to the terminal as an HTTP response.

[0852] Step 10:

[0853] The terminal receives the transmitted city model data and displays it on the user interface. Specifically, it interprets the received data and displays it on the GUI (Graphical User Interface) as data for display.

[0854] Step 11:

[0855] The user checks the displayed city model and configures the simulation, for example, entering parameters to start a simulation of an autonomous vehicle.

[0856] Step 12:

[0857] When the user presses the simulation start button, the device starts the simulation engine, which then begins calculations based on the generated city model.

[0858] Step 13:

[0859] The device runs the simulation and generates results, which are calculated and visualized as performance data and risk assessments.

[0860] Step 14:

[0861] The terminal displays the calculation results to the user, who can then evaluate and analyze them.

[0862] Example 1

[0863] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0864] There is a need for a system that allows users to easily input specifications for the city model to be used in the simulation and quickly generate a large number of fictitious city models. The generated city models must also contain realistic data, which will improve the accuracy of various simulations. Furthermore, there is a need for a system that allows users to easily run simulations based on the generated city models and use the results for evaluation and analysis.

[0865] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0866] In this invention, the server includes means for a user to input specifications of a city model to be used in a simulation into a user device, means for the user device to transmit the specification data to the server, means for the server to generate a fictitious city model using a generative model based on the specification data, means for the server to save the generated city model in a storage device, means for the server to transmit the generated city model to the user device, and means for the user device to execute a simulation based on the generated city model. This makes it possible to quickly generate a variety of city models tailored to each user and execute realistic simulations.

[0867] A "user device" is a device through which a user inputs specifications for a city model to be used in a simulation, and is an electronic device such as a computer or smartphone.

[0868] "Specification data" is data that describes the characteristics and conditions of a city model entered by the user, and specifically includes information such as population, climate, and geographical conditions.

[0869] A "server" is a computer system that receives specification data sent from user devices and generates, stores, and transmits analysis and city models.

[0870] A "generative model" is an algorithm or AI technology that generates a fictional city model based on specified specification data.

[0871] A "city model" is a digital representation of a fictional city generated based on specification data, and is a dataset that includes population data, climate data, geographic data, resident data, etc.

[0872] A "storage device" is a database or storage system for saving the city model generated by the server.

[0873] "Simulation" is the process of conducting virtual experiments and analyses based on the generated urban model.

[0874] This invention is a system for generating and simulating fictional city models that can be used in fields such as autonomous driving, map applications, urban development, and disaster prevention. The system operates by combining hardware and software, including user devices, servers, generative models, and storage devices.

[0875] To use the system, a user first accesses their device and logs in. The user then fills in the specifications for the city model in an input form provided by the system. For example, the user specifies a city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure. This specification data is converted into JSON or XML format and sent to the server via an HTTP request.

[0876] The server receives the HTTP request and analyzes the specification data. Based on this analyzed data, the server uses a generative model to generate a fictional city model. For example, OpenAI GPT-3 is used as the generative model. The generative model prompt uses text such as "Generate a city model with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure."

[0877] The generated city model includes various information such as population data (number of people, age structure, occupational distribution), climate data (average annual temperature, precipitation), geographic data (road network, public transportation), and resident data (resident profiles, lifestyles). This data is stored in the server's storage device.

[0878] The saved city model data is converted back to JSON or XML format and sent to the user device as an HTTP response. The user device receives this data, interprets it, and displays it on the user interface, allowing the user to visually check an overview of the generated city model.

[0879] After the city model is displayed on the user device, the user configures the simulation settings, for example, entering conditions for evaluating the algorithms of a self-driving car. When the user clicks the "Start Simulation" button, the simulation engine is launched and the specified scenario is simulated. The simulation results are reflected in the user interface, allowing the user to evaluate and analyze them.

[0880] As a concrete example, consider the procedure for a user evaluating a new autonomous vehicle algorithm. The user inputs the specifications for a city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure, and sends them to a server. The server uses the generative model to generate a fictitious city model and saves it to a storage device. The generated city model is then sent back to the user's device, and the user runs a simulation based on this city model.

[0881] This system allows users to quickly run fair and diverse simulations without relying on real city data. It also helps to avoid reputational damage that can occur when using real cities for disaster risk assessments, and provides an ethical simulation environment.

[0882] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0883] Step 1:

[0884] The user accesses a terminal and logs in. The user enters the specifications of the city model for the simulation into an input form. For example, the user enters the specifications as "a city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure." Input data: City model specifications. Output data: Specification data converted into JSON or XML format.

[0885] Step 2:

[0886] The terminal converts the specification data entered by the user into JSON or XML format. The terminal generates an HTTP request and sends this specification data to the server. Input data: City model specifications entered by the user. Output data: Specification data in JSON or XML format sent to the server.

[0887] Step 3:

[0888] The server receives the HTTP request and extracts the specification data. The server parses the specification data to detect the required parameters. Input data: Specification data in JSON or XML format sent as the HTTP request. Output data: Parsed parameter set.

[0889] Step 4:

[0890] The server calls the API of the generating AI (e.g., OpenAI GPT-3). The prompt states, "Generate a city model with a population of 500,000, a hot and humid climate, and extensive public transportation infrastructure." Input data: The analyzed parameter set. Output data: The fictitious city model data returned by the generating AI.

[0891] Step 5:

[0892] The server verifies the generated city model data and checks for errors. If there are no errors, it saves it to a database. For example, PostgreSQL or MongoDB is used. Input data: City model data provided by the generation AI. Output data: City model data saved in the database or an error log.

[0893] Step 6:

[0894] The server converts the saved city model data back into JSON or XML format and packages it as an HTTP response. The server sends the HTTP response to the user's device. Input data: City model data saved in the database. Output data: City model data in JSON or XML format sent to the user's device.

[0895] Step 7:

[0896] The terminal receives the HTTP response from the server and interprets the city model data. The terminal displays an overview of the city model in the user interface. Input data: City model data in JSON or XML format sent from the server. Output data: An overview of the city model displayed in the user interface.

[0897] Step 8:

[0898] The user sets up the simulation, for example by filling out a form to evaluate an autonomous vehicle algorithm. The user clicks the "Start Simulation" button. Input data: The city model displayed in the user interface and additional simulation settings. Output data: Simulation parameters sent to the simulation engine.

[0899] Step 9:

[0900] The terminal starts the simulation engine and runs a simulation of the specified scenario. The results of the simulation are displayed on the user interface. For example, data such as traffic volume and accident occurrence rate are displayed. Input data: Simulation parameters. Output data: Simulation results displayed on the user interface.

[0901] (Application example 1)

[0902] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0903] Evaluating autonomous vehicle algorithms fairly and efficiently under diverse environmental conditions is challenging. Using real-world urban data can lead to ethical issues and misinterpretations. Furthermore, relying on real urban environments limits the flexibility of simulations.

[0904] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0905] In this invention, the server includes: [means for a user to input specifications of the city model to be used in the simulation into a terminal;] [means for the terminal to transmit the specification data to the server; and] [means for the server to generate a fictitious city model using a generation AI based on the specification data.] This makes it possible to simulate autonomous vehicles using a fictitious city model. This makes it possible to simulate under a variety of environmental conditions, enabling fair and efficient evaluation without relying on real city data.

[0906] A "user" is a person or organization that operates the system and inputs specifications for the city model.

[0907] A "simulation" is an experiment or trial conducted in a virtual environment under specific conditions or settings.

[0908] A "city model" is a dataset of a virtual city that includes attributes such as population, climate, geography, and transportation infrastructure.

[0909] "Specification data" is data entered by the user that describes the conditions and characteristics of the city model.

[0910] A "terminal" is a device that allows a user to input specifications for a city model and run a simulation.

[0911] "Server" means a central processing unit that receives specification data, processes the data, and stores and transmits the generated city model.

[0912] "Generative AI" is an artificial intelligence technology that generates fictional city models based on specified specification data.

[0913] A "database" is an information system that stores the generated city models and makes them accessible as needed.

[0914] "3D display" is the process of displaying the generated city model in a 3D visual format.

[0915] An "autonomous vehicle" is a vehicle that has the ability to drive autonomously without human operation.

[0916] "Transportation Data" means information about roads, public transportation, traffic volumes and patterns within a city.

[0917] "Infrastructure data" is information about a city's basic public facilities and systems (e.g., transportation, electricity, water supply).

[0918] "Fictional resident profiles" are data describing the characteristics and attributes of residents living in a virtual city.

[0919] "Traffic simulation data" refers to data that represents the results of a simulation based on traffic conditions and scenarios.

[0920] This invention is a system that generates a fictitious city model based on specifications for the city model entered by a user and uses the model to simulate an autonomous vehicle. The system includes a terminal operated by the user, a server that sends and receives data, a generation AI that generates the city model, and a database that stores and manages the generated city model.

[0921] System configuration

[0922] 1. User Input

[0923] Users input specifications for the city model they want to use in the simulation into the device, including attributes such as population, climate, and transportation infrastructure. For example, they might input, "Please generate a hot, humid city with a population of 500,000. I want to simulate a scenario with autonomous vehicles in an environment with extensive public transportation infrastructure."

[0924] 2. Sending specification data

[0925] The terminal sends the entered specification data to the server, which packages the data in JSON or XML format and sends it as an HTTP request. The server receives it and analyzes it.

[0926] 3. City model generation

[0927] The server generates a fictional city model using generative AI based on the received specification data. This generative AI is implemented using TensorFlow and Python. The generated city model includes population data, climate data, geographic data, resident data, traffic data, and infrastructure data.

[0928] 4. Data storage

[0929] The generated city model is saved in a database such as PostgreSQL, making it reusable for future simulations.

[0930] 5. Submitting the Model

[0931] The server sends the saved city model to the device, where it is converted back into JSON or XML format and sent as an HTTP response. The device receives and interprets this data.

[0932] 6. Running the Simulation

[0933] The device then runs a simulation based on the received city model. Specifically, it displays a city model generated using Three.js or similar software in three dimensions, and then simulates autonomous vehicles from that. The simulation includes scenarios such as traffic congestion assessment, accident risk assessment, and route optimization.

[0934] Specific examples

[0935] For example, if a user inputs a specification such as "a city with a population of 1 million, a dry climate, and an advanced public transportation infrastructure," the system can generate a fictional city model based on that specification and run simulations of autonomous vehicles in this city. This simulation allows the performance of new autonomous driving algorithms to be evaluated under a variety of conditions.

[0936] An example of a prompt to input to a generative AI model is as follows:

[0937] "Generate a hot, humid city with a population of 500,000. Simulate autonomous vehicle scenarios in an environment with extensive public transport infrastructure."

[0938] By implementing this invention, it is possible to quickly perform fair and diverse simulations without relying on real city data. Furthermore, it is possible to avoid reputational damage that can occur when using real cities in simulations such as disaster risk assessment. This makes it possible to provide a fair and practical simulation environment.

[0939] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0940] Step 1:

[0941] User input of specification data

[0942] The user inputs the specifications of the city model to be used in the simulation into the terminal, such as the population, climate, public transportation infrastructure, etc. At this time, the input data is collected through a form on the terminal.

[0943] Input: User-supplied specification data (e.g., population 500,000, high temperature and humidity, extensive public transport infrastructure)

[0944] Output: Specification data (e.g., data packaged in JSON format)

[0945] Step 2:

[0946] Sending specification data to the server

[0947] The terminal sends the specification data entered by the user to the server as an HTTP request.

[0948] Input: Specification data (JSON format)

[0949] Output: Specification data received by the server

[0950] Step 3:

[0951] The server analyzes the specification data and starts the generation AI

[0952] The server analyzes the received specification data and generates a fictional city model using generative AI, which uses Python and TensorFlow. Based on the specification data, the generative AI generates population data, climate data, geographic data, resident data, traffic data, and infrastructure data.

[0953] Input: Received specification data

[0954] Output: Generated city model (including various data)

[0955] Step 4:

[0956] Saving the generated city model

[0957] The server stores the generated city model in a database such as PostgreSQL.

[0958] Input: Generated city model

[0959] Output: City model stored in a database

[0960] Step 5:

[0961] Providing the generated city model

[0962] The server then sends the saved city model to the device, again as an HTTP response.

[0963] Input: City model stored in a database

[0964] Output: The city model received by the device

[0965] Step 6:

[0966] 3D display of city models on terminals

[0967] The device then runs a simulation based on the received city model, displaying the city model in three dimensions using Three.js.

[0968] Input: Received city model

[0969] Output: 3D city model

[0970] Step 7:

[0971] Running simulations of autonomous vehicles

[0972] The device simulates autonomous vehicles within a 3D city model, including traffic congestion assessment, accident risk assessment, and route optimization.

[0973] Input: 3D city model, simulation settings (starting point, destination, etc.)

[0974] Output: Simulation results (e.g. traffic congestion map, accident risk assessment results, etc.)

[0975] This allows users to run simulations of autonomous vehicles under a variety of conditions. The entire system works in concert to achieve fair and efficient simulations that do not rely on real-world urban data.

[0976] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0977] This invention combines an emotion engine that recognizes the user's emotions with a system for generating and simulating fictional city models that can be used in fields such as autonomous driving, map applications, urban development, and disaster prevention. This system generates a fictional city model based on the specifications of the city model entered by the user and their emotional state at the time, and enables various simulations to be performed using that model.

[0978] System configuration

[0979] 1. User Input

[0980] The user inputs the specifications of the city model to be used in the simulation into the device. For example, the user inputs information such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" into an application on the device or an input form in a web browser. During this input, the emotion engine detects the user's emotional state through facial recognition and voice analysis.

[0981] 2. Sending specification data and emotion data

[0982] The device packages the specification data and emotion data entered by the user. The specification data and emotion data are organized in JSON or XML format and converted into a format that is easy to use for subsequent server processing. The device then sends the packaged data to the server as an HTTP request.

[0983] 3. Data Generation

[0984] The server receives the HTTP request and analyzes the requested specification data and emotion data. The analyzed data is passed to the generation AI, which then generates a fictional city model based on the specified population size, regional characteristics, and climatic conditions. During this process, emotion data is also taken into account, and the city model desired by the user is generated.

[0985] 4. Saving city models and emotion data

[0986] The server stores the generated city model and associated emotion data in a database, where the model and emotion data can be referenced for later use in simulations.

[0987] 5. Submitting the Model

[0988] The server sends the generated city model back to the device, where the data is again converted into JSON or XML format and sent as an HTTP response.

[0989] 6. Running the Simulation

[0990] The device interprets the city model data and emotion data received from the server and displays them on the user interface. The user then configures the simulation settings based on this information, launches the simulation engine, and runs simulations of various scenarios (e.g., performance evaluation of self-driving cars, disaster risk assessment, etc.). During this process, emotion data is also taken into account and reflected in the simulation results.

[0991] Specific examples

[0992] For example, suppose a user uses the system to evaluate a new self-driving car algorithm. In this case, the user inputs the following specifications into their device: "Population 500,000, hot and humid climate, extensive public transportation infrastructure" and sends them to the server. Based on this, the server uses generative AI to generate a fictional city model and stores it in a database. At the same time, the emotion engine collects and stores the user's emotional data at the time of input. The generated city model and emotional data are then sent to the device, and the user runs a self-driving car simulation based on this city model and emotional data. The results of this simulation are visualized as an evaluation result that takes into account the user's emotional state.

[0993] This system makes it possible to quickly perform fair and diverse simulations without relying on real-world city data. Furthermore, by combining it with an emotion engine, it is possible to obtain more realistic simulation results that take into account the user's emotional state, providing users with more intuitive and convincing results.

[0994] The processing flow will be explained below.

[0995] Step 1:

[0996] The user inputs the specifications of the fictional city model to be used in the simulation into the device, such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" into an application on the device or a form in a web browser.

[0997] Step 2:

[0998] When the device packages the input specification data, it activates the emotion engine, which uses the camera and microphone to recognize the user's face and analyze their voice to detect their emotional state (e.g., joy, sadness, excitement, etc.).

[0999] Step 3:

[1000] The device integrates the detected emotion data with the input specification data and packages it in JSON or XML format, allowing the specification data and emotion data to be treated as a single dataset.

[1001] Step 4:

[1002] The device sends the packaged data to the server as an HTTP request, and the sent data is sent as a POST request to the specified URL on the server.

[1003] Step 5:

[1004] The server receives the HTTP request and deserializes the sent data. The deserialized data is expanded within the server as specification data and emotion data.

[1005] Step 6:

[1006] The server receives the expanded specification data and emotion data and activates the generation AI. The generation AI generates the following data based on the specified population size, regional characteristics, and climatic conditions:

[1007] Population data (e.g., population numbers, age distribution, occupational distribution)

[1008] Climate data (e.g., average annual temperature, precipitation)

[1009] Geographic data (e.g. road networks, public transport)

[1010] Resident data (e.g., resident profiles, lifestyles)

[1011] Step 7:

[1012] The generation AI in the server uses the detected emotion data to adjust the city model to fit the user's emotional state. This process ensures that the generated city model is more in line with the user's intentions and emotions.

[1013] Step 8:

[1014] The server integrates the various data generated and compiles it into a single fictional city model, which is then organized into a coherent city model.

[1015] Step 9:

[1016] The server stores the integrated fictional city model and associated emotion data in a database, which is indexed for easy access and reference later.

[1017] Step 10:

[1018] The server sends the saved city model and emotion data to the device, where it is converted back into JSON or XML format and sent as an HTTP response.

[1019] Step 11:

[1020] The terminal receives the transmitted city model data and emotion data and displays them on the user interface. Based on this, the user can check the city model and set up the simulation.

[1021] Step 12:

[1022] When the user sets the simulation conditions and presses the start button, the device starts the simulation engine, which then begins calculation processing based on the generated city model and emotion data.

[1023] Step 13:

[1024] The device runs the simulation and generates results, which are calculated as performance data and risk assessments and visualized in a way that is intuitive to the user.

[1025] Step 14:

[1026] The device displays the calculation results to the user, who can then evaluate and analyze them. Emotional data is also reflected in the results, which are displayed based on the user's emotional state.

[1027] Example 2

[1028] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1029] When conducting simulations using virtual city models, it is necessary to quickly obtain highly realistic simulation results that take into account the user's emotional state. However, current technology makes it difficult to reflect the user's emotional data in the simulation. Therefore, it is necessary to realize a city model generation and simulation system that includes emotional data.

[1030] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1031] In this invention, the server includes: [means for generating a fictional city model using a generation AI based on the specification data and emotion data]; [means for storing the generated city model and emotion data in a database]; and [means for transmitting the generated city model and emotion data to the terminal.] This enables [the generation and simulation of a highly realistic city model that takes into account the user's emotion data].

[1032] "User" refers to a person or organization that uses this system to input specifications for a city model and run a simulation.

[1033] "Terminal" refers to a device used by a user to input specifications for a city model and communicate with the server, and includes computers, smartphones, tablets, etc.

[1034] "Specification data" refers to information about the city model that the user inputs into the terminal, and includes population data, climate data, geographical data, resident data, and the like.

[1035] "Emotion data" is data collected by the terminal's emotion engine from the user's facial expressions and voice, and includes information indicating the user's emotional state.

[1036] "Server" refers to the computer system that receives specification data and emotion data, generates a city model using generative AI, and stores and transmits that data.

[1037] "Generative AI" refers to artificial intelligence algorithms that generate fictional city models based on specification and emotion data.

[1038] A "city model" refers to a dataset that represents the structure and characteristics of a fictional city generated by generative AI based on specification data and emotion data.

[1039] "Database" refers to an information system for storing the generated city model and associated emotion data and retrieving them as needed.

[1040] "Simulation Engine" refers to the software or algorithms used to simulate various scenarios based on the generated city model and emotion data.

[1041] "JSON format" refers to a lightweight data exchange format for storing and transferring specification and sentiment data in a structured manner.

[1042] "HTTP request" refers to the communication protocol used when a terminal sends data to a server.

[1043] "HTTP response" refers to the communication protocol used when a server sends data to a terminal.

[1044] This invention is a system that generates a fictional city model based on the specifications of the city model entered by the user and the emotional state at the time, and enables various simulations to be performed using that model. By incorporating an emotion engine, this system can collect the user's emotional state in real time and provide more realistic simulations that reflect this.

[1045] Hardware and Software Configuration

[1046] 1. Terminal

[1047] The terminal is a device used by users to input specifications for the city model. Typical hardware includes computers, smartphones, and tablets. This terminal includes applications that implement various input forms and software that runs on a web browser. It also incorporates an emotion engine that collects emotion data from users through facial recognition and voice analysis.

[1048] 2. Server

[1049] The server receives the specification data and emotion data sent from the device and generates a fictional city model using generative AI based on this data. The server stores the generated city model and emotion data in a database and transmits them to the device as needed. The server requires a high-performance processing unit and large-capacity storage. Specific software includes a data analysis algorithm, generative AI model, and database management system.

[1050] Specific examples

[1051] For example, a user might use this system to evaluate a new self-driving car algorithm. The user would enter specifications for a city model, such as a population of 500,000, a hot and humid climate, and extensive public transportation infrastructure, into an application on the device or an input form in a web browser. At the same time, the device's emotion engine would perform facial recognition and voice analysis to collect emotion data such as "happiness" and "expectation."

[1052] The device then converts the specification data and emotion data into JSON format and sends it to the server as an HTTP request. The server then analyzes the received data and passes it to a generation AI to generate a fictional city model. The device also takes into account the user's emotion data, resulting in a city model that reflects more positive elements.

[1053] The generated city model and emotion data are stored in a database by the server. The server then transmits the stored data to the device, which interprets it and displays it on the user interface. The user uses this data to configure the simulation settings for evaluating the performance of the autonomous vehicle and launch the simulation engine. The simulation results also reflect the user's emotional state, making the evaluation more intuitive and convincing.

[1054] Prompt Sentence Examples

[1055] Urban specifications: 500,000 people, hot and humid climate, extensive public transport infrastructure

[1056] Emotion data: joy, anticipation

[1057] This system enables users to quickly run diverse and fair simulations without relying on real-world city data. Furthermore, the emotion engine provides simulation results that reflect the user's emotional state in real time, resulting in more intuitive and convincing results.

[1058] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1059] Step 1: User Input

[1060] The user inputs specifications for a city model using a device application or web browser. For example, they input information such as "population 500,000, hot and humid climate, extensive public transportation infrastructure." The device's built-in emotion engine also collects emotional data through facial recognition and voice analysis. The input is entered into a form on the device and then saved as specification data on the device.

[1061] input:

[1062] City model specifications (e.g., population 500,000, hot and humid climate, extensive public transport infrastructure)

[1063] The user's emotional state (e.g., joy, anticipation)

[1064] output:

[1065] Specification data and sentiment data in JSON or XML format

[1066] Step 2: Sending specification data and emotion data

[1067] The device packages the user's input specifications and emotion data and sends it to the server as an HTTP request, organized in JSON or XML format.

[1068] input:

[1069] Specification data and sentiment data in JSON or XML format

[1070] output:

[1071] HTTP request sent to the server

[1072] Step 3: Data generation

[1073] The server receives the HTTP request and analyzes the specification data and emotion data. The analyzed data is passed to the generation AI, which generates a fictional city model based on the specified population size, regional characteristics, and climatic conditions. Emotion data is also taken into account during this process, and a city model that matches the user's emotions is generated.

[1074] input:

[1075] Specification and sentiment data in HTTP requests

[1076] output:

[1077] Generated city model

[1078] Specific behavior:

[1079] The server parses the data using a JSON parser

[1080] Input the analyzed data into the generative AI

[1081] Generative AI generates fictional city models

[1082] Step 4: Saving the city model and emotion data

[1083] The server stores the generated city model and emotion data in a database, where it can be quickly accessed for later use in simulations.

[1084] input:

[1085] Generated city model and emotion data

[1086] output:

[1087] City models and emotion data stored in a database

[1088] Specific behavior:

[1089] The server establishes a database connection and stores the data via SQL or NoSQL

[1090] Step 5: Serve the model

[1091] The server sends the generated city model to the terminal as an HTTP response, which is again converted to JSON or XML format.

[1092] input:

[1093] City models and emotion data stored in a database

[1094] output:

[1095] HTTP response sent to the device

[1096] Specific behavior:

[1097] The server retrieves the data from the database

[1098] Convert data to JSON or XML format

[1099] Send to the terminal as an HTTP response

[1100] Step 6: Run the simulation

[1101] The device interprets the received city model data and emotion data and displays them on the user interface. The user configures the simulation settings, starts the simulation engine, and executes various simulations. The simulation results, which reflect the user's emotional state, are displayed intuitively.

[1102] input:

[1103] City model and emotion data received as an HTTP response

[1104] output:

[1105] Simulation results

[1106] Specific behavior:

[1107] The device analyzes the city model data and visualizes it on the user interface.

[1108] The user configures the simulation settings and inputs the settings data into the simulation engine.

[1109] The simulation engine runs various scenarios and displays the results in the user interface.

[1110] The above is a detailed description of each processing step. This system allows users to quickly generate and simulate realistic city models that reflect their emotional states.

[1111] (Application example 2)

[1112] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1113] Conventional simulation systems do not take into account the user's emotional state, and therefore are unable to provide highly accurate simulation results that meet the user's intuition and desires. Furthermore, because they rely on specific city data, the simulation lacks fairness and diversity. Therefore, there is a need to provide simulation results that are more convincing to users.

[1114] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1115] In this invention, the server includes: [means for generating a fictional city model using a generation AI based on the specification data and emotion data]; [means for storing the generated city model and emotion data in a database]; and [means for transmitting the generated city model and emotion data to the terminal.] This makes it possible to [generate fair and diverse city models that reflect the user's emotions and provide highly accurate simulation results].

[1116] A "user" is a person or organization that inputs specification data and emotion data to generate a fictional city model using the simulation system.

[1117] A "terminal" is a device that allows a user to input specification data and emotion data and exchange data with the server. This includes smartphones, PCs, tablets, etc.

[1118] "Specification data" is information that indicates the characteristics and conditions of the city model used in the simulation, and includes data on population, climate, transportation infrastructure, etc.

[1119] "Emotional data" refers to information that reflects the user's emotional state, and includes data obtained through facial recognition and voice analysis.

[1120] A "server" is a device or system that receives specification data and emotion data sent from a terminal, generates a fictional city model, stores it in a database, and then returns the data to the terminal.

[1121] "Generative AI" includes artificial intelligence models for generating fictional city models based on specification data and emotion data.

[1122] A "fictional city model" is not a real city, but is digital data that virtually represents the structure, population, climate, etc. of a city created for simulation purposes.

[1123] The "database" is a system that systematically stores the generated city models and emotion data, making them available for reference as needed.

[1124] "Simulation" is the process of predicting movements and situations under specific conditions based on a generated fictional city model and analyzing the results.

[1125] A "simulation scenario" is generated based on the user's emotional data and specification data, and indicates the assumptions and specific situations used in the simulation.

[1126] MODE FOR CARRYING OUT THE INVENTION

[1127] To put this invention into practice, it is necessary to build a city model generation system based on emotion data. A system is realized in which users, terminals, and servers work together to generate a fictional city model that reflects the user's emotions and perform a simulation.

[1128] System Configuration

[1129] 1. User Input

[1130] The user inputs the specifications of the city model to be used in the simulation and the emotional data at the time of input into the device. For example, the user can input specification data such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" via an application on the device or a web browser.

[1131] 2. Collecting Emotional Data

[1132] The device collects the user's emotional data along with the device's specifications. Specifically, it uses facial recognition and speech analysis APIs to detect the user's emotional state and captures this data. Examples of software used include the Facial Recognition API and the Speech Analysis API.

[1133] 3. Sending specification data and emotion data

[1134] The device packages the specification data and emotion data, converts it into JSON or XML format, and sends it to the server using an HTTP request.

[1135] 4. Data analysis and urban model generation

[1136] The server receives the HTTP request and analyzes the specification data and sentiment data. This data analysis is performed using a generative AI, which uses a model trained using TensorFlow or PyTorch to generate a fictional city model.

[1137] 5. Saving city models and emotion data

[1138] The server stores the generated city model and related emotion data in a database, such as PostgreSQL or MongoDB. The stored data is organized in a referenceable state so that it can be used later in the simulation.

[1139] 6. Providing city models

[1140] The server returns the generated city model and emotion data to the device, where the data is converted back to JSON or XML format and sent as an HTTP response.

[1141] 7. Running the Simulation

[1142] The device interprets the city model data and emotion data received from the server and displays them on the user interface. The user then configures the simulation settings based on this information, launches the simulation engine, and runs simulations of various scenarios (e.g., performance evaluation of self-driving cars, disaster risk assessment, etc.). During this process, the emotion data is also taken into account and reflected in the simulation results.

[1143] Specific examples

[1144] For example, consider a case where a user uses the system to evaluate a new self-driving car algorithm. The user inputs the following specifications into the device: "population 500,000, hot and humid climate, extensive public transportation infrastructure," and sends them to the server. At the same time, facial recognition and voice analysis detect the emotional state of "anxiety." The server uses generative AI based on this data to generate a fictional city model. This city model incorporates specific scenarios that take the user's anxieties into account. The generated data is stored in a database and sent to the device. The user runs a simulation based on this city model, and the results are displayed with the emotional data reflected.

[1145] Prompt Sentence Examples

[1146] "We generate a city model with a population of 500,000, a hot and humid climate, and an extensive public transport infrastructure, and create scenarios for simulating autonomous vehicles, taking into account user concerns."

[1147] This invention generates a fictional city model that reflects the user's emotions, making it possible to provide fair and diverse simulation results, which provide intuitive and convincing results for the user.

[1148] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1149] Step 1:

[1150] The user inputs the specifications of the city model to be used in the simulation into the terminal.

[1151] Input: A user enters city specifications such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" into a form in a device application or web browser.

[1152] Output: Specification data entered into the terminal.

[1153] Specific actions: The user uses the keyboard or touchscreen to enter data about the city's characteristics into the terminal and presses the done button.

[1154] Step 2:

[1155] The device collects emotional data.

[1156] Input: Emotional data such as the user's facial expressions and voice.

[1157] Output: Emotion data processed by the emotion engine.

[1158] Specific operation: The device uses the camera and microphone to obtain the user's emotional data using facial recognition and speech analysis APIs (e.g., Facial Recognition API and Speech Analysis API). The emotional data is output in the form of "anxiety" or "happiness."

[1159] Step 3:

[1160] The terminal packages the specification data and emotion data and transmits them to the server.

[1161] Input: User-entered specification data and device-collected emotion data.

[1162] Output: Packaged data in JSON or XML format that is sent to the server.

[1163] Specific operation: The device converts the specification data and emotion data into JSON / XML format and sends it to the server using an HTTP request.

[1164] Step 4:

[1165] The server analyzes the received specification data and emotion data and generates a fictional city model using a generative AI model.

[1166] Input: Specification data and emotion data received by the server in JSON / XML format.

[1167] Output: The generated fictional city model.

[1168] Specific operation: The server parses and analyzes the received data, and then uses that data to generate a city model that meets the criteria using a generative AI model (for example, using a framework such as TensorFlow or PyTorch).

[1169] Step 5:

[1170] The server stores the generated city model and emotion data in a database.

[1171] Input: The generated city model and associated sentiment data.

[1172] Output: City model and emotion data stored in a database.

[1173] Specific operation: The server uses a database management system (e.g., PostgreSQL or MongoDB) to systematically store the generated city model and emotion data.

[1174] Step 6:

[1175] The server transmits the generated city model and emotion data to the terminal.

[1176] Input: City model and emotion data stored in a database.

[1177] Output: City model and emotion data in JSON or XML format sent to the device.

[1178] Specific operation: The server reads the necessary data from the database, converts it back into JSON / XML format, and sends it to the terminal as an HTTP response.

[1179] Step 7:

[1180] The device runs a simulation based on the generated city model and emotion data.

[1181] Input: City model data and emotion data sent from the server.

[1182] Output: Simulation results.

[1183] Specific operation: The device displays the received data on the user interface, and the user configures the simulation. The simulation engine then executes various scenarios and visualizes the results, taking into account the emotion data.

[1184] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1185] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1186] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1187] [Fourth embodiment]

[1188] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1189] 7, a 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.

[1190] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1191] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1192] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1193] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1194] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1195] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1196] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1197] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1198] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1199] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1200] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1201] This invention is a system for generating and simulating fictitious city models that can be used in fields such as autonomous driving, map applications, urban development, and disaster prevention. This system generates a large number of fictitious city models at high speed based on the specifications of the city model entered by the user, and enables various simulations to be performed using the models.

[1202] System configuration

[1203] 1. User Input

[1204] The user inputs the specifications of the city model to be used in the simulation into the terminal. For example, the user specifies "a city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure."

[1205] 2. Sending specification data

[1206] The device sends the specification data entered by the user to the server, packaged in JSON or XML format, and sent as an HTTP request.

[1207] 3. Data Generation

[1208] The server analyzes the received specification data and generates a fictional city model using a generative AI. The generated data includes the following information:

[1209] Population data: population, age structure, occupational distribution

[1210] Climate data: average annual temperature, precipitation

[1211] Geographic data: road networks, public transport

[1212] Resident data: Resident profile, lifestyle

[1213] 4. Save the city model

[1214] The server saves the generated city model in a database, where it can be referenced for later use in simulations.

[1215] 5. Submitting the Model

[1216] The server sends the generated city model back to the device, where the data is converted back into JSON or XML format and sent as an HTTP response.

[1217] 6. Running the Simulation

[1218] The device interprets the city model data received from the server and displays it on the user interface. The user can then configure the simulation settings based on this data and start the simulation engine to run simulations of various scenarios (e.g., performance evaluation of autonomous vehicles, disaster risk assessment, etc.).

[1219] Specific examples

[1220] For example, suppose a user uses the system to evaluate a new self-driving car algorithm. In this case, the user inputs the following specifications into their device: "A city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure." The specifications are then sent to the server. Based on this, the server uses generative AI to generate a fictitious city model and stores it in a database. The generated city model is then sent to the device, and the user runs a self-driving car simulation based on this city model. By analyzing the results of the simulation, the user can evaluate the performance of the new algorithm under fair and diverse conditions.

[1221] This system makes it possible to quickly run fair and diverse simulations without relying on real city data. Furthermore, it can avoid the reputational damage that can occur when using real cities in simulations such as disaster risk assessments. This makes it possible to provide a practical and ethical simulation environment.

[1222] The processing flow will be explained below.

[1223] Step 1:

[1224] The user inputs the specifications of the fictional city model to be used in the simulation into the device, such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" into an application on the device or a web browser input form.

[1225] Step 2:

[1226] The terminal packages the input specification data, organizes it in JSON or XML format, and converts it into a format that is easy to use for subsequent server processing.

[1227] Step 3:

[1228] The terminal sends the packaged specification data to the server as an HTTP request. Specifically, when the send button is clicked, the terminal sends the data to the specified URL of the server as a POST request.

[1229] Step 4:

[1230] The server receives the HTTP request and parses the requested specification data. Specifically, it deserializes the received data and stores it on the server as a JSON object or XML document.

[1231] Step 5:

[1232] The server launches a generation AI based on the analyzed specification data, which then begins the process of generating a fictional city model based on the specified population size, regional characteristics, and climatic conditions.

[1233] Step 6:

[1234] The server-based generation AI generates detailed population data (population count, age structure, occupational distribution), climate data (average annual temperature, precipitation), geographic data (road network, public transportation), and resident data (resident profiles, lifestyles).The generation AI uses existing datasets and statistical models to perform probabilistic and rule-based generation.

[1235] Step 7:

[1236] The server integrates the various data generated and compiles a fictional city model into a single dataset. Specifically, it combines each individual data set to create a consistent city model.

[1237] Step 8:

[1238] The server stores the integrated fictional city model in a database, where the data is indexed for easy search and access later.

[1239] Step 9:

[1240] The server sends the saved fictional city model to the terminal. Specifically, it reconverts the city model into JSON or XML format and sends it to the terminal as an HTTP response.

[1241] Step 10:

[1242] The terminal receives the transmitted city model data and displays it on the user interface. Specifically, it interprets the received data and displays it on the GUI (Graphical User Interface) as data for display.

[1243] Step 11:

[1244] The user checks the displayed city model and configures the simulation, for example, entering parameters to start a simulation of an autonomous vehicle.

[1245] Step 12:

[1246] When the user presses the simulation start button, the device starts the simulation engine, which then begins calculations based on the generated city model.

[1247] Step 13:

[1248] The device runs the simulation and generates results, which are calculated and visualized as performance data and risk assessments.

[1249] Step 14:

[1250] The terminal displays the calculation results to the user, who can then evaluate and analyze them.

[1251] Example 1

[1252] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1253] There is a need for a system that allows users to easily input specifications for the city model to be used in the simulation and quickly generate a large number of fictitious city models. The generated city models must also contain realistic data, which will improve the accuracy of various simulations. Furthermore, there is a need for a system that allows users to easily run simulations based on the generated city models and use the results for evaluation and analysis.

[1254] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1255] In this invention, the server includes means for a user to input specifications of a city model to be used in a simulation into a user device, means for the user device to transmit the specification data to the server, means for the server to generate a fictitious city model using a generative model based on the specification data, means for the server to save the generated city model in a storage device, means for the server to transmit the generated city model to the user device, and means for the user device to execute a simulation based on the generated city model. This makes it possible to quickly generate a variety of city models tailored to each user and execute realistic simulations.

[1256] A "user device" is a device through which a user inputs specifications for a city model to be used in a simulation, and is an electronic device such as a computer or smartphone.

[1257] "Specification data" is data that describes the characteristics and conditions of a city model entered by the user, and specifically includes information such as population, climate, and geographical conditions.

[1258] A "server" is a computer system that receives specification data sent from user devices and generates, stores, and transmits analysis and city models.

[1259] A "generative model" is an algorithm or AI technology that generates a fictional city model based on specified specification data.

[1260] A "city model" is a digital representation of a fictional city generated based on specification data, and is a dataset that includes population data, climate data, geographic data, resident data, etc.

[1261] A "storage device" is a database or storage system for saving the city model generated by the server.

[1262] "Simulation" is the process of conducting virtual experiments and analyses based on the generated urban model.

[1263] This invention is a system for generating and simulating fictional city models that can be used in fields such as autonomous driving, map applications, urban development, and disaster prevention. The system operates by combining hardware and software, including user devices, servers, generative models, and storage devices.

[1264] To use the system, a user first accesses their device and logs in. The user then fills in the specifications for the city model in an input form provided by the system. For example, the user specifies a city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure. This specification data is converted into JSON or XML format and sent to the server via an HTTP request.

[1265] The server receives the HTTP request and analyzes the specification data. Based on this analyzed data, the server uses a generative model to generate a fictional city model. For example, OpenAI GPT-3 is used as the generative model. The generative model prompt uses text such as "Generate a city model with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure."

[1266] The generated city model includes various information such as population data (number of people, age structure, occupational distribution), climate data (average annual temperature, precipitation), geographic data (road network, public transportation), and resident data (resident profiles, lifestyles). This data is stored in the server's storage device.

[1267] The saved city model data is converted back to JSON or XML format and sent to the user device as an HTTP response. The user device receives this data, interprets it, and displays it on the user interface, allowing the user to visually check an overview of the generated city model.

[1268] After the city model is displayed on the user device, the user configures the simulation settings, for example, entering conditions for evaluating the algorithms of a self-driving car. When the user clicks the "Start Simulation" button, the simulation engine is launched and the specified scenario is simulated. The simulation results are reflected in the user interface, allowing the user to evaluate and analyze them.

[1269] As a concrete example, consider the procedure for a user evaluating a new autonomous vehicle algorithm. The user inputs the specifications for a city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure, and sends them to a server. The server uses the generative model to generate a fictitious city model and saves it to a storage device. The generated city model is then sent back to the user's device, and the user runs a simulation based on this city model.

[1270] This system allows users to quickly run fair and diverse simulations without relying on real city data. It also helps to avoid reputational damage that can occur when using real cities for disaster risk assessments, and provides an ethical simulation environment.

[1271] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1272] Step 1:

[1273] The user accesses a terminal and logs in. The user enters the specifications of the city model for the simulation into an input form. For example, the user enters the specifications as "a city with a population of 500,000, a hot and humid climate, and an extensive public transportation infrastructure." Input data: City model specifications. Output data: Specification data converted into JSON or XML format.

[1274] Step 2:

[1275] The terminal converts the specification data entered by the user into JSON or XML format. The terminal generates an HTTP request and sends this specification data to the server. Input data: City model specifications entered by the user. Output data: Specification data in JSON or XML format sent to the server.

[1276] Step 3:

[1277] The server receives the HTTP request and extracts the specification data. The server parses the specification data to detect the required parameters. Input data: Specification data in JSON or XML format sent as the HTTP request. Output data: Parsed parameter set.

[1278] Step 4:

[1279] The server calls the API of the generating AI (e.g., OpenAI GPT-3). The prompt states, "Generate a city model with a population of 500,000, a hot and humid climate, and extensive public transportation infrastructure." Input data: The analyzed parameter set. Output data: The fictitious city model data returned by the generating AI.

[1280] Step 5:

[1281] The server verifies the generated city model data and checks for errors. If there are no errors, it saves it to a database. For example, PostgreSQL or MongoDB is used. Input data: City model data provided by the generation AI. Output data: City model data saved in the database or an error log.

[1282] Step 6:

[1283] The server converts the saved city model data back into JSON or XML format and packages it as an HTTP response. The server sends the HTTP response to the user's device. Input data: City model data saved in the database. Output data: City model data in JSON or XML format sent to the user's device.

[1284] Step 7:

[1285] The terminal receives the HTTP response from the server and interprets the city model data. The terminal displays an overview of the city model in the user interface. Input data: City model data in JSON or XML format sent from the server. Output data: An overview of the city model displayed in the user interface.

[1286] Step 8:

[1287] The user sets up the simulation, for example by filling out a form to evaluate an autonomous vehicle algorithm. The user clicks the "Start Simulation" button. Input data: The city model displayed in the user interface and additional simulation settings. Output data: Simulation parameters sent to the simulation engine.

[1288] Step 9:

[1289] The terminal starts the simulation engine and runs a simulation of the specified scenario. The results of the simulation are displayed on the user interface. For example, data such as traffic volume and accident occurrence rate are displayed. Input data: Simulation parameters. Output data: Simulation results displayed on the user interface.

[1290] (Application example 1)

[1291] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1292] Evaluating autonomous vehicle algorithms fairly and efficiently under diverse environmental conditions is challenging. Using real-world urban data can lead to ethical issues and misinterpretations. Furthermore, relying on real urban environments limits the flexibility of simulations.

[1293] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1294] In this invention, the server includes: [means for a user to input specifications of the city model to be used in the simulation into a terminal;] [means for the terminal to transmit the specification data to the server; and] [means for the server to generate a fictitious city model using a generation AI based on the specification data.] This makes it possible to simulate autonomous vehicles using a fictitious city model. This makes it possible to simulate under a variety of environmental conditions, enabling fair and efficient evaluation without relying on real city data.

[1295] A "user" is a person or organization that operates the system and inputs specifications for the city model.

[1296] A "simulation" is an experiment or trial conducted in a virtual environment under specific conditions or settings.

[1297] A "city model" is a dataset of a virtual city that includes attributes such as population, climate, geography, and transportation infrastructure.

[1298] "Specification data" is data entered by the user that describes the conditions and characteristics of the city model.

[1299] A "terminal" is a device that allows a user to input specifications for a city model and run a simulation.

[1300] "Server" means a central processing unit that receives specification data, processes the data, and stores and transmits the generated city model.

[1301] "Generative AI" is an artificial intelligence technology that generates fictional city models based on specified specification data.

[1302] A "database" is an information system that stores the generated city models and makes them accessible as needed.

[1303] "3D display" is the process of displaying the generated city model in a 3D visual format.

[1304] An "autonomous vehicle" is a vehicle that has the ability to drive autonomously without human operation.

[1305] "Transportation Data" means information about roads, public transportation, traffic volumes and patterns within a city.

[1306] "Infrastructure data" is information about a city's basic public facilities and systems (e.g., transportation, electricity, water supply).

[1307] "Fictional resident profiles" are data describing the characteristics and attributes of residents living in a virtual city.

[1308] "Traffic simulation data" refers to data that represents the results of a simulation based on traffic conditions and scenarios.

[1309] This invention is a system that generates a fictitious city model based on specifications for the city model entered by a user and uses the model to simulate an autonomous vehicle. The system includes a terminal operated by the user, a server that sends and receives data, a generation AI that generates the city model, and a database that stores and manages the generated city model.

[1310] System configuration

[1311] 1. User Input

[1312] Users input specifications for the city model they want to use in the simulation into the device, including attributes such as population, climate, and transportation infrastructure. For example, they might input, "Please generate a hot, humid city with a population of 500,000. I want to simulate a scenario with autonomous vehicles in an environment with extensive public transportation infrastructure."

[1313] 2. Sending specification data

[1314] The terminal sends the entered specification data to the server, which packages the data in JSON or XML format and sends it as an HTTP request. The server receives it and analyzes it.

[1315] 3. City model generation

[1316] The server generates a fictional city model using generative AI based on the received specification data. This generative AI is implemented using TensorFlow and Python. The generated city model includes population data, climate data, geographic data, resident data, traffic data, and infrastructure data.

[1317] 4. Data storage

[1318] The generated city model is saved in a database such as PostgreSQL, making it reusable for future simulations.

[1319] 5. Submitting the Model

[1320] The server sends the saved city model to the device, where it is converted back into JSON or XML format and sent as an HTTP response. The device receives and interprets this data.

[1321] 6. Running the Simulation

[1322] The device then runs a simulation based on the received city model. Specifically, it displays a city model generated using Three.js or similar software in three dimensions, and then simulates autonomous vehicles from that. The simulation includes scenarios such as traffic congestion assessment, accident risk assessment, and route optimization.

[1323] Specific examples

[1324] For example, if a user inputs a specification such as "a city with a population of 1 million, a dry climate, and an advanced public transportation infrastructure," the system can generate a fictional city model based on that specification and run simulations of autonomous vehicles in this city. This simulation allows the performance of new autonomous driving algorithms to be evaluated under a variety of conditions.

[1325] An example of a prompt to input to a generative AI model is as follows:

[1326] "Generate a hot, humid city with a population of 500,000. Simulate autonomous vehicle scenarios in an environment with extensive public transport infrastructure."

[1327] By implementing this invention, it is possible to quickly perform fair and diverse simulations without relying on real city data. Furthermore, it is possible to avoid reputational damage that can occur when using real cities in simulations such as disaster risk assessment. This makes it possible to provide a fair and practical simulation environment.

[1328] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1329] Step 1:

[1330] User input of specification data

[1331] The user inputs the specifications of the city model to be used in the simulation into the terminal, such as the population, climate, public transportation infrastructure, etc. At this time, the input data is collected through a form on the terminal.

[1332] Input: User-supplied specification data (e.g., population 500,000, high temperature and humidity, extensive public transport infrastructure)

[1333] Output: Specification data (e.g., data packaged in JSON format)

[1334] Step 2:

[1335] Sending specification data to the server

[1336] The terminal sends the specification data entered by the user to the server as an HTTP request.

[1337] Input: Specification data (JSON format)

[1338] Output: Specification data received by the server

[1339] Step 3:

[1340] The server analyzes the specification data and starts the generation AI

[1341] The server analyzes the received specification data and generates a fictional city model using generative AI, which uses Python and TensorFlow. Based on the specification data, the generative AI generates population data, climate data, geographic data, resident data, traffic data, and infrastructure data.

[1342] Input: Received specification data

[1343] Output: Generated city model (including various data)

[1344] Step 4:

[1345] Saving the generated city model

[1346] The server stores the generated city model in a database such as PostgreSQL.

[1347] Input: Generated city model

[1348] Output: City model stored in a database

[1349] Step 5:

[1350] Providing the generated city model

[1351] The server then sends the saved city model to the device, again as an HTTP response.

[1352] Input: City model stored in a database

[1353] Output: The city model received by the device

[1354] Step 6:

[1355] 3D display of city models on terminals

[1356] The device then runs a simulation based on the received city model, displaying the city model in three dimensions using Three.js.

[1357] Input: Received city model

[1358] Output: 3D city model

[1359] Step 7:

[1360] Running simulations of autonomous vehicles

[1361] The device simulates autonomous vehicles within a 3D city model, including traffic congestion assessment, accident risk assessment, and route optimization.

[1362] Input: 3D city model, simulation settings (starting point, destination, etc.)

[1363] Output: Simulation results (e.g. traffic congestion map, accident risk assessment results, etc.)

[1364] This allows users to run simulations of autonomous vehicles under a variety of conditions. The entire system works in concert to achieve fair and efficient simulations that do not rely on real-world urban data.

[1365] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1366] This invention combines an emotion engine that recognizes the user's emotions with a system for generating and simulating fictional city models that can be used in fields such as autonomous driving, map applications, urban development, and disaster prevention. This system generates a fictional city model based on the specifications of the city model entered by the user and their emotional state at the time, and enables various simulations to be performed using that model.

[1367] System configuration

[1368] 1. User Input

[1369] The user inputs the specifications of the city model to be used in the simulation into the device. For example, the user inputs information such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" into an application on the device or an input form in a web browser. During this input, the emotion engine detects the user's emotional state through facial recognition and voice analysis.

[1370] 2. Sending specification data and emotion data

[1371] The device packages the specification data and emotion data entered by the user. The specification data and emotion data are organized in JSON or XML format and converted into a format that is easy to use for subsequent server processing. The device then sends the packaged data to the server as an HTTP request.

[1372] 3. Data Generation

[1373] The server receives the HTTP request and analyzes the requested specification data and emotion data. The analyzed data is passed to the generation AI, which then generates a fictional city model based on the specified population size, regional characteristics, and climatic conditions. During this process, emotion data is also taken into account, and the city model desired by the user is generated.

[1374] 4. Saving city models and emotion data

[1375] The server stores the generated city model and associated emotion data in a database, where the model and emotion data can be referenced for later use in simulations.

[1376] 5. Submitting the Model

[1377] The server sends the generated city model back to the device, where the data is again converted into JSON or XML format and sent as an HTTP response.

[1378] 6. Running the Simulation

[1379] The device interprets the city model data and emotion data received from the server and displays them on the user interface. The user then configures the simulation settings based on this information, launches the simulation engine, and runs simulations of various scenarios (e.g., performance evaluation of self-driving cars, disaster risk assessment, etc.). During this process, emotion data is also taken into account and reflected in the simulation results.

[1380] Specific examples

[1381] For example, suppose a user uses the system to evaluate a new self-driving car algorithm. In this case, the user inputs the following specifications into their device: "Population 500,000, hot and humid climate, extensive public transportation infrastructure" and sends them to the server. Based on this, the server uses generative AI to generate a fictional city model and stores it in a database. At the same time, the emotion engine collects and stores the user's emotional data at the time of input. The generated city model and emotional data are then sent to the device, and the user runs a self-driving car simulation based on this city model and emotional data. The results of this simulation are visualized as an evaluation result that takes into account the user's emotional state.

[1382] This system makes it possible to quickly perform fair and diverse simulations without relying on real-world city data. Furthermore, by combining it with an emotion engine, it is possible to obtain more realistic simulation results that take into account the user's emotional state, providing users with more intuitive and convincing results.

[1383] The processing flow will be explained below.

[1384] Step 1:

[1385] The user inputs the specifications of the fictional city model to be used in the simulation into the device, such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" into an application on the device or a form in a web browser.

[1386] Step 2:

[1387] When the device packages the input specification data, it activates the emotion engine, which uses the camera and microphone to recognize the user's face and analyze their voice to detect their emotional state (e.g., joy, sadness, excitement, etc.).

[1388] Step 3:

[1389] The device integrates the detected emotion data with the input specification data and packages it in JSON or XML format, allowing the specification data and emotion data to be treated as a single dataset.

[1390] Step 4:

[1391] The device sends the packaged data to the server as an HTTP request, and the sent data is sent as a POST request to the specified URL on the server.

[1392] Step 5:

[1393] The server receives the HTTP request and deserializes the sent data. The deserialized data is expanded within the server as specification data and emotion data.

[1394] Step 6:

[1395] The server receives the expanded specification data and emotion data and activates the generation AI. The generation AI generates the following data based on the specified population size, regional characteristics, and climatic conditions:

[1396] Population data (e.g., population numbers, age distribution, occupational distribution)

[1397] Climate data (e.g., average annual temperature, precipitation)

[1398] Geographic data (e.g. road networks, public transport)

[1399] Resident data (e.g., resident profiles, lifestyles)

[1400] Step 7:

[1401] The generation AI in the server uses the detected emotion data to adjust the city model to fit the user's emotional state. This process ensures that the generated city model is more in line with the user's intentions and emotions.

[1402] Step 8:

[1403] The server integrates the various data generated and compiles it into a single fictional city model, which is then organized into a coherent city model.

[1404] Step 9:

[1405] The server stores the integrated fictional city model and associated emotion data in a database, which is indexed for easy access and reference later.

[1406] Step 10:

[1407] The server sends the saved city model and emotion data to the device, where it is converted back into JSON or XML format and sent as an HTTP response.

[1408] Step 11:

[1409] The terminal receives the transmitted city model data and emotion data and displays them on the user interface. Based on this, the user can check the city model and set up the simulation.

[1410] Step 12:

[1411] When the user sets the simulation conditions and presses the start button, the device starts the simulation engine, which then begins calculation processing based on the generated city model and emotion data.

[1412] Step 13:

[1413] The device runs the simulation and generates results, which are calculated as performance data and risk assessments and visualized in a way that is intuitive to the user.

[1414] Step 14:

[1415] The device displays the calculation results to the user, who can then evaluate and analyze them. Emotional data is also reflected in the results, which are displayed based on the user's emotional state.

[1416] Example 2

[1417] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1418] When conducting simulations using virtual city models, it is necessary to quickly obtain highly realistic simulation results that take into account the user's emotional state. However, current technology makes it difficult to reflect the user's emotional data in the simulation. Therefore, it is necessary to realize a city model generation and simulation system that includes emotional data.

[1419] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1420] In this invention, the server includes: [means for generating a fictional city model using a generation AI based on the specification data and emotion data]; [means for storing the generated city model and emotion data in a database]; and [means for transmitting the generated city model and emotion data to the terminal.] This enables [the generation and simulation of a highly realistic city model that takes into account the user's emotion data].

[1421] "User" refers to a person or organization that uses this system to input specifications for a city model and run a simulation.

[1422] "Terminal" refers to a device used by a user to input specifications for a city model and communicate with the server, and includes computers, smartphones, tablets, etc.

[1423] "Specification data" refers to information about the city model that the user inputs into the terminal, and includes population data, climate data, geographical data, resident data, and the like.

[1424] "Emotion data" is data collected by the terminal's emotion engine from the user's facial expressions and voice, and includes information indicating the user's emotional state.

[1425] "Server" refers to the computer system that receives specification data and emotion data, generates a city model using generative AI, and stores and transmits that data.

[1426] "Generative AI" refers to artificial intelligence algorithms that generate fictional city models based on specification and emotion data.

[1427] A "city model" refers to a dataset that represents the structure and characteristics of a fictional city generated by generative AI based on specification data and emotion data.

[1428] "Database" refers to an information system for storing the generated city model and associated emotion data and retrieving them as needed.

[1429] "Simulation Engine" refers to the software or algorithms used to simulate various scenarios based on the generated city model and emotion data.

[1430] "JSON format" refers to a lightweight data exchange format for storing and transferring specification and sentiment data in a structured manner.

[1431] "HTTP request" refers to the communication protocol used when a terminal sends data to a server.

[1432] "HTTP response" refers to the communication protocol used when a server sends data to a terminal.

[1433] This invention is a system that generates a fictional city model based on the specifications of the city model entered by the user and the emotional state at the time, and enables various simulations to be performed using that model. By incorporating an emotion engine, this system can collect the user's emotional state in real time and provide more realistic simulations that reflect this.

[1434] Hardware and Software Configuration

[1435] 1. Terminal

[1436] The terminal is a device used by users to input specifications for the city model. Typical hardware includes computers, smartphones, and tablets. This terminal includes applications that implement various input forms and software that runs on a web browser. It also incorporates an emotion engine that collects emotion data from users through facial recognition and voice analysis.

[1437] 2. Server

[1438] The server receives the specification data and emotion data sent from the device and generates a fictional city model using generative AI based on this data. The server stores the generated city model and emotion data in a database and transmits them to the device as needed. The server requires a high-performance processing unit and large-capacity storage. Specific software includes a data analysis algorithm, generative AI model, and database management system.

[1439] Specific examples

[1440] For example, a user might use this system to evaluate a new self-driving car algorithm. The user would enter specifications for a city model, such as a population of 500,000, a hot and humid climate, and extensive public transportation infrastructure, into an application on the device or an input form in a web browser. At the same time, the device's emotion engine would perform facial recognition and voice analysis to collect emotion data such as "happiness" and "expectation."

[1441] The device then converts the specification data and emotion data into JSON format and sends it to the server as an HTTP request. The server then analyzes the received data and passes it to a generation AI to generate a fictional city model. The device also takes into account the user's emotion data, resulting in a city model that reflects more positive elements.

[1442] The generated city model and emotion data are stored in a database by the server. The server then transmits the stored data to the device, which interprets it and displays it on the user interface. The user uses this data to configure the simulation settings for evaluating the performance of the autonomous vehicle and launch the simulation engine. The simulation results also reflect the user's emotional state, making the evaluation more intuitive and convincing.

[1443] Prompt Sentence Examples

[1444] Urban specifications: 500,000 people, hot and humid climate, extensive public transport infrastructure

[1445] Emotion data: joy, anticipation

[1446] This system enables users to quickly run diverse and fair simulations without relying on real-world city data. Furthermore, the emotion engine provides simulation results that reflect the user's emotional state in real time, resulting in more intuitive and convincing results.

[1447] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1448] Step 1: User Input

[1449] The user inputs specifications for a city model using a device application or web browser. For example, they input information such as "population 500,000, hot and humid climate, extensive public transportation infrastructure." The device's built-in emotion engine also collects emotional data through facial recognition and voice analysis. The input is entered into a form on the device and then saved as specification data on the device.

[1450] input:

[1451] City model specifications (e.g., population 500,000, hot and humid climate, extensive public transport infrastructure)

[1452] The user's emotional state (e.g., joy, anticipation)

[1453] output:

[1454] Specification data and sentiment data in JSON or XML format

[1455] Step 2: Sending specification data and emotion data

[1456] The device packages the user's input specifications and emotion data and sends it to the server as an HTTP request, organized in JSON or XML format.

[1457] input:

[1458] Specification data and sentiment data in JSON or XML format

[1459] output:

[1460] HTTP request sent to the server

[1461] Step 3: Data generation

[1462] The server receives the HTTP request and analyzes the specification data and emotion data. The analyzed data is passed to the generation AI, which generates a fictional city model based on the specified population size, regional characteristics, and climatic conditions. Emotion data is also taken into account during this process, and a city model that matches the user's emotions is generated.

[1463] input:

[1464] Specification and sentiment data in HTTP requests

[1465] output:

[1466] Generated city model

[1467] Specific behavior:

[1468] The server parses the data using a JSON parser

[1469] Input the analyzed data into the generative AI

[1470] Generative AI generates fictional city models

[1471] Step 4: Saving the city model and emotion data

[1472] The server stores the generated city model and emotion data in a database, where it can be quickly accessed for later use in simulations.

[1473] input:

[1474] Generated city model and emotion data

[1475] output:

[1476] City models and emotion data stored in a database

[1477] Specific behavior:

[1478] The server establishes a database connection and stores the data via SQL or NoSQL

[1479] Step 5: Serve the model

[1480] The server sends the generated city model to the terminal as an HTTP response, which is again converted to JSON or XML format.

[1481] input:

[1482] City models and emotion data stored in a database

[1483] output:

[1484] HTTP response sent to the device

[1485] Specific behavior:

[1486] The server retrieves the data from the database

[1487] Convert data to JSON or XML format

[1488] Send to the terminal as an HTTP response

[1489] Step 6: Run the simulation

[1490] The device interprets the received city model data and emotion data and displays them on the user interface. The user configures the simulation settings, starts the simulation engine, and executes various simulations. The simulation results, which reflect the user's emotional state, are displayed intuitively.

[1491] input:

[1492] City model and emotion data received as an HTTP response

[1493] output:

[1494] Simulation results

[1495] Specific behavior:

[1496] The device analyzes the city model data and visualizes it on the user interface.

[1497] The user configures the simulation settings and inputs the settings data into the simulation engine.

[1498] The simulation engine runs various scenarios and displays the results in the user interface.

[1499] The above is a detailed description of each processing step. This system allows users to quickly generate and simulate realistic city models that reflect their emotional states.

[1500] (Application example 2)

[1501] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1502] Conventional simulation systems do not take into account the user's emotional state, and therefore are unable to provide highly accurate simulation results that meet the user's intuition and desires. Furthermore, because they rely on specific city data, the simulation lacks fairness and diversity. Therefore, there is a need to provide simulation results that are more convincing to users.

[1503] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1504] In this invention, the server includes: [means for generating a fictional city model using a generation AI based on the specification data and emotion data]; [means for storing the generated city model and emotion data in a database]; and [means for transmitting the generated city model and emotion data to the terminal.] This makes it possible to [generate fair and diverse city models that reflect the user's emotions and provide highly accurate simulation results].

[1505] A "user" is a person or organization that inputs specification data and emotion data to generate a fictional city model using the simulation system.

[1506] A "terminal" is a device that allows a user to input specification data and emotion data and exchange data with the server. This includes smartphones, PCs, tablets, etc.

[1507] "Specification data" is information that indicates the characteristics and conditions of the city model used in the simulation, and includes data on population, climate, transportation infrastructure, etc.

[1508] "Emotional data" refers to information that reflects the user's emotional state, and includes data obtained through facial recognition and voice analysis.

[1509] A "server" is a device or system that receives specification data and emotion data sent from a terminal, generates a fictional city model, stores it in a database, and then returns the data to the terminal.

[1510] "Generative AI" includes artificial intelligence models for generating fictional city models based on specification data and emotion data.

[1511] A "fictional city model" is not a real city, but is digital data that virtually represents the structure, population, climate, etc. of a city created for simulation purposes.

[1512] The "database" is a system that systematically stores the generated city models and emotion data, making them available for reference as needed.

[1513] "Simulation" is the process of predicting movements and situations under specific conditions based on a generated fictional city model and analyzing the results.

[1514] A "simulation scenario" is generated based on the user's emotional data and specification data, and indicates the assumptions and specific situations used in the simulation.

[1515] MODE FOR CARRYING OUT THE INVENTION

[1516] To put this invention into practice, it is necessary to build a city model generation system based on emotion data. A system is realized in which users, terminals, and servers work together to generate a fictional city model that reflects the user's emotions and perform a simulation.

[1517] System Configuration

[1518] 1. User Input

[1519] The user inputs the specifications of the city model to be used in the simulation and the emotional data at the time of input into the device. For example, the user can input specification data such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" via an application on the device or a web browser.

[1520] 2. Collecting Emotional Data

[1521] The device collects the user's emotional data along with the device's specifications. Specifically, it uses facial recognition and speech analysis APIs to detect the user's emotional state and captures this data. Examples of software used include the Facial Recognition API and the Speech Analysis API.

[1522] 3. Sending specification data and emotion data

[1523] The device packages the specification data and emotion data, converts it into JSON or XML format, and sends it to the server using an HTTP request.

[1524] 4. Data analysis and urban model generation

[1525] The server receives the HTTP request and analyzes the specification data and sentiment data. This data analysis is performed using a generative AI, which uses a model trained using TensorFlow or PyTorch to generate a fictional city model.

[1526] 5. Saving city models and emotion data

[1527] The server stores the generated city model and related emotion data in a database, such as PostgreSQL or MongoDB. The stored data is organized in a referenceable state so that it can be used later in the simulation.

[1528] 6. Providing city models

[1529] The server returns the generated city model and emotion data to the device, where the data is converted back to JSON or XML format and sent as an HTTP response.

[1530] 7. Running the Simulation

[1531] The device interprets the city model data and emotion data received from the server and displays them on the user interface. The user then configures the simulation settings based on this information, launches the simulation engine, and runs simulations of various scenarios (e.g., performance evaluation of self-driving cars, disaster risk assessment, etc.). During this process, the emotion data is also taken into account and reflected in the simulation results.

[1532] Specific examples

[1533] For example, consider a case where a user uses the system to evaluate a new self-driving car algorithm. The user inputs the following specifications into the device: "population 500,000, hot and humid climate, extensive public transportation infrastructure," and sends them to the server. At the same time, facial recognition and voice analysis detect the emotional state of "anxiety." The server uses generative AI based on this data to generate a fictional city model. This city model incorporates specific scenarios that take the user's anxieties into account. The generated data is stored in a database and sent to the device. The user runs a simulation based on this city model, and the results are displayed with the emotional data reflected.

[1534] Prompt Sentence Examples

[1535] "We generate a city model with a population of 500,000, a hot and humid climate, and an extensive public transport infrastructure, and create scenarios for simulating autonomous vehicles, taking into account user concerns."

[1536] This invention generates a fictional city model that reflects the user's emotions, making it possible to provide fair and diverse simulation results, which provide intuitive and convincing results for the user.

[1537] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1538] Step 1:

[1539] The user inputs the specifications of the city model to be used in the simulation into the terminal.

[1540] Input: A user enters city specifications such as "population 500,000, hot and humid climate, extensive public transportation infrastructure" into a form in a device application or web browser.

[1541] Output: Specification data entered into the terminal.

[1542] Specific actions: The user uses the keyboard or touchscreen to enter data about the city's characteristics into the terminal and presses the done button.

[1543] Step 2:

[1544] The device collects emotional data.

[1545] Input: Emotional data such as the user's facial expressions and voice.

[1546] Output: Emotion data processed by the emotion engine.

[1547] Specific operation: The device uses the camera and microphone to obtain the user's emotional data using facial recognition and speech analysis APIs (e.g., Facial Recognition API and Speech Analysis API). The emotional data is output in the form of "anxiety" or "happiness."

[1548] Step 3:

[1549] The terminal packages the specification data and emotion data and transmits them to the server.

[1550] Input: User-entered specification data and device-collected emotion data.

[1551] Output: Packaged data in JSON or XML format that is sent to the server.

[1552] Specific operation: The device converts the specification data and emotion data into JSON / XML format and sends it to the server using an HTTP request.

[1553] Step 4:

[1554] The server analyzes the received specification data and emotion data and generates a fictional city model using a generative AI model.

[1555] Input: Specification data and emotion data received by the server in JSON / XML format.

[1556] Output: The generated fictional city model.

[1557] Specific operation: The server parses and analyzes the received data, and then uses that data to generate a city model that meets the criteria using a generative AI model (for example, using a framework such as TensorFlow or PyTorch).

[1558] Step 5:

[1559] The server stores the generated city model and emotion data in a database.

[1560] Input: The generated city model and associated sentiment data.

[1561] Output: City model and emotion data stored in a database.

[1562] Specific operation: The server uses a database management system (e.g., PostgreSQL or MongoDB) to systematically store the generated city model and emotion data.

[1563] Step 6:

[1564] The server transmits the generated city model and emotion data to the terminal.

[1565] Input: City model and emotion data stored in a database.

[1566] Output: City model and emotion data in JSON or XML format sent to the device.

[1567] Specific operation: The server reads the necessary data from the database, converts it back into JSON / XML format, and sends it to the terminal as an HTTP response.

[1568] Step 7:

[1569] The device runs a simulation based on the generated city model and emotion data.

[1570] Input: City model data and emotion data sent from the server.

[1571] Output: Simulation results.

[1572] Specific operation: The device displays the received data on the user interface, and the user configures the simulation. The simulation engine then executes various scenarios and visualizes the results, taking into account the emotion data.

[1573] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1574] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1575] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1576] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1577] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1578] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1579] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1580] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1581] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1582] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1583] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1584] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1585] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1586] 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.

[1587] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1588] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1589] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1590] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1591] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1592] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1593] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1594] The following is further disclosed regarding the above embodiment.

[1595] (Claim 1)

[1596] [Means for a user to input specifications of the city model to be used in the simulation into the terminal;

[1597] [Means for the terminal to transmit the specification data to a server;

[1598] [Means for the server to generate a fictitious city model using a generation AI based on the specification data;

[1599] [Means for the server to store the generated city model in a database;

[1600] [Means for the server to transmit the generated city model to the terminal;

[1601] [Means for the terminal to execute a simulation based on the generated city model;

[1602] A system including:

[1603] (Claim 2)

[1604] The system of claim 1, wherein the generated city model includes population data, climate data, geographic data, and resident data.

[1605] (Claim 3)

[1606] The system of claim 1, wherein the generating AI generates a name for the city model, sample resident profiles, maps, 3D models, and image and video data.

[1607] "Example 1"

[1608] (Claim 1)

[1609] [Means for a user to input specifications of the city model to be used in the simulation into the user device;

[1610] [Means for the user equipment to transmit the specification data to a server;

[1611] [Means for the server to generate a fictitious city model using a generative model based on the specification data;

[1612] [Means for the server to store the generated city model in a storage device;

[1613] [Means for the server to transmit the generated city model to the user equipment;

[1614] [Means for the user equipment to execute a simulation based on the generated city model;

[1615] A system including:

[1616] (Claim 2)

[1617] The system of claim 1, wherein the generated city model includes population data, climate data, geographic data, and resident data.

[1618] (Claim 3)

[1619] [The system of claim 1, wherein the generative model generates the name of the city model, attributes of sample residents, maps, three-dimensional models, and image and video data.

[1620] "Application Example 1"

[1621] (Claim 1)

[1622] [Means for a user to input specifications of the city model to be used in the simulation into the terminal;

[1623] [Means for the terminal to transmit the specification data to a server;

[1624] [Means for the server to generate a fictitious city model using a generation AI based on the specification data;

[1625] [Means for the server to store the generated city model in a database;

[1626] [Means for the server to transmit the generated city model to the terminal;

[1627] [Means for the terminal to execute a simulation based on the generated city model;

[1628] [Means for displaying the generated city model in three dimensions on the terminal;

[1629] [Means for executing a simulation of an autonomous driving vehicle by the terminal;

[1630] A system including:

[1631] (Claim 2)

[1632] The system of claim 1, wherein the generated city model includes population data, climate data, geographic data, resident data, transportation data, and infrastructure data.

[1633] (Claim 3)

[1634] [The system of claim 1, wherein the generating AI generates the name of the city model, profiles of fictional residents, maps, three-dimensional models, image and video data, and traffic simulation data.

[1635] "Example 2: Combining Emotion Engines"

[1636] (Claim 1)

[1637] [Means for a user to input specifications of the city model to be used in the simulation into the terminal;

[1638] [Means for the terminal to transmit the specification data and emotion data to a server;

[1639] [Means for the server to generate a fictional city model using a generation AI based on the specification data and emotion data;

[1640] [Means for the server to store the generated city model and emotion data in a database;

[1641] [Means for the server to transmit the generated city model and emotion data to the terminal;

[1642] [Means for the terminal to execute a simulation based on the generated city model and emotion data;

[1643] A system including:

[1644] (Claim 2)

[1645] The system of claim 1, wherein the generated city model includes population data, climate data, geographic data, and resident data.

[1646] (Claim 3)

[1647] [The system of claim 1, wherein the generating AI generates a name for the city model, sample resident profiles, maps, three-dimensional models, and image and video data.

[1648] "Application example 2 when combining emotion engines"

[1649] (Claim 1)

[1650] [Means for a user to input specifications of the city model to be used in the simulation into the terminal;

[1651] [Means in the terminal to package the specification data and emotion data and transmit them to a server;

[1652] [Means for the server to generate a fictional city model using a generation AI based on the specification data and emotion data;

[1653] [Means for the server to store the generated city model and emotion data in a database;

[1654] [Means for the server to transmit the generated city model and emotion data to the terminal;

[1655] [Means for the terminal to execute a simulation based on the generated city model and emotion data;

[1656] A system including:

[1657] (Claim 2)

[1658] The system of claim 1, wherein the generated city model includes population data, climate data, geographic data, resident data, and user emotion data.

[1659] (Claim 3)

[1660] [The system of claim 1, wherein the generative AI generates a simulation scenario based on the name of the city model, sample resident profiles, maps, 3D models, image and video data, and the user's emotional state. [Explanation of symbols]

[1661] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for a user to input specifications of the city model to be used in the simulation into a terminal; means for transmitting the specification data to a server by the terminal; A means for the server to generate a fictional city model using a generation AI based on the specification data; a means for the server to store the generated city model in a database; means for transmitting the generated city model to the terminal by the server; means for executing a simulation based on the generated city model in the terminal; A system including:

2. The system of claim 1 , wherein the generated city model includes population data, climate data, geographic data, and resident data.

3. The system of claim 1 , wherein the generative AI generates a name for the city model, sample resident profiles, maps, 3D models, and image and video data.

Images