System

Abstract

A system is provided.SOLUTION: A system comprising: means for designing an urban green space using a generative artificial intelligence model; means for calculating an optimal plant type and arrangement based on the design of the urban green space; and means for storing the calculated plant type and arrangement.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] Japan's densely populated urban areas face a lack of space and expertise to effectively integrate and maintain green spaces. This can result in a decline in urban biodiversity and a loss of residents' well-being. The present invention aims to solve these problems and provide an effective means for designing sustainable and environmentally friendly urban green spaces. [Means for solving the problem]

[0005] The present invention provides a system including a means for designing urban green spaces using a generative artificial intelligence model, a means for calculating optimal plant species and arrangements based on the design of the urban green spaces, and a means for storing the calculated plant species and arrangements. The system may further include a means for designing urban green spaces based on the population density, available space, and climate information of the city, and a means for outputting the design and calculated arrangement information. This enables the efficient design and maintenance of sustainable green spaces in urban areas, contributing to the enhancement of biodiversity and the improvement of resident welfare.

[0006] A "generative artificial intelligence model" is an algorithmic system that can automatically learn patterns and relationships from data and generate new designs and proposals.

[0007] "Urban green space" refers to spaces within urban areas that provide a natural environment and serve as habitats for plants and animals, and includes parks, gardens, and greenways.

[0008] "Design" is the act or result of planning the structure or function of an object in order to achieve a goal.

[0009] "Type of plant" refers to the specific classification or species of plants to be placed in the green space.

[0010] "Layout" refers to the specific positional relationship and arrangement of elements within a specific space.

[0011] "Calculation" is the process of deriving results based on numerical data.

[0012] "Memory" refers to storing data or information so that it can be retrieved at a later time.

[0013] "Population density" is the number of people in a particular area divided by the area of ​​that area.

[0014] "Available space" refers to physical space set aside for a specific purpose.

[0015] "Climate information" refers to all climate-related data, such as temperature, precipitation, humidity, and wind in a particular region. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

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

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention relates to a system for designing urban green spaces using a generative artificial intelligence model. This system calculates optimal plant species and placement based on the city's population density, available space, and climate information, and then stores and outputs the resulting design. The program process and specific examples are described below.

[0038] Program processing

[0039] Server Processing

[0040] The server first initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data and green space designs. Then, it loads the city data provided by the user into the server. This data includes population density, available space, climate information, and more.

[0041] The server then calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data. Specifically, it uses a generative artificial intelligence model to calculate the optimal number of trees, shrubs, and flowers for the city's conditions, as well as their placement. The generated design is stored as a property on the server.

[0042] Finally, the server retrieves the generated green space design using the get_green_space_design method and outputs the design content.

[0043] Specific examples

[0044] For example, consider a Japanese city with a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate. When a user inputs this city data into the system, the server loads it and uses a generative AI model to generate an optimal green space design. This design could include, for example:

[0045] Trees: 75

[0046] Shrubs: 150

[0047] Flowers: 250 stems

[0048] Arrangement: 10x10 matrix

[0049] In this way, the present invention provides a green space design in urban areas that is sustainable and contributes to biodiversity, and also contributes to improving the welfare of residents.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The server creates an instance of the GreenAISystem class, which sets up properties to hold the city data and green space design.

[0053] Step 2:

[0054] Users input information such as the city's population density, available space, and climate information. This data is the basic information needed to design urban green spaces.

[0055] Step 3:

[0056] The server loads the city data provided by the user using the load_city_data method, which stores the entered data in the server.

[0057] Step 4:

[0058] The server calls the generate_green_space_design method, which generates a green space design based on the loaded city data.

[0059] Step 5:

[0060] The server uses a generative artificial intelligence model to calculate the optimal plant types and placement for urban conditions, specifically generating a matrix of tree numbers, shrub numbers, flower numbers, and placement.

[0061] Step 6:

[0062] The server stores the generated green space design in its properties, allowing you to retrieve the design information later.

[0063] Step 7:

[0064] The server uses the get_green_space_design method to retrieve the generated green space design, which includes the types of plants and their specific placement.

[0065] Step 8:

[0066] The server outputs the acquired design information to a terminal or other output means, allowing the user to check the generated design content.

[0067] Example 1

[0068] 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."

[0069] In recent years, environmental problems in urban areas have become more serious, making the design of sustainable green spaces an important issue. However, calculating the optimal plant types and placement requires a comprehensive analysis of various data, which presents a problem of difficulty in doing so efficiently and accurately. There is also a lack of systems that can memorize and quickly output calculated designs. This has led to a delay in the automation of green space design in urban planning.

[0070] 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.

[0071] In this invention, the server includes a means for initializing the system, a means for loading user-provided city data into the server, and a means for using a generative AI model to calculate the optimal plant types and placements for the city's conditions. This enables efficient and accurate automation of urban green space design. Furthermore, this invention includes a means for storing and quickly outputting the calculated green space design, thereby providing green space design information that can be immediately used in urban planning.

[0072] A "server" is a computer system or device that processes data, and is a device that processes requests from clients via a network.

[0073] "Initialization" is the process of preparing the necessary settings and resources when a system or program is started.

[0074] "User" refers to the person or entity that operates the system and inputs the required data.

[0075] "City data" refers to data that includes information about the state and characteristics of a city, including, for example, population density, available space, and climate information.

[0076] A "generative AI model" is an algorithm or mechanism that uses artificial intelligence technology to generate appropriate results or predictions based on specific input data.

[0077] "Green space design" is the process of calculating and determining the placement and type of plants in urban environments, with the aim of promoting urban greening.

[0078] "Memory" is the act or means of saving data or calculation results once obtained so that they can be reused.

[0079] "Output" is the act of providing calculation results or data in a form that can be displayed, printed, or sent to another system.

[0080] This invention relates to a system for designing urban green spaces using a generative artificial intelligence model. The system calculates optimal plant species and placement based on city data provided by the user, and stores and outputs the design.

[0081] First, the server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold the city data and green space design, preferably using the Python programming language and a database management system.

[0082] Next, the user inputs city data through the terminal. This data includes population density, available space, climate information, etc. The user inputs this data and sends it from the terminal to the server. The terminal interface provides an input form and is configured to ensure that the input data is accurately transferred to the server.

[0083] The server receives and loads city data provided by the user. Based on the loaded city data, the server uses a generative AI model to calculate the optimal green space design. This generative AI model has an algorithm for predicting the optimal plant types and placement in an urban environment. Here, the generative AI model receives city data as input and calculates the green space design.

[0084] For example, consider a Japanese city with a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate. When a user inputs this city data into the system, the server loads it and uses a generative AI model to generate an optimal green space design. This design could include, for example:

[0085] Trees: 75

[0086] Shrubs: 150

[0087] Flowers: 250 stems

[0088] Arrangement: 10x10 matrix

[0089] The green space design thus generated is stored in the server, and finally, the server can output the design content and provide it to users and other related systems.

[0090] An example of a prompt to input to a generative AI model is as follows:

[0091] The city has a population density of 5000 people / km², available space of 10000m², and a temperate climate. Generate an optimal green space design for these conditions.

[0092] This system will automate the design of sustainable green spaces in urban areas, contributing to increased biodiversity and improved welfare for residents.

[0093] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0094] Step 1: Initialize the system

[0095] The server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data and green space designs. The input is the initial settings and program startup information, and the output is the initialized system instance.

[0096] Specific behavior:

[0097] The server initializes the GreenAISystem class as follows:

[0098] python

[0099] green_ai_system = GreenAISystem()

[0100] urban_data and green_space_design are initialized as empty properties:

[0101] python

[0102] green_ai_system.urban_data = {}

[0103] green_ai_system.green_space_design = None

[0104] Step 2: Enter city data

[0105] The user inputs city data such as population density, available space, and climate information through the terminal. The input is the city data provided by the user, and the output is the data sent from the terminal to the server.

[0106] Specific behavior:

[0107] The user inputs data into an input form on the terminal.

[0108] Example: Population density: 5000 people / km², Available space: 10000m², Climate: Temperate

[0109] The user presses the send button to send the data to the server.

[0110] Step 3: Loading city data

[0111] The server receives and loads city data provided by the user. The input is the city data provided by the user, and the output is the data stored in the urban_data property in the server.

[0112] Specific behavior:

[0113] The server receives the data sent by the user:

[0114] python

[0115] provided_data = receive_data_from_user()

[0116] Store the data in the urban_data property:

[0117] python

[0118] green_ai_system.urban_data = provided_data

[0119] Step 4: Generate green space design

[0120] The server calls the generate_green_space_design method to generate an optimal green space design using a generative AI model. The input is the loaded city data, and the output is the generated green space design.

[0121] Specific behavior:

[0122] The server calls the generate_green_space_design method:

[0123] python

[0124] green_ai_system.generate_green_space_design()

[0125] Generative AI model uses city data to calculate optimal plant types and placement:

[0126] python

[0127] model_input = green_ai_system.urban_data

[0128] green_ai_system.green_space_design = ai_model.generate_design(model_input)

[0129] Step 5: Obtain and output the green space design

[0130] The server retrieves the generated green space design using the get_green_space_design method and outputs the design. The input is the calculated green space design, and the output is the design information presented to users and related systems.

[0131] Specific behavior:

[0132] Call the get_green_space_design method:

[0133] python

[0134] design = green_ai_system.get_green_space_design()

[0135] Export the design:

[0136] python

[0137] print(design)

[0138] (Application example 1)

[0139] 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."

[0140] In recent years, the design of urban green spaces has become increasingly important in order to address urban environmental issues and improve the quality of life for residents. However, conventional green space design methods are unable to properly utilize a wide range of data, making effective design difficult. In addition, selecting and arranging plants appropriate for the exterior and interior of physical stores requires specialized knowledge, which is time-consuming and laborious. This presents a challenge in that improvements to the urban environment and increased customer attraction are not being fully achieved.

[0141] 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.

[0142] In this invention, the server includes means for designing urban green spaces using a generative artificial intelligence model, means for calculating optimal plant types and arrangements based on the urban green space design, means for storing the calculated plant types and arrangements, means for recognizing spatial information about the exterior and interior of the store through smart glasses, and means for displaying the green space design on the smart glasses based on the recognized spatial information. This allows for the rapid and efficient provision of optimal green space designs based on urban conditions, thereby improving the environment of physical stores and increasing their customer attraction.

[0143] A "generative artificial intelligence model" is an artificial intelligence model that is built to generate a specific result or design based on input data.

[0144] "Urban green space" is an area of ​​vegetation and green space within a city that is designed to improve the environment and the well-being of residents.

[0145] "Calculation" refers to the process of finding the optimal result based on the data required for a specific purpose, and in this case refers to determining the types and placement of plants that are suitable for urban green spaces.

[0146] "Storage" is the process of saving calculated data or design information for later access.

[0147] "Smart glasses" are eyeglass-type devices that have built-in cameras and sensors and have the ability to overlay information on the user's field of vision.

[0148] "Spatial information" is data including the dimensions, shape, and layout of a specific area, and in this case refers to the external and internal location information of a store captured by smart glasses.

[0149] "Perception" is the process of processing information obtained through devices such as cameras and sensors to identify specific objects or situations.

[0150] "Display" refers to the visual output of information using an electronic device, and in this case refers to the overlay of a green space design on the display of smart glasses.

[0151] To implement this invention, the server must first initialize the system. During the system initialization phase, an instance of the GreenAISystem class is created and properties are set up to hold city data and green space designs. These properties include the city's population density, available space, and climate information.

[0152] Next, the server is loaded with user-provided city data, for example, by using smart glasses to capture spatial information about the exterior and interior of the store, and the cloud server retrieves the city data along with related data, including the city's climate and population density data.

[0153] The server then calls the generate_green_space_design method to generate an optimal green space design based on the city data previously loaded. This generation process uses a generative artificial intelligence model to calculate the number and placement of trees, shrubs, and flowers that are optimal for the city's conditions. The generated design is saved as a property on the server.

[0154] The server then retrieves the generated green space design using the get_green_space_design method and displays it on the smart glasses' display. The smart glasses visually show the user the placement of plants based on spatial data acquired through cameras and sensors. This display function allows the smart glasses to provide optimal green space designs for the exterior and interior of stores.

[0155] As a concrete example, let's take a scenario that applies to a "nature-inspired cafe" in a certain city. The cafe's exterior space is 12,000 square meters, the population density is 4,500 people / km², and the climate is temperate. When a user provides this information as city data, the cloud server retrieves the information and generates an optimal green space design. As a result, the following plant placements are proposed, for example:

[0156] Trees: 85

[0157] Shrubs: 170

[0158] Flowers: 280 stems

[0159] Arrangement: 12x12 matrix

[0160] Users can visually check these layouts through smart glasses and adjust them as needed. By efficiently and effectively designing green spaces based on the generated green space design, it is possible to improve the urban environment and increase the number of customers visiting stores.

[0161] Example prompt sentence:

[0162] Population density: 4500

[0163] Available space: 12000

[0164] Climate: Temperate

[0165] Store name: Natural Style Cafe

[0166] Store type: Restaurant

[0167] External design information: Image / video data from camera

[0168] Internal design information: spatial data from sensors

[0169] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0170] Step 1:

[0171] The server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data and green space design. The properties to be initialized include the city's population density, available space, and climate information. The input requires system configuration information, and the output is an initialized GreenAISystem instance.

[0172] Step 2:

[0173] The city data provided by the user (for example, the exterior and interior spatial information of a store acquired by smart glasses) is loaded into the server. The server receives this data and stores it in internal properties. The input requires city data and spatial information, and the output is the city data loaded internally.

[0174] Step 3:

[0175] The server calls the generate_green_space_design method to generate a green space design based on the loaded city data. During this process, it uses a generative artificial intelligence model to calculate the number and placement of trees, shrubs, and flowers appropriate for the city's conditions. The loaded city data is required as input, and the calculated green space design is obtained as output.

[0176] Step 4:

[0177] The server uses the get_green_space_design method to retrieve the generated green space design and convert it into a data format for display on the smart glasses. This conversion process sets the appropriate coordinates and size for display on the smart glasses display. The input requires the green space design data, and the output is the formatted data for display.

[0178] Step 5:

[0179] The smart glasses receive the formatted data from the server and overlay the green space design on the display. The user can visually confirm the proposed plant placement through the smart glasses. The input requires display data sent from the server, and the output is visual design information that the user can recognize.

[0180] Step 6:

[0181] The user can check the design through the smart glasses and provide feedback if necessary. If there is feedback, it is sent to the server, which then calls the generate_green_space_design method again to adjust the green space design. The input requires the user's feedback data, and the output is the adjusted green space design.

[0182] This series of processes enables the system to quickly and efficiently provide optimal green space designs based on urban conditions, improving the environment of physical stores and increasing their ability to attract customers.

[0183] 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.

[0184] The present invention relates to a system for designing urban green spaces using a generative artificial intelligence model. The system calculates optimal plant species and placement based on a city's population density, available space, and climate information, and then stores and outputs the design. The present invention also includes an emotion engine that recognizes a user's emotions and adjusts the green space design accordingly.

[0185] Program processing

[0186] Server Processing

[0187] The server first initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data, green space design, and user emotion data. Next, it loads the city data provided by the user into the server. This data includes population density, available space, and weather information.

[0188] The server then calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data. Specifically, it uses a generative artificial intelligence model to calculate the optimal number of trees, shrubs, and flowers for the city's conditions, as well as their placement. The generated design is stored as a property on the server.

[0189] The server also uses an emotion engine that recognizes the user's emotions. The emotion engine analyzes emotions from user input and sensor data and adjusts the green space design based on those emotions, taking into account past user emotion data. For example, if the user wants to relax, the server generates a design that includes many plants with a relaxing effect.

[0190] Finally, the server retrieves the generated green space design using the get_green_space_design method and outputs the design content.

[0191] Specific examples

[0192] For example, consider a Japanese city with a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate. When a user inputs this city data into the system, the server loads it and uses a generative AI model to generate an optimal green space design. This design could include, for example:

[0193] Trees: 75

[0194] Shrubs: 150

[0195] Flowers: 250 stems

[0196] Arrangement: 10x10 matrix

[0197] Furthermore, if the user expresses their desire to relax through sensor input or direct input, the emotion engine will recognize this and adjust the design to include more plants with relaxing effects, such as lavender and jasmine, based on the user's emotions.

[0198] This system provides sustainable green space designs in urban areas that contribute to biodiversity, and by combining a generative artificial intelligence model with an emotion engine, it also contributes to improving the welfare of residents.

[0199] The processing flow will be explained below.

[0200] Step 1:

[0201] The server creates an instance of the GreenAISystem class, which sets up properties to hold city data, green space design, and user emotion data.

[0202] Step 2:

[0203] Users input information about the city's population density, available space, and climate, which are the basic information for green space design.

[0204] Step 3:

[0205] The server loads the city data provided by the user using the load_city_data method, which saves the entered data in the server.

[0206] Step 4:

[0207] The server invokes the generate_green_space_design method, which generates an optimal green space design based on the loaded city data using a generative artificial intelligence model.

[0208] Step 5:

[0209] The server uses a generative artificial intelligence model to calculate the number of trees, shrubs, and flowers, as well as a placement matrix based on urban conditions, which then generates a specific green space design.

[0210] Step 6:

[0211] The server stores the generated green space design in a property, which allows the design information to be output later.

[0212] Step 7:

[0213] The user inputs emotion data, which indicates the user's feelings such as a desire to relax or to be energized.

[0214] Step 8:

[0215] The server analyzes the user's emotional data using an emotion engine, which recognizes emotions from user input and sensor data and adjusts the green space design accordingly.

[0216] Step 9:

[0217] The server adjusts the green space design based on the analysis results of the emotion engine. For example, if the user wants to relax, the design will be revised to include more plants with relaxing effects, such as lavender and jasmine.

[0218] Step 10:

[0219] The server retrieves the generated green space design using the get_green_space_design method, which saves the final design within the server.

[0220] Step 11:

[0221] The server outputs the acquired design information to the terminal, allowing the user to check the generated green space design.

[0222] Example 2

[0223] 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."

[0224] Until now, there has been no system that can efficiently and effectively design urban green spaces. In particular, there is a need for a system that can take into account various data such as the city's population density, available space, and climate information, and can also adjust green space design based on user emotional data. However, existing systems have had the problem of being unable to design individual green spaces based on the user's emotions and preferences.

[0225] 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.

[0226] In this invention, the server includes means for designing urban green spaces using a generative artificial intelligence model, means for calculating optimal plant types and arrangements based on the urban green space design, means for analyzing user emotion data and adjusting the calculated design, means for storing the calculated plant types and arrangements, and means for outputting the design and adjusted arrangement information. This enables optimal urban green space design that takes into account the diverse conditions of the city and the user's emotions.

[0227] A "generative artificial intelligence model" is a computer program that is trained to automatically perform specific tasks from data using machine learning algorithms.

[0228] "Urban green space" refers to a green area established within a city to protect or provide a natural environment.

[0229] "Design" is the process of planning and documenting a configuration or arrangement based on specific goals and requirements.

[0230] "Plant types" refer to different plant species (trees, shrubs, flowering plants, etc.) in biological classification.

[0231] "Arrangement" refers to how elements or objects are arranged and positioned within a particular area.

[0232] "User emotion data" is information that quantitatively or qualitatively expresses the user's emotional state.

[0233] An "emotion engine" is a software component that analyzes sensor data and user input data to identify a user's emotions.

[0234] "Storage" is the process or function of storing data so that it can be retrieved at a later time.

[0235] "Output" is the process of displaying, printing, or transmitting processed data or information for viewing by an external system or user.

[0236] "Population density" is the number of people living in a particular geographic area.

[0237] "Available space" is the physical area that can be used for a particular purpose.

[0238] "Climate information" is data on weather conditions such as temperature, precipitation, and humidity.

[0239] "Property" is a concept that refers to the characteristics or attributes of a particular object or data element.

[0240] "Adjustment" is the act of changing or modifying something to suit particular conditions or requirements.

[0241] An "instance" is a specific instance of a class or object, a concrete object created within a program based on the definition of that class.

[0242] The present invention relates to a system for designing urban green spaces using a generative artificial intelligence model. The system calculates optimal plant species and placement based on a city's population density, available space, and climate information, and stores and outputs the design. It also includes an emotion engine that recognizes user emotions and adjusts the design based on the emotion data.

[0243] Server Processing

[0244] The server first initializes the system. At this stage, it creates an instance of the GreenAISystem class and sets up properties to hold city data, green space design, and user emotion data. It also loads the necessary libraries and generative AI models.

[0245] The server then loads the city data provided by the user, including population density, available space, climate information, etc. The server validates this data and stores it in the correct format.

[0246] The server then calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data. It uses a generative artificial intelligence model to calculate the optimal number of trees, shrubs, and flowers, and their placement, for the city's conditions. The results of this calculation are stored in a property on the server.

[0247] Additionally, the server uses an emotion engine that recognizes the user's emotions. The emotion engine analyzes emotions from user input and sensor data and adjusts the green space design based on those emotions. Past user emotion data is also taken into account. For example, if the user wants to relax, the server generates a design that includes many plants with a relaxing effect.

[0248] Finally, the server retrieves the generated green space design using the get_green_space_design method and outputs the design to the user, who can then view the final green space design on their terminal. The design is displayed in text and graphical formats.

[0249] Hardware and software used

[0250] Hardware: Server (e.g., general-purpose computer system), user device (e.g., PC, smartphone), emotion recognition sensor (e.g., video camera, microphone)

[0251] Software: Generative AI models (e.g., GPT-3), emotion engines (e.g., Affectiva SDK)

[0252] Specific examples

[0253] For example, consider a Japanese city with a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate. When a user inputs this city data into the system, the server loads it and uses a generative artificial intelligence model to generate an optimal green space design. This design includes:

[0254] Trees: 75

[0255] Shrubs: 150

[0256] Flowers: 250 stems

[0257] Arrangement: 10x10 matrix

[0258] When the user expresses their desire to relax through sensor input or direct input, the emotion engine recognizes this and adjusts the design to include more plants with relaxing effects, such as lavender and jasmine.

[0259] Prompt Sentence Examples

[0260] For example:

[0261] "I would like to design a green space for a city. The population density of the city is 5,000 people / km², the available space is 10,000 m², and the climate is temperate. Users want to relax. Based on these conditions, please propose the optimal green space design."

[0262] In this way, the present invention provides a sustainable green space design in urban areas that contributes to biodiversity and also contributes to improving the welfare of residents.

[0263] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0264] Step 1:

[0265] Initializing the Server

[0266] The server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data, green space design, and user emotion data. It also loads the necessary libraries and generative AI models.

[0267] input:

[0268] Initial setting data

[0269] output:

[0270] A set up GreenAISystem instance

[0271] Specific behavior:

[0272] GreenAISystem = new GreenAISystem(); This code creates an instance.

[0273] Step 2:

[0274] Entering and Loading City Data

[0275] Users input city data (e.g., population density, available space, climate information) through a terminal. The server receives this data, loads it into the system, and verifies that it is in the correct format.

[0276] input:

[0277] Population density, available space, and climate information

[0278] output:

[0279] Loaded city data

[0280] Specific behavior:

[0281] GreenAISystem.load_city_data(5000, 10000, 'Temperate'); This method loads the data.

[0282] Step 3:

[0283] Generate green space designs

[0284] The server calls the generate_green_space_design method to generate an optimal green space design based on city data. It uses a generative artificial intelligence model to calculate the number of trees, shrubs, and flowers, and their placement.

[0285] input:

[0286] Loaded city data

[0287] output:

[0288] Generated green space design data (number of trees, number of shrubs, number of flowers, and their placement)

[0289] Specific behavior:

[0290] GreenAISystem.generate_green_space_design(); This method is called and the AI ​​model performs the calculations.

[0291] Step 4:

[0292] Emotion data input and analysis

[0293] The user inputs their emotion (e.g., "I want to relax") through a device or sensor. The server receives this emotion data and analyzes it using an emotion engine.

[0294] input:

[0295] User emotion data

[0296] output:

[0297] Analyzed emotion data

[0298] Specific behavior:

[0299] GreenAISystem.analyze_emotion('I want to relax'); This method analyzes the sensor data.

[0300] Step 5:

[0301] Coordination of green space design

[0302] The server adjusts the green space design based on the analysis results of the emotion engine, for example by changing the design to include more plants that have a relaxing effect.

[0303] input:

[0304] Analyzed emotion data, generated green space design data

[0305] output:

[0306] Adjusted green space design data

[0307] Specific behavior:

[0308] GreenAISystem.adjust_design_based_on_emotion('I want to relax'); This method adjusts the design based on emotion.

[0309] Step 6:

[0310] Final green space design output

[0311] The server retrieves the final green space design using the get_green_space_design method and sends it to the user's device, where the user can view the design.

[0312] input:

[0313] Adjusted green space design data

[0314] output:

[0315] Final green space design data

[0316] Specific behavior:

[0317] final_design = GreenAISystem.get_green_space_design(); This method retrieves the final design and outputs it to the user.

[0318] These are the specific processing steps of this system, which enables optimal urban green space design that takes into account the diverse conditions of the city and the emotions of users.

[0319] (Application example 2)

[0320] 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."

[0321] There is a need to effectively utilize available space in urban areas as green spaces, contributing to the welfare of residents and improving the urban environment. However, while conventional green space design systems can optimally select and arrange plants based on urban data, they are unable to adjust the plantings based on the user's emotions or specific preferences. To address these issues, the present invention provides a system that realizes green space designs that reflect the user's emotions and visually displays the designs using augmented reality or a three-dimensional model.

[0322] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for designing urban green space using a generative artificial intelligence model, means for calculating optimal plant types and arrangements based on the urban green space design, means for storing the calculated plant types and arrangements, means for adjusting the plant arrangements based on the user's emotional state, means for inputting and analyzing the user's emotions, and means for displaying the design results in augmented reality or a three-dimensional model. This makes it possible to provide customized green space designs using city data and the user's emotional state.

[0323] A "generative artificial intelligence model" is an algorithm or computational model that analyzes the complex data required for designing urban green spaces and generates optimal plant types and placements.

[0324] "Urban green space design" is the planning of plant types and placements to effectively use available space in urban areas as green spaces.

[0325] "Calculation of plant types and placement" refers to calculations based on city data and climate information to ensure that plants selected for urban green space design are optimally placed.

[0326] "Storing the calculated plant types and arrangements" means saving the generated green space design information as digital data.

[0327] "Adjustment based on the user's emotional state" means optimizing the initial green space design based on emotions analyzed from user input and sensor data, and providing a green space design that takes the user's emotional state into account.

[0328] "Emotion input and analysis" refers to acquiring a user's emotional data and analyzing it to identify a specific emotional state.

[0329] "Augmented reality or three-dimensional model display" is a technology that displays the generated green space design in a virtual space using a smartphone or head-mounted display, allowing the user to visually confirm the design.

[0330] "City data" refers to data such as a city's population density, available space, and climate information.

[0331] A "prompt sentence format" is a sentence that is presented to the user in a format that is easy for the user to understand as input to a generative AI model.

[0332] To implement this invention, a system for designing urban green spaces is required. This system includes a server, a user's device (such as a smartphone, smart glasses, or a head-mounted display), a generative artificial intelligence model, and an emotion engine.

[0333] The server first initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data, green space design, and user emotion data. Next, it loads the city data provided by the user into the server. This data includes population density, available space, and weather information.

[0334] The server then calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data. Specifically, it uses a generative artificial intelligence model to calculate the optimal plant types and placement for the city's conditions. The generated design is stored in a property on the server.

[0335] The server also uses an emotion engine that recognizes the user's emotions. The emotion engine analyzes emotions from user input and sensor data and adjusts the green space design based on those emotions, taking into account past user emotion data. For example, if the user wants to relax, the server generates a design that includes many plants with a relaxing effect.

[0336] The final design results can be visually confirmed on the user's device, which has the ability to display the design results in augmented reality (AR) and three-dimensional models (3D models), allowing users to overlay the green space design on the real space.

[0337] For example, if a city's data shows a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate, the generative AI model will provide an optimal green space design based on this data. If a user inputs their emotional state of "I want to relax," the emotion engine will recognize this and generate a green space design that includes many plants with a relaxing effect.

[0338] As a concrete example, the generative AI model is designed by inputting the following prompt sentence:

[0339] "Design an optimal green space for a city with a population density of 5000 people / km², available space of 10000m², and a temperate climate. The user wants to relax."

[0340] This will provide a concrete and practical system that effectively uses available space in urban areas as green space, contributing to the improvement of residents' welfare and the urban environment.

[0341] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0342] Step 1:

[0343] The server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data, green space design, and user emotion data. This completes the necessary initial setup.

[0344] Input: None

[0345] Output: An instance of the GreenAISystem class, city data properties, green space design properties, and emotion data properties.

[0346] Step 2:

[0347] The server loads the city data provided by the user into the server, which includes population density, available space, and climate information, and stores this data in properties of the city data.

[0348] Inputs: population density, available space, climate information

[0349] Output: Properties where city data is stored

[0350] Step 3:

[0351] The server calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data, using a generative artificial intelligence model to calculate plant types and placement.

[0352] Input: City data

[0353] Output: Optimal green space design (plant types and placement)

[0354] Step 4:

[0355] The server saves the optimal green space design in the property, and this design result is used in subsequent processing.

[0356] Input: Optimal green space design

[0357] Output: Saved green space design

[0358] Step 5:

[0359] The server uses an emotion engine to receive the user's emotional state as input, and the emotion engine analyzes the user's emotional data and stores the results in the properties of the emotional data.

[0360] Input: User's emotional state (e.g., "I want to relax")

[0361] Output: Parsed emotion data

[0362] Step 6:

[0363] The server adjusts the green space design based on the emotional data analyzed by the emotion engine. For example, if the user wants to relax, the server changes the design to include more plants that have a relaxing effect.

[0364] Input: Analyzed emotion data, optimal green space design

[0365] Output: Coordinated green space design

[0366] Step 7:

[0367] The device displays the adjusted green space design retrieved from the server in augmented reality (AR) or three-dimensional (3D) models, allowing users to check the design overlaid on the real space.

[0368] Input: Coordinated green space design

[0369] Output: Augmented reality or 3D model display

[0370] Step 8:

[0371] The user visually checks the green space design displayed on the device and, if necessary, sends further adjustment requests and feedback to the server, which then completes the final green space design that reflects the user's specific wishes.

[0372] Input: User feedback, adjustment requests

[0373] Output: Final design reflecting user wishes

[0374] 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.

[0375] 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.

[0376] 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.

[0377] [Second embodiment]

[0378] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0379] 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.

[0380] 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).

[0381] 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.

[0382] 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.

[0383] 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).

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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."

[0390] This invention relates to a system for designing urban green spaces using a generative artificial intelligence model. This system calculates optimal plant species and placement based on the city's population density, available space, and climate information, and then stores and outputs the resulting design. The program process and specific examples are described below.

[0391] Program processing

[0392] Server Processing

[0393] The server first initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data and green space designs. Then, it loads the city data provided by the user into the server. This data includes population density, available space, climate information, and more.

[0394] The server then calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data. Specifically, it uses a generative artificial intelligence model to calculate the optimal number of trees, shrubs, and flowers for the city's conditions, as well as their placement. The generated design is stored as a property on the server.

[0395] Finally, the server retrieves the generated green space design using the get_green_space_design method and outputs the design content.

[0396] Specific examples

[0397] For example, consider a Japanese city with a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate. When a user inputs this city data into the system, the server loads it and uses a generative AI model to generate an optimal green space design. This design could include, for example:

[0398] Trees: 75

[0399] Shrubs: 150

[0400] Flowers: 250 stems

[0401] Arrangement: 10x10 matrix

[0402] In this way, the present invention provides a green space design in urban areas that is sustainable and contributes to biodiversity, and also contributes to improving the welfare of residents.

[0403] The processing flow will be explained below.

[0404] Step 1:

[0405] The server creates an instance of the GreenAISystem class, which sets up properties to hold the city data and green space design.

[0406] Step 2:

[0407] Users input information such as the city's population density, available space, and climate information. This data is the basic information needed to design urban green spaces.

[0408] Step 3:

[0409] The server loads the city data provided by the user using the load_city_data method, which stores the entered data in the server.

[0410] Step 4:

[0411] The server calls the generate_green_space_design method, which generates a green space design based on the loaded city data.

[0412] Step 5:

[0413] The server uses a generative artificial intelligence model to calculate the optimal plant types and placement for urban conditions, specifically generating a matrix of tree numbers, shrub numbers, flower numbers, and placement.

[0414] Step 6:

[0415] The server stores the generated green space design in its properties, allowing you to retrieve the design information later.

[0416] Step 7:

[0417] The server uses the get_green_space_design method to retrieve the generated green space design, which includes the types of plants and their specific placement.

[0418] Step 8:

[0419] The server outputs the acquired design information to a terminal or other output means, allowing the user to check the generated design content.

[0420] Example 1

[0421] 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."

[0422] In recent years, environmental problems in urban areas have become more serious, making the design of sustainable green spaces an important issue. However, calculating the optimal plant types and placement requires a comprehensive analysis of various data, which presents a problem of difficulty in doing so efficiently and accurately. There is also a lack of systems that can memorize and quickly output calculated designs. This has led to a delay in the automation of green space design in urban planning.

[0423] 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.

[0424] In this invention, the server includes a means for initializing the system, a means for loading user-provided city data into the server, and a means for using a generative AI model to calculate the optimal plant types and placements for the city's conditions. This enables efficient and accurate automation of urban green space design. Furthermore, this invention includes a means for storing and quickly outputting the calculated green space design, thereby providing green space design information that can be immediately used in urban planning.

[0425] A "server" is a computer system or device that processes data, and is a device that processes requests from clients via a network.

[0426] "Initialization" is the process of preparing the necessary settings and resources when a system or program is started.

[0427] "User" refers to the person or entity that operates the system and inputs the required data.

[0428] "City data" refers to data that includes information about the state and characteristics of a city, including, for example, population density, available space, and climate information.

[0429] A "generative AI model" is an algorithm or mechanism that uses artificial intelligence technology to generate appropriate results or predictions based on specific input data.

[0430] "Green space design" is the process of calculating and determining the placement and type of plants in urban environments, with the aim of promoting urban greening.

[0431] "Memory" is the act or means of saving data or calculation results once obtained so that they can be reused.

[0432] "Output" is the act of providing calculation results or data in a form that can be displayed, printed, or sent to another system.

[0433] This invention relates to a system for designing urban green spaces using a generative artificial intelligence model. The system calculates optimal plant species and placement based on city data provided by the user, and stores and outputs the design.

[0434] First, the server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold the city data and green space design, preferably using the Python programming language and a database management system.

[0435] Next, the user inputs city data through the terminal. This data includes population density, available space, climate information, etc. The user inputs this data and sends it from the terminal to the server. The terminal interface provides an input form and is configured to ensure that the input data is accurately transferred to the server.

[0436] The server receives and loads city data provided by the user. Based on the loaded city data, the server uses a generative AI model to calculate the optimal green space design. This generative AI model has an algorithm for predicting the optimal plant types and placement in an urban environment. Here, the generative AI model receives city data as input and calculates the green space design.

[0437] For example, consider a Japanese city with a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate. When a user inputs this city data into the system, the server loads it and uses a generative AI model to generate an optimal green space design. This design could include, for example:

[0438] Trees: 75

[0439] Shrubs: 150

[0440] Flowers: 250 stems

[0441] Arrangement: 10x10 matrix

[0442] The green space design thus generated is stored in the server, and finally, the server can output the design content and provide it to users and other related systems.

[0443] An example of a prompt to input to a generative AI model is as follows:

[0444] The city has a population density of 5000 people / km², available space of 10000m², and a temperate climate. Generate an optimal green space design for these conditions.

[0445] This system will automate the design of sustainable green spaces in urban areas, contributing to increased biodiversity and improved welfare for residents.

[0446] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0447] Step 1: Initialize the system

[0448] The server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data and green space designs. The input is the initial settings and program startup information, and the output is the initialized system instance.

[0449] Specific behavior:

[0450] The server initializes the GreenAISystem class as follows:

[0451] python

[0452] green_ai_system = GreenAISystem()

[0453] urban_data and green_space_design are initialized as empty properties:

[0454] python

[0455] green_ai_system.urban_data = {}

[0456] green_ai_system.green_space_design = None

[0457] Step 2: Enter city data

[0458] The user inputs city data such as population density, available space, and climate information through the terminal. The input is the city data provided by the user, and the output is the data sent from the terminal to the server.

[0459] Specific behavior:

[0460] The user inputs data into an input form on the terminal.

[0461] Example: Population density: 5000 people / km², Available space: 10000m², Climate: Temperate

[0462] The user presses the send button to send the data to the server.

[0463] Step 3: Loading city data

[0464] The server receives and loads city data provided by the user. The input is the city data provided by the user, and the output is the data stored in the urban_data property in the server.

[0465] Specific behavior:

[0466] The server receives the data sent by the user:

[0467] python

[0468] provided_data = receive_data_from_user()

[0469] Store the data in the urban_data property:

[0470] python

[0471] green_ai_system.urban_data = provided_data

[0472] Step 4: Generate green space design

[0473] The server calls the generate_green_space_design method to generate an optimal green space design using a generative AI model. The input is the loaded city data, and the output is the generated green space design.

[0474] Specific behavior:

[0475] The server calls the generate_green_space_design method:

[0476] python

[0477] green_ai_system.generate_green_space_design()

[0478] Generative AI model uses city data to calculate optimal plant types and placement:

[0479] python

[0480] model_input = green_ai_system.urban_data

[0481] green_ai_system.green_space_design = ai_model.generate_design(model_input)

[0482] Step 5: Obtain and output the green space design

[0483] The server retrieves the generated green space design using the get_green_space_design method and outputs the design. The input is the calculated green space design, and the output is the design information presented to users and related systems.

[0484] Specific behavior:

[0485] Call the get_green_space_design method:

[0486] python

[0487] design = green_ai_system.get_green_space_design()

[0488] Export the design:

[0489] python

[0490] print(design)

[0491] (Application example 1)

[0492] 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."

[0493] In recent years, the design of urban green spaces has become increasingly important in order to address urban environmental issues and improve the quality of life for residents. However, conventional green space design methods are unable to properly utilize a wide range of data, making effective design difficult. In addition, selecting and arranging plants appropriate for the exterior and interior of physical stores requires specialized knowledge, which is time-consuming and laborious. This presents a challenge in that improvements to the urban environment and increased customer attraction are not being fully achieved.

[0494] 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.

[0495] In this invention, the server includes means for designing urban green spaces using a generative artificial intelligence model, means for calculating optimal plant types and arrangements based on the urban green space design, means for storing the calculated plant types and arrangements, means for recognizing spatial information about the exterior and interior of the store through smart glasses, and means for displaying the green space design on the smart glasses based on the recognized spatial information. This allows for the rapid and efficient provision of optimal green space designs based on urban conditions, thereby improving the environment of physical stores and increasing their customer attraction.

[0496] A "generative artificial intelligence model" is an artificial intelligence model that is built to generate a specific result or design based on input data.

[0497] "Urban green space" is an area of ​​vegetation and green space within a city that is designed to improve the environment and the well-being of residents.

[0498] "Calculation" refers to the process of finding the optimal result based on the data required for a specific purpose, and in this case refers to determining the types and placement of plants that are suitable for urban green spaces.

[0499] "Storage" is the process of saving calculated data or design information for later access.

[0500] "Smart glasses" are eyeglass-type devices that have built-in cameras and sensors and have the ability to overlay information on the user's field of vision.

[0501] "Spatial information" is data including the dimensions, shape, and layout of a specific area, and in this case refers to the external and internal location information of a store captured by smart glasses.

[0502] "Perception" is the process of processing information obtained through devices such as cameras and sensors to identify specific objects or situations.

[0503] "Display" refers to the visual output of information using an electronic device, and in this case refers to the overlay of a green space design on the display of smart glasses.

[0504] To implement this invention, the server must first initialize the system. During the system initialization phase, an instance of the GreenAISystem class is created and properties are set up to hold city data and green space designs. These properties include the city's population density, available space, and climate information.

[0505] Next, the server is loaded with user-provided city data, for example, by using smart glasses to capture spatial information about the exterior and interior of the store, and the cloud server retrieves the city data along with related data, including the city's climate and population density data.

[0506] The server then calls the generate_green_space_design method to generate an optimal green space design based on the city data previously loaded. This generation process uses a generative artificial intelligence model to calculate the number and placement of trees, shrubs, and flowers that are optimal for the city's conditions. The generated design is saved as a property on the server.

[0507] The server then retrieves the generated green space design using the get_green_space_design method and displays it on the smart glasses' display. The smart glasses visually show the user the placement of plants based on spatial data acquired through cameras and sensors. This display function allows the smart glasses to provide optimal green space designs for the exterior and interior of stores.

[0508] As a concrete example, let's take a scenario that applies to a "nature-inspired cafe" in a certain city. The cafe's exterior space is 12,000 square meters, the population density is 4,500 people / km², and the climate is temperate. When a user provides this information as city data, the cloud server retrieves the information and generates an optimal green space design. As a result, the following plant placements are proposed, for example:

[0509] Trees: 85

[0510] Shrubs: 170

[0511] Flowers: 280 stems

[0512] Arrangement: 12x12 matrix

[0513] Users can visually check these layouts through smart glasses and adjust them as needed. By efficiently and effectively designing green spaces based on the generated green space design, it is possible to improve the urban environment and increase the number of customers visiting stores.

[0514] Example prompt sentence:

[0515] Population density: 4500

[0516] Available space: 12000

[0517] Climate: Temperate

[0518] Store name: Natural Style Cafe

[0519] Store type: Restaurant

[0520] External design information: Image / video data from camera

[0521] Internal design information: spatial data from sensors

[0522] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0523] Step 1:

[0524] The server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data and green space design. The properties to be initialized include the city's population density, available space, and climate information. The input requires system configuration information, and the output is an initialized GreenAISystem instance.

[0525] Step 2:

[0526] The city data provided by the user (for example, the exterior and interior spatial information of a store acquired by smart glasses) is loaded into the server. The server receives this data and stores it in internal properties. The input requires city data and spatial information, and the output is the city data loaded internally.

[0527] Step 3:

[0528] The server calls the generate_green_space_design method to generate a green space design based on the loaded city data. During this process, it uses a generative artificial intelligence model to calculate the number and placement of trees, shrubs, and flowers appropriate for the city's conditions. The loaded city data is required as input, and the calculated green space design is obtained as output.

[0529] Step 4:

[0530] The server uses the get_green_space_design method to retrieve the generated green space design and convert it into a data format for display on the smart glasses. This conversion process sets the appropriate coordinates and size for display on the smart glasses display. The input requires the green space design data, and the output is the formatted data for display.

[0531] Step 5:

[0532] The smart glasses receive the formatted data from the server and overlay the green space design on the display. The user can visually confirm the proposed plant placement through the smart glasses. The input requires display data sent from the server, and the output is visual design information that the user can recognize.

[0533] Step 6:

[0534] The user can check the design through the smart glasses and provide feedback if necessary. If there is feedback, it is sent to the server, which then calls the generate_green_space_design method again to adjust the green space design. The input requires the user's feedback data, and the output is the adjusted green space design.

[0535] This series of processes enables the system to quickly and efficiently provide optimal green space designs based on urban conditions, improving the environment of physical stores and increasing their ability to attract customers.

[0536] 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.

[0537] The present invention relates to a system for designing urban green spaces using a generative artificial intelligence model. The system calculates optimal plant species and placement based on a city's population density, available space, and climate information, and then stores and outputs the design. The present invention also includes an emotion engine that recognizes a user's emotions and adjusts the green space design accordingly.

[0538] Program processing

[0539] Server Processing

[0540] The server first initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data, green space design, and user emotion data. Next, it loads the city data provided by the user into the server. This data includes population density, available space, and weather information.

[0541] The server then calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data. Specifically, it uses a generative artificial intelligence model to calculate the optimal number of trees, shrubs, and flowers for the city's conditions, as well as their placement. The generated design is stored as a property on the server.

[0542] The server also uses an emotion engine that recognizes the user's emotions. The emotion engine analyzes emotions from user input and sensor data and adjusts the green space design based on those emotions, taking into account past user emotion data. For example, if the user wants to relax, the server generates a design that includes many plants with a relaxing effect.

[0543] Finally, the server retrieves the generated green space design using the get_green_space_design method and outputs the design content.

[0544] Specific examples

[0545] For example, consider a Japanese city with a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate. When a user inputs this city data into the system, the server loads it and uses a generative AI model to generate an optimal green space design. This design could include, for example:

[0546] Trees: 75

[0547] Shrubs: 150

[0548] Flowers: 250 stems

[0549] Arrangement: 10x10 matrix

[0550] Furthermore, if the user expresses their desire to relax through sensor input or direct input, the emotion engine will recognize this and adjust the design to include more plants with relaxing effects, such as lavender and jasmine, based on the user's emotions.

[0551] This system provides sustainable green space designs in urban areas that contribute to biodiversity, and by combining a generative artificial intelligence model with an emotion engine, it also contributes to improving the welfare of residents.

[0552] The processing flow will be explained below.

[0553] Step 1:

[0554] The server creates an instance of the GreenAISystem class, which sets up properties to hold city data, green space design, and user emotion data.

[0555] Step 2:

[0556] Users input information about the city's population density, available space, and climate, which are the basic information for green space design.

[0557] Step 3:

[0558] The server loads the city data provided by the user using the load_city_data method, which saves the entered data in the server.

[0559] Step 4:

[0560] The server invokes the generate_green_space_design method, which generates an optimal green space design based on the loaded city data using a generative artificial intelligence model.

[0561] Step 5:

[0562] The server uses a generative artificial intelligence model to calculate the number of trees, shrubs, and flowers, as well as a placement matrix based on urban conditions, which then generates a specific green space design.

[0563] Step 6:

[0564] The server stores the generated green space design in a property, which allows the design information to be output later.

[0565] Step 7:

[0566] The user inputs emotion data, which indicates the user's feelings such as a desire to relax or to be energized.

[0567] Step 8:

[0568] The server analyzes the user's emotional data using an emotion engine, which recognizes emotions from user input and sensor data and adjusts the green space design accordingly.

[0569] Step 9:

[0570] The server adjusts the green space design based on the analysis results of the emotion engine. For example, if the user wants to relax, the design will be revised to include more plants with relaxing effects, such as lavender and jasmine.

[0571] Step 10:

[0572] The server retrieves the generated green space design using the get_green_space_design method, which saves the final design within the server.

[0573] Step 11:

[0574] The server outputs the acquired design information to the terminal, allowing the user to check the generated green space design.

[0575] Example 2

[0576] 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."

[0577] Until now, there has been no system that can efficiently and effectively design urban green spaces. In particular, there is a need for a system that can take into account various data such as the city's population density, available space, and climate information, and can also adjust green space design based on user emotional data. However, existing systems have had the problem of being unable to design individual green spaces based on the user's emotions and preferences.

[0578] 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.

[0579] In this invention, the server includes means for designing urban green spaces using a generative artificial intelligence model, means for calculating optimal plant types and arrangements based on the urban green space design, means for analyzing user emotion data and adjusting the calculated design, means for storing the calculated plant types and arrangements, and means for outputting the design and adjusted arrangement information. This enables optimal urban green space design that takes into account the diverse conditions of the city and the user's emotions.

[0580] A "generative artificial intelligence model" is a computer program that is trained to automatically perform specific tasks from data using machine learning algorithms.

[0581] "Urban green space" refers to a green area established within a city to protect or provide a natural environment.

[0582] "Design" is the process of planning and documenting a configuration or arrangement based on specific goals and requirements.

[0583] "Plant types" refer to different plant species (trees, shrubs, flowering plants, etc.) in biological classification.

[0584] "Arrangement" refers to how elements or objects are arranged and positioned within a particular area.

[0585] "User emotion data" is information that quantitatively or qualitatively expresses the user's emotional state.

[0586] An "emotion engine" is a software component that analyzes sensor data and user input data to identify a user's emotions.

[0587] "Storage" is the process or function of storing data so that it can be retrieved at a later time.

[0588] "Output" is the process of displaying, printing, or transmitting processed data or information for viewing by an external system or user.

[0589] "Population density" is the number of people living in a particular geographic area.

[0590] "Available space" is the physical area that can be used for a particular purpose.

[0591] "Climate information" is data on weather conditions such as temperature, precipitation, and humidity.

[0592] "Property" is a concept that refers to the characteristics or attributes of a particular object or data element.

[0593] "Adjustment" is the act of changing or modifying something to suit particular conditions or requirements.

[0594] An "instance" is a specific instance of a class or object, a concrete object created within a program based on the definition of that class.

[0595] The present invention relates to a system for designing urban green spaces using a generative artificial intelligence model. The system calculates optimal plant species and placement based on a city's population density, available space, and climate information, and stores and outputs the design. It also includes an emotion engine that recognizes user emotions and adjusts the design based on the emotion data.

[0596] Server Processing

[0597] The server first initializes the system. At this stage, it creates an instance of the GreenAISystem class and sets up properties to hold city data, green space design, and user emotion data. It also loads the necessary libraries and generative AI models.

[0598] The server then loads the city data provided by the user, including population density, available space, climate information, etc. The server validates this data and stores it in the correct format.

[0599] The server then calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data. It uses a generative artificial intelligence model to calculate the optimal number of trees, shrubs, and flowers, and their placement, for the city's conditions. The results of this calculation are stored in a property on the server.

[0600] Additionally, the server uses an emotion engine that recognizes the user's emotions. The emotion engine analyzes emotions from user input and sensor data and adjusts the green space design based on those emotions. Past user emotion data is also taken into account. For example, if the user wants to relax, the server generates a design that includes many plants with a relaxing effect.

[0601] Finally, the server retrieves the generated green space design using the get_green_space_design method and outputs the design to the user, who can then view the final green space design on their terminal. The design is displayed in text and graphical formats.

[0602] Hardware and software used

[0603] Hardware: Server (e.g., general-purpose computer system), user device (e.g., PC, smartphone), emotion recognition sensor (e.g., video camera, microphone)

[0604] Software: Generative AI models (e.g., GPT-3), emotion engines (e.g., Affectiva SDK)

[0605] Specific examples

[0606] For example, consider a Japanese city with a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate. When a user inputs this city data into the system, the server loads it and uses a generative artificial intelligence model to generate an optimal green space design. This design includes:

[0607] Trees: 75

[0608] Shrubs: 150

[0609] Flowers: 250 stems

[0610] Arrangement: 10x10 matrix

[0611] When the user expresses their desire to relax through sensor input or direct input, the emotion engine recognizes this and adjusts the design to include more plants with relaxing effects, such as lavender and jasmine.

[0612] Prompt Sentence Examples

[0613] For example:

[0614] "I would like to design a green space for a city. The population density of the city is 5,000 people / km², the available space is 10,000 m², and the climate is temperate. Users want to relax. Based on these conditions, please propose the optimal green space design."

[0615] In this way, the present invention provides a sustainable green space design in urban areas that contributes to biodiversity and also contributes to improving the welfare of residents.

[0616] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0617] Step 1:

[0618] Initializing the Server

[0619] The server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data, green space design, and user emotion data. It also loads the necessary libraries and generative AI models.

[0620] input:

[0621] Initial setting data

[0622] output:

[0623] A set up GreenAISystem instance

[0624] Specific behavior:

[0625] GreenAISystem = new GreenAISystem(); This code creates an instance.

[0626] Step 2:

[0627] Entering and Loading City Data

[0628] Users input city data (e.g., population density, available space, climate information) through a terminal. The server receives this data, loads it into the system, and verifies that it is in the correct format.

[0629] input:

[0630] Population density, available space, and climate information

[0631] output:

[0632] Loaded city data

[0633] Specific behavior:

[0634] GreenAISystem.load_city_data(5000, 10000, 'Temperate'); This method loads the data.

[0635] Step 3:

[0636] Generate green space designs

[0637] The server calls the generate_green_space_design method to generate an optimal green space design based on city data. It uses a generative artificial intelligence model to calculate the number of trees, shrubs, and flowers, and their placement.

[0638] input:

[0639] Loaded city data

[0640] output:

[0641] Generated green space design data (number of trees, number of shrubs, number of flowers, and their placement)

[0642] Specific behavior:

[0643] GreenAISystem.generate_green_space_design(); This method is called and the AI ​​model performs the calculations.

[0644] Step 4:

[0645] Emotion data input and analysis

[0646] The user inputs their emotion (e.g., "I want to relax") through a device or sensor. The server receives this emotion data and analyzes it using an emotion engine.

[0647] input:

[0648] User emotion data

[0649] output:

[0650] Analyzed emotion data

[0651] Specific behavior:

[0652] GreenAISystem.analyze_emotion('I want to relax'); This method analyzes the sensor data.

[0653] Step 5:

[0654] Coordination of green space design

[0655] The server adjusts the green space design based on the analysis results of the emotion engine, for example by changing the design to include more plants that have a relaxing effect.

[0656] input:

[0657] Analyzed emotion data, generated green space design data

[0658] output:

[0659] Adjusted green space design data

[0660] Specific behavior:

[0661] GreenAISystem.adjust_design_based_on_emotion('I want to relax'); This method adjusts the design based on emotion.

[0662] Step 6:

[0663] Final green space design output

[0664] The server retrieves the final green space design using the get_green_space_design method and sends it to the user's device, where the user can view the design.

[0665] input:

[0666] Adjusted green space design data

[0667] output:

[0668] Final green space design data

[0669] Specific behavior:

[0670] final_design = GreenAISystem.get_green_space_design(); This method retrieves the final design and outputs it to the user.

[0671] These are the specific processing steps of this system, which enables optimal urban green space design that takes into account the diverse conditions of the city and the emotions of users.

[0672] (Application example 2)

[0673] 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."

[0674] There is a need to effectively utilize available space in urban areas as green spaces, contributing to the welfare of residents and improving the urban environment. However, while conventional green space design systems can optimally select and arrange plants based on urban data, they are unable to adjust the plantings based on the user's emotions or specific preferences. To address these issues, the present invention provides a system that realizes green space designs that reflect the user's emotions and visually displays the designs using augmented reality or a three-dimensional model.

[0675] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for designing urban green space using a generative artificial intelligence model, means for calculating optimal plant types and arrangements based on the urban green space design, means for storing the calculated plant types and arrangements, means for adjusting the plant arrangements based on the user's emotional state, means for inputting and analyzing the user's emotions, and means for displaying the design results in augmented reality or a three-dimensional model. This makes it possible to provide customized green space designs using city data and the user's emotional state.

[0676] A "generative artificial intelligence model" is an algorithm or computational model that analyzes the complex data required for designing urban green spaces and generates optimal plant types and placements.

[0677] "Urban green space design" is the planning of plant types and placements to effectively use available space in urban areas as green spaces.

[0678] "Calculation of plant types and placement" refers to calculations based on city data and climate information to ensure that plants selected for urban green space design are optimally placed.

[0679] "Storing the calculated plant types and arrangements" means saving the generated green space design information as digital data.

[0680] "Adjustment based on the user's emotional state" means optimizing the initial green space design based on emotions analyzed from user input and sensor data, and providing a green space design that takes the user's emotional state into account.

[0681] "Emotion input and analysis" refers to acquiring a user's emotional data and analyzing it to identify a specific emotional state.

[0682] "Augmented reality or three-dimensional model display" is a technology that displays the generated green space design in a virtual space using a smartphone or head-mounted display, allowing the user to visually confirm the design.

[0683] "City data" refers to data such as a city's population density, available space, and climate information.

[0684] A "prompt sentence format" is a sentence that is presented to the user in a format that is easy for the user to understand as input to a generative AI model.

[0685] To implement this invention, a system for designing urban green spaces is required. This system includes a server, a user's device (such as a smartphone, smart glasses, or a head-mounted display), a generative artificial intelligence model, and an emotion engine.

[0686] The server first initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data, green space design, and user emotion data. Next, it loads the city data provided by the user into the server. This data includes population density, available space, and weather information.

[0687] The server then calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data. Specifically, it uses a generative artificial intelligence model to calculate the optimal plant types and placement for the city's conditions. The generated design is stored in a property on the server.

[0688] The server also uses an emotion engine that recognizes the user's emotions. The emotion engine analyzes emotions from user input and sensor data and adjusts the green space design based on those emotions, taking into account past user emotion data. For example, if the user wants to relax, the server generates a design that includes many plants with a relaxing effect.

[0689] The final design results can be visually confirmed on the user's device, which has the ability to display the design results in augmented reality (AR) and three-dimensional models (3D models), allowing users to overlay the green space design on the real space.

[0690] For example, if a city's data shows a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate, the generative AI model will provide an optimal green space design based on this data. If a user inputs their emotional state of "I want to relax," the emotion engine will recognize this and generate a green space design that includes many plants with a relaxing effect.

[0691] As a concrete example, the generative AI model is designed by inputting the following prompt sentence:

[0692] "Design an optimal green space for a city with a population density of 5000 people / km², available space of 10000m², and a temperate climate. The user wants to relax."

[0693] This will provide a concrete and practical system that effectively uses available space in urban areas as green space, contributing to the improvement of residents' welfare and the urban environment.

[0694] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0695] Step 1:

[0696] The server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data, green space design, and user emotion data. This completes the necessary initial setup.

[0697] Input: None

[0698] Output: An instance of the GreenAISystem class, city data properties, green space design properties, and emotion data properties.

[0699] Step 2:

[0700] The server loads the city data provided by the user into the server, which includes population density, available space, and climate information, and stores this data in properties of the city data.

[0701] Inputs: population density, available space, climate information

[0702] Output: Properties where city data is stored

[0703] Step 3:

[0704] The server calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data, using a generative artificial intelligence model to calculate plant types and placement.

[0705] Input: City data

[0706] Output: Optimal green space design (plant types and placement)

[0707] Step 4:

[0708] The server saves the optimal green space design in the property, and this design result is used in subsequent processing.

[0709] Input: Optimal green space design

[0710] Output: Saved green space design

[0711] Step 5:

[0712] The server uses an emotion engine to receive the user's emotional state as input, and the emotion engine analyzes the user's emotional data and stores the results in the properties of the emotional data.

[0713] Input: User's emotional state (e.g., "I want to relax")

[0714] Output: Parsed emotion data

[0715] Step 6:

[0716] The server adjusts the green space design based on the emotional data analyzed by the emotion engine. For example, if the user wants to relax, the server changes the design to include more plants that have a relaxing effect.

[0717] Input: Analyzed emotion data, optimal green space design

[0718] Output: Coordinated green space design

[0719] Step 7:

[0720] The device displays the adjusted green space design retrieved from the server in augmented reality (AR) or three-dimensional (3D) models, allowing users to check the design overlaid on the real space.

[0721] Input: Coordinated green space design

[0722] Output: Augmented reality or 3D model display

[0723] Step 8:

[0724] The user visually checks the green space design displayed on the device and, if necessary, sends further adjustment requests and feedback to the server, which then completes the final green space design that reflects the user's specific wishes.

[0725] Input: User feedback, adjustment requests

[0726] Output: Final design reflecting user wishes

[0727] 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.

[0728] 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.

[0729] 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.

[0730] [Third embodiment]

[0731] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0732] 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.

[0733] 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).

[0734] 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.

[0735] 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.

[0736] 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).

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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."

[0743] This invention relates to a system for designing urban green spaces using a generative artificial intelligence model. This system calculates optimal plant species and placement based on the city's population density, available space, and climate information, and then stores and outputs the resulting design. The program process and specific examples are described below.

[0744] Program processing

[0745] Server Processing

[0746] The server first initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data and green space designs. Then, it loads the city data provided by the user into the server. This data includes population density, available space, climate information, and more.

[0747] The server then calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data. Specifically, it uses a generative artificial intelligence model to calculate the optimal number of trees, shrubs, and flowers for the city's conditions, as well as their placement. The generated design is stored as a property on the server.

[0748] Finally, the server retrieves the generated green space design using the get_green_space_design method and outputs the design content.

[0749] Specific examples

[0750] For example, consider a Japanese city with a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate. When a user inputs this city data into the system, the server loads it and uses a generative AI model to generate an optimal green space design. This design could include, for example:

[0751] Trees: 75

[0752] Shrubs: 150

[0753] Flowers: 250 stems

[0754] Arrangement: 10x10 matrix

[0755] In this way, the present invention provides a green space design in urban areas that is sustainable and contributes to biodiversity, and also contributes to improving the welfare of residents.

[0756] The processing flow will be explained below.

[0757] Step 1:

[0758] The server creates an instance of the GreenAISystem class, which sets up properties to hold the city data and green space design.

[0759] Step 2:

[0760] Users input information such as the city's population density, available space, and climate information. This data is the basic information needed to design urban green spaces.

[0761] Step 3:

[0762] The server loads the city data provided by the user using the load_city_data method, which stores the entered data in the server.

[0763] Step 4:

[0764] The server calls the generate_green_space_design method, which generates a green space design based on the loaded city data.

[0765] Step 5:

[0766] The server uses a generative artificial intelligence model to calculate the optimal plant types and placement for urban conditions, specifically generating a matrix of tree numbers, shrub numbers, flower numbers, and placement.

[0767] Step 6:

[0768] The server stores the generated green space design in its properties, allowing you to retrieve the design information later.

[0769] Step 7:

[0770] The server uses the get_green_space_design method to retrieve the generated green space design, which includes the types of plants and their specific placement.

[0771] Step 8:

[0772] The server outputs the acquired design information to a terminal or other output means, allowing the user to check the generated design content.

[0773] Example 1

[0774] 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."

[0775] In recent years, environmental problems in urban areas have become more serious, making the design of sustainable green spaces an important issue. However, calculating the optimal plant types and placement requires a comprehensive analysis of various data, which presents a problem of difficulty in doing so efficiently and accurately. There is also a lack of systems that can memorize and quickly output calculated designs. This has led to a delay in the automation of green space design in urban planning.

[0776] 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.

[0777] In this invention, the server includes a means for initializing the system, a means for loading user-provided city data into the server, and a means for using a generative AI model to calculate the optimal plant types and placements for the city's conditions. This enables efficient and accurate automation of urban green space design. Furthermore, this invention includes a means for storing and quickly outputting the calculated green space design, thereby providing green space design information that can be immediately used in urban planning.

[0778] A "server" is a computer system or device that processes data, and is a device that processes requests from clients via a network.

[0779] "Initialization" is the process of preparing the necessary settings and resources when a system or program is started.

[0780] "User" refers to the person or entity that operates the system and inputs the required data.

[0781] "City data" refers to data that includes information about the state and characteristics of a city, including, for example, population density, available space, and climate information.

[0782] A "generative AI model" is an algorithm or mechanism that uses artificial intelligence technology to generate appropriate results or predictions based on specific input data.

[0783] "Green space design" is the process of calculating and determining the placement and type of plants in urban environments, with the aim of promoting urban greening.

[0784] "Memory" is the act or means of saving data or calculation results once obtained so that they can be reused.

[0785] "Output" is the act of providing calculation results or data in a form that can be displayed, printed, or sent to another system.

[0786] This invention relates to a system for designing urban green spaces using a generative artificial intelligence model. The system calculates optimal plant species and placement based on city data provided by the user, and stores and outputs the design.

[0787] First, the server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold the city data and green space design, preferably using the Python programming language and a database management system.

[0788] Next, the user inputs city data through the terminal. This data includes population density, available space, climate information, etc. The user inputs this data and sends it from the terminal to the server. The terminal interface provides an input form and is configured to ensure that the input data is accurately transferred to the server.

[0789] The server receives and loads city data provided by the user. Based on the loaded city data, the server uses a generative AI model to calculate the optimal green space design. This generative AI model has an algorithm for predicting the optimal plant types and placement in an urban environment. Here, the generative AI model receives city data as input and calculates the green space design.

[0790] For example, consider a Japanese city with a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate. When a user inputs this city data into the system, the server loads it and uses a generative AI model to generate an optimal green space design. This design could include, for example:

[0791] Trees: 75

[0792] Shrubs: 150

[0793] Flowers: 250 stems

[0794] Arrangement: 10x10 matrix

[0795] The green space design thus generated is stored in the server, and finally, the server can output the design content and provide it to users and other related systems.

[0796] An example of a prompt to input to a generative AI model is as follows:

[0797] The city has a population density of 5000 people / km², available space of 10000m², and a temperate climate. Generate an optimal green space design for these conditions.

[0798] This system will automate the design of sustainable green spaces in urban areas, contributing to increased biodiversity and improved welfare for residents.

[0799] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0800] Step 1: Initialize the system

[0801] The server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data and green space designs. The input is the initial settings and program startup information, and the output is the initialized system instance.

[0802] Specific behavior:

[0803] The server initializes the GreenAISystem class as follows:

[0804] python

[0805] green_ai_system = GreenAISystem()

[0806] urban_data and green_space_design are initialized as empty properties:

[0807] python

[0808] green_ai_system.urban_data = {}

[0809] green_ai_system.green_space_design = None

[0810] Step 2: Enter city data

[0811] The user inputs city data such as population density, available space, and climate information through the terminal. The input is the city data provided by the user, and the output is the data sent from the terminal to the server.

[0812] Specific behavior:

[0813] The user inputs data into an input form on the terminal.

[0814] Example: Population density: 5000 people / km², Available space: 10000m², Climate: Temperate

[0815] The user presses the send button to send the data to the server.

[0816] Step 3: Loading city data

[0817] The server receives and loads city data provided by the user. The input is the city data provided by the user, and the output is the data stored in the urban_data property in the server.

[0818] Specific behavior:

[0819] The server receives the data sent by the user:

[0820] python

[0821] provided_data = receive_data_from_user()

[0822] Store the data in the urban_data property:

[0823] python

[0824] green_ai_system.urban_data = provided_data

[0825] Step 4: Generate green space design

[0826] The server calls the generate_green_space_design method to generate an optimal green space design using a generative AI model. The input is the loaded city data, and the output is the generated green space design.

[0827] Specific behavior:

[0828] The server calls the generate_green_space_design method:

[0829] python

[0830] green_ai_system.generate_green_space_design()

[0831] Generative AI model uses city data to calculate optimal plant types and placement:

[0832] python

[0833] model_input = green_ai_system.urban_data

[0834] green_ai_system.green_space_design = ai_model.generate_design(model_input)

[0835] Step 5: Obtain and output the green space design

[0836] The server retrieves the generated green space design using the get_green_space_design method and outputs the design. The input is the calculated green space design, and the output is the design information presented to users and related systems.

[0837] Specific behavior:

[0838] Call the get_green_space_design method:

[0839] python

[0840] design = green_ai_system.get_green_space_design()

[0841] Export the design:

[0842] python

[0843] print(design)

[0844] (Application example 1)

[0845] 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."

[0846] In recent years, the design of urban green spaces has become increasingly important in order to address urban environmental issues and improve the quality of life for residents. However, conventional green space design methods are unable to properly utilize a wide range of data, making effective design difficult. In addition, selecting and arranging plants appropriate for the exterior and interior of physical stores requires specialized knowledge, which is time-consuming and laborious. This presents a challenge in that improvements to the urban environment and increased customer attraction are not being fully achieved.

[0847] 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.

[0848] In this invention, the server includes means for designing urban green spaces using a generative artificial intelligence model, means for calculating optimal plant types and arrangements based on the urban green space design, means for storing the calculated plant types and arrangements, means for recognizing spatial information about the exterior and interior of the store through smart glasses, and means for displaying the green space design on the smart glasses based on the recognized spatial information. This allows for the rapid and efficient provision of optimal green space designs based on urban conditions, thereby improving the environment of physical stores and increasing their customer attraction.

[0849] A "generative artificial intelligence model" is an artificial intelligence model that is built to generate a specific result or design based on input data.

[0850] "Urban green space" is an area of ​​vegetation and green space within a city that is designed to improve the environment and the well-being of residents.

[0851] "Calculation" refers to the process of finding the optimal result based on the data required for a specific purpose, and in this case refers to determining the types and placement of plants that are suitable for urban green spaces.

[0852] "Storage" is the process of saving calculated data or design information for later access.

[0853] "Smart glasses" are eyeglass-type devices that have built-in cameras and sensors and have the ability to overlay information on the user's field of vision.

[0854] "Spatial information" is data including the dimensions, shape, and layout of a specific area, and in this case refers to the external and internal location information of a store captured by smart glasses.

[0855] "Perception" is the process of processing information obtained through devices such as cameras and sensors to identify specific objects or situations.

[0856] "Display" refers to the visual output of information using an electronic device, and in this case refers to the overlay of a green space design on the display of smart glasses.

[0857] To implement this invention, the server must first initialize the system. During the system initialization phase, an instance of the GreenAISystem class is created and properties are set up to hold city data and green space designs. These properties include the city's population density, available space, and climate information.

[0858] Next, the server is loaded with user-provided city data, for example, by using smart glasses to capture spatial information about the exterior and interior of the store, and the cloud server retrieves the city data along with related data, including the city's climate and population density data.

[0859] The server then calls the generate_green_space_design method to generate an optimal green space design based on the city data previously loaded. This generation process uses a generative artificial intelligence model to calculate the number and placement of trees, shrubs, and flowers that are optimal for the city's conditions. The generated design is saved as a property on the server.

[0860] The server then retrieves the generated green space design using the get_green_space_design method and displays it on the smart glasses' display. The smart glasses visually show the user the placement of plants based on spatial data acquired through cameras and sensors. This display function allows the smart glasses to provide optimal green space designs for the exterior and interior of stores.

[0861] As a concrete example, let's take a scenario that applies to a "nature-inspired cafe" in a certain city. The cafe's exterior space is 12,000 square meters, the population density is 4,500 people / km², and the climate is temperate. When a user provides this information as city data, the cloud server retrieves the information and generates an optimal green space design. As a result, the following plant placements are proposed, for example:

[0862] Trees: 85

[0863] Shrubs: 170

[0864] Flowers: 280 stems

[0865] Arrangement: 12x12 matrix

[0866] Users can visually check these layouts through smart glasses and adjust them as needed. By efficiently and effectively designing green spaces based on the generated green space design, it is possible to improve the urban environment and increase the number of customers visiting stores.

[0867] Example prompt sentence:

[0868] Population density: 4500

[0869] Available space: 12000

[0870] Climate: Temperate

[0871] Store name: Natural Style Cafe

[0872] Store type: Restaurant

[0873] External design information: Image / video data from camera

[0874] Internal design information: spatial data from sensors

[0875] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0876] Step 1:

[0877] The server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data and green space design. The properties to be initialized include the city's population density, available space, and climate information. The input requires system configuration information, and the output is an initialized GreenAISystem instance.

[0878] Step 2:

[0879] The city data provided by the user (for example, the exterior and interior spatial information of a store acquired by smart glasses) is loaded into the server. The server receives this data and stores it in internal properties. The input requires city data and spatial information, and the output is the city data loaded internally.

[0880] Step 3:

[0881] The server calls the generate_green_space_design method to generate a green space design based on the loaded city data. During this process, it uses a generative artificial intelligence model to calculate the number and placement of trees, shrubs, and flowers appropriate for the city's conditions. The loaded city data is required as input, and the calculated green space design is obtained as output.

[0882] Step 4:

[0883] The server uses the get_green_space_design method to retrieve the generated green space design and convert it into a data format for display on the smart glasses. This conversion process sets the appropriate coordinates and size for display on the smart glasses display. The input requires the green space design data, and the output is the formatted data for display.

[0884] Step 5:

[0885] The smart glasses receive the formatted data from the server and overlay the green space design on the display. The user can visually confirm the proposed plant placement through the smart glasses. The input requires display data sent from the server, and the output is visual design information that the user can recognize.

[0886] Step 6:

[0887] The user can check the design through the smart glasses and provide feedback if necessary. If there is feedback, it is sent to the server, which then calls the generate_green_space_design method again to adjust the green space design. The input requires the user's feedback data, and the output is the adjusted green space design.

[0888] This series of processes enables the system to quickly and efficiently provide optimal green space designs based on urban conditions, improving the environment of physical stores and increasing their ability to attract customers.

[0889] 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.

[0890] The present invention relates to a system for designing urban green spaces using a generative artificial intelligence model. The system calculates optimal plant species and placement based on a city's population density, available space, and climate information, and then stores and outputs the design. The present invention also includes an emotion engine that recognizes a user's emotions and adjusts the green space design accordingly.

[0891] Program processing

[0892] Server Processing

[0893] The server first initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data, green space design, and user emotion data. Next, it loads the city data provided by the user into the server. This data includes population density, available space, and weather information.

[0894] The server then calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data. Specifically, it uses a generative artificial intelligence model to calculate the optimal number of trees, shrubs, and flowers for the city's conditions, as well as their placement. The generated design is stored as a property on the server.

[0895] The server also uses an emotion engine that recognizes the user's emotions. The emotion engine analyzes emotions from user input and sensor data and adjusts the green space design based on those emotions, taking into account past user emotion data. For example, if the user wants to relax, the server generates a design that includes many plants with a relaxing effect.

[0896] Finally, the server retrieves the generated green space design using the get_green_space_design method and outputs the design content.

[0897] Specific examples

[0898] For example, consider a Japanese city with a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate. When a user inputs this city data into the system, the server loads it and uses a generative AI model to generate an optimal green space design. This design could include, for example:

[0899] Trees: 75

[0900] Shrubs: 150

[0901] Flowers: 250 stems

[0902] Arrangement: 10x10 matrix

[0903] Furthermore, if the user expresses their desire to relax through sensor input or direct input, the emotion engine will recognize this and adjust the design to include more plants with relaxing effects, such as lavender and jasmine, based on the user's emotions.

[0904] This system provides sustainable green space designs in urban areas that contribute to biodiversity, and by combining a generative artificial intelligence model with an emotion engine, it also contributes to improving the welfare of residents.

[0905] The processing flow will be explained below.

[0906] Step 1:

[0907] The server creates an instance of the GreenAISystem class, which sets up properties to hold city data, green space design, and user emotion data.

[0908] Step 2:

[0909] Users input information about the city's population density, available space, and climate, which are the basic information for green space design.

[0910] Step 3:

[0911] The server loads the city data provided by the user using the load_city_data method, which saves the entered data in the server.

[0912] Step 4:

[0913] The server invokes the generate_green_space_design method, which generates an optimal green space design based on the loaded city data using a generative artificial intelligence model.

[0914] Step 5:

[0915] The server uses a generative artificial intelligence model to calculate the number of trees, shrubs, and flowers, as well as a placement matrix based on urban conditions, which then generates a specific green space design.

[0916] Step 6:

[0917] The server stores the generated green space design in a property, which allows the design information to be output later.

[0918] Step 7:

[0919] The user inputs emotion data, which indicates the user's feelings such as a desire to relax or to be energized.

[0920] Step 8:

[0921] The server analyzes the user's emotional data using an emotion engine, which recognizes emotions from user input and sensor data and adjusts the green space design accordingly.

[0922] Step 9:

[0923] The server adjusts the green space design based on the analysis results of the emotion engine. For example, if the user wants to relax, the design will be revised to include more plants with relaxing effects, such as lavender and jasmine.

[0924] Step 10:

[0925] The server retrieves the generated green space design using the get_green_space_design method, which saves the final design within the server.

[0926] Step 11:

[0927] The server outputs the acquired design information to the terminal, allowing the user to check the generated green space design.

[0928] Example 2

[0929] 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."

[0930] Until now, there has been no system that can efficiently and effectively design urban green spaces. In particular, there is a need for a system that can take into account various data such as the city's population density, available space, and climate information, and can also adjust green space design based on user emotional data. However, existing systems have had the problem of being unable to design individual green spaces based on the user's emotions and preferences.

[0931] 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.

[0932] In this invention, the server includes means for designing urban green spaces using a generative artificial intelligence model, means for calculating optimal plant types and arrangements based on the urban green space design, means for analyzing user emotion data and adjusting the calculated design, means for storing the calculated plant types and arrangements, and means for outputting the design and adjusted arrangement information. This enables optimal urban green space design that takes into account the diverse conditions of the city and the user's emotions.

[0933] A "generative artificial intelligence model" is a computer program that is trained to automatically perform specific tasks from data using machine learning algorithms.

[0934] "Urban green space" refers to a green area established within a city to protect or provide a natural environment.

[0935] "Design" is the process of planning and documenting a configuration or arrangement based on specific goals and requirements.

[0936] "Plant types" refer to different plant species (trees, shrubs, flowering plants, etc.) in biological classification.

[0937] "Arrangement" refers to how elements or objects are arranged and positioned within a particular area.

[0938] "User emotion data" is information that quantitatively or qualitatively expresses the user's emotional state.

[0939] An "emotion engine" is a software component that analyzes sensor data and user input data to identify a user's emotions.

[0940] "Storage" is the process or function of storing data so that it can be retrieved at a later time.

[0941] "Output" is the process of displaying, printing, or transmitting processed data or information for viewing by an external system or user.

[0942] "Population density" is the number of people living in a particular geographic area.

[0943] "Available space" is the physical area that can be used for a particular purpose.

[0944] "Climate information" is data on weather conditions such as temperature, precipitation, and humidity.

[0945] "Property" is a concept that refers to the characteristics or attributes of a particular object or data element.

[0946] "Adjustment" is the act of changing or modifying something to suit particular conditions or requirements.

[0947] An "instance" is a specific instance of a class or object, a concrete object created within a program based on the definition of that class.

[0948] The present invention relates to a system for designing urban green spaces using a generative artificial intelligence model. The system calculates optimal plant species and placement based on a city's population density, available space, and climate information, and stores and outputs the design. It also includes an emotion engine that recognizes user emotions and adjusts the design based on the emotion data.

[0949] Server Processing

[0950] The server first initializes the system. At this stage, it creates an instance of the GreenAISystem class and sets up properties to hold city data, green space design, and user emotion data. It also loads the necessary libraries and generative AI models.

[0951] The server then loads the city data provided by the user, including population density, available space, climate information, etc. The server validates this data and stores it in the correct format.

[0952] The server then calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data. It uses a generative artificial intelligence model to calculate the optimal number of trees, shrubs, and flowers, and their placement, for the city's conditions. The results of this calculation are stored in a property on the server.

[0953] Additionally, the server uses an emotion engine that recognizes the user's emotions. The emotion engine analyzes emotions from user input and sensor data and adjusts the green space design based on those emotions. Past user emotion data is also taken into account. For example, if the user wants to relax, the server generates a design that includes many plants with a relaxing effect.

[0954] Finally, the server retrieves the generated green space design using the get_green_space_design method and outputs the design to the user, who can then view the final green space design on their terminal. The design is displayed in text and graphical formats.

[0955] Hardware and software used

[0956] Hardware: Server (e.g., general-purpose computer system), user device (e.g., PC, smartphone), emotion recognition sensor (e.g., video camera, microphone)

[0957] Software: Generative AI models (e.g., GPT-3), emotion engines (e.g., Affectiva SDK)

[0958] Specific examples

[0959] For example, consider a Japanese city with a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate. When a user inputs this city data into the system, the server loads it and uses a generative artificial intelligence model to generate an optimal green space design. This design includes:

[0960] Trees: 75

[0961] Shrubs: 150

[0962] Flowers: 250 stems

[0963] Arrangement: 10x10 matrix

[0964] When the user expresses their desire to relax through sensor input or direct input, the emotion engine recognizes this and adjusts the design to include more plants with relaxing effects, such as lavender and jasmine.

[0965] Prompt Sentence Examples

[0966] For example:

[0967] "I would like to design a green space for a city. The population density of the city is 5,000 people / km², the available space is 10,000 m², and the climate is temperate. Users want to relax. Based on these conditions, please propose the optimal green space design."

[0968] In this way, the present invention provides a sustainable green space design in urban areas that contributes to biodiversity and also contributes to improving the welfare of residents.

[0969] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0970] Step 1:

[0971] Initializing the Server

[0972] The server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data, green space design, and user emotion data. It also loads the necessary libraries and generative AI models.

[0973] input:

[0974] Initial setting data

[0975] output:

[0976] A set up GreenAISystem instance

[0977] Specific behavior:

[0978] GreenAISystem = new GreenAISystem(); This code creates an instance.

[0979] Step 2:

[0980] Entering and Loading City Data

[0981] Users input city data (e.g., population density, available space, climate information) through a terminal. The server receives this data, loads it into the system, and verifies that it is in the correct format.

[0982] input:

[0983] Population density, available space, and climate information

[0984] output:

[0985] Loaded city data

[0986] Specific behavior:

[0987] GreenAISystem.load_city_data(5000, 10000, 'Temperate'); This method loads the data.

[0988] Step 3:

[0989] Generate green space designs

[0990] The server calls the generate_green_space_design method to generate an optimal green space design based on city data. It uses a generative artificial intelligence model to calculate the number of trees, shrubs, and flowers, and their placement.

[0991] input:

[0992] Loaded city data

[0993] output:

[0994] Generated green space design data (number of trees, number of shrubs, number of flowers, and their placement)

[0995] Specific behavior:

[0996] GreenAISystem.generate_green_space_design(); This method is called and the AI ​​model performs the calculations.

[0997] Step 4:

[0998] Emotion data input and analysis

[0999] The user inputs their emotion (e.g., "I want to relax") through a device or sensor. The server receives this emotion data and analyzes it using an emotion engine.

[1000] input:

[1001] User emotion data

[1002] output:

[1003] Analyzed emotion data

[1004] Specific behavior:

[1005] GreenAISystem.analyze_emotion('I want to relax'); This method analyzes the sensor data.

[1006] Step 5:

[1007] Coordination of green space design

[1008] The server adjusts the green space design based on the analysis results of the emotion engine, for example by changing the design to include more plants that have a relaxing effect.

[1009] input:

[1010] Analyzed emotion data, generated green space design data

[1011] output:

[1012] Adjusted green space design data

[1013] Specific behavior:

[1014] GreenAISystem.adjust_design_based_on_emotion('I want to relax'); This method adjusts the design based on emotion.

[1015] Step 6:

[1016] Final green space design output

[1017] The server retrieves the final green space design using the get_green_space_design method and sends it to the user's device, where the user can view the design.

[1018] input:

[1019] Adjusted green space design data

[1020] output:

[1021] Final green space design data

[1022] Specific behavior:

[1023] final_design = GreenAISystem.get_green_space_design(); This method retrieves the final design and outputs it to the user.

[1024] These are the specific processing steps of this system, which enables optimal urban green space design that takes into account the diverse conditions of the city and the emotions of users.

[1025] (Application example 2)

[1026] 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."

[1027] There is a need to effectively utilize available space in urban areas as green spaces, contributing to the welfare of residents and improving the urban environment. However, while conventional green space design systems can optimally select and arrange plants based on urban data, they are unable to adjust the plantings based on the user's emotions or specific preferences. To address these issues, the present invention provides a system that realizes green space designs that reflect the user's emotions and visually displays the designs using augmented reality or a three-dimensional model.

[1028] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for designing urban green space using a generative artificial intelligence model, means for calculating optimal plant types and arrangements based on the urban green space design, means for storing the calculated plant types and arrangements, means for adjusting the plant arrangements based on the user's emotional state, means for inputting and analyzing the user's emotions, and means for displaying the design results in augmented reality or a three-dimensional model. This makes it possible to provide customized green space designs using city data and the user's emotional state.

[1029] A "generative artificial intelligence model" is an algorithm or computational model that analyzes the complex data required for designing urban green spaces and generates optimal plant types and placements.

[1030] "Urban green space design" is the planning of plant types and placements to effectively use available space in urban areas as green spaces.

[1031] "Calculation of plant types and placement" refers to calculations based on city data and climate information to ensure that plants selected for urban green space design are optimally placed.

[1032] "Storing the calculated plant types and arrangements" means saving the generated green space design information as digital data.

[1033] "Adjustment based on the user's emotional state" means optimizing the initial green space design based on emotions analyzed from user input and sensor data, and providing a green space design that takes the user's emotional state into account.

[1034] "Emotion input and analysis" refers to acquiring a user's emotional data and analyzing it to identify a specific emotional state.

[1035] "Augmented reality or three-dimensional model display" is a technology that displays the generated green space design in a virtual space using a smartphone or head-mounted display, allowing the user to visually confirm the design.

[1036] "City data" refers to data such as a city's population density, available space, and climate information.

[1037] A "prompt sentence format" is a sentence that is presented to the user in a format that is easy for the user to understand as input to a generative AI model.

[1038] To implement this invention, a system for designing urban green spaces is required. This system includes a server, a user's device (such as a smartphone, smart glasses, or a head-mounted display), a generative artificial intelligence model, and an emotion engine.

[1039] The server first initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data, green space design, and user emotion data. Next, it loads the city data provided by the user into the server. This data includes population density, available space, and weather information.

[1040] The server then calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data. Specifically, it uses a generative artificial intelligence model to calculate the optimal plant types and placement for the city's conditions. The generated design is stored in a property on the server.

[1041] The server also uses an emotion engine that recognizes the user's emotions. The emotion engine analyzes emotions from user input and sensor data and adjusts the green space design based on those emotions, taking into account past user emotion data. For example, if the user wants to relax, the server generates a design that includes many plants with a relaxing effect.

[1042] The final design results can be visually confirmed on the user's device, which has the ability to display the design results in augmented reality (AR) and three-dimensional models (3D models), allowing users to overlay the green space design on the real space.

[1043] For example, if a city's data shows a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate, the generative AI model will provide an optimal green space design based on this data. If a user inputs their emotional state of "I want to relax," the emotion engine will recognize this and generate a green space design that includes many plants with a relaxing effect.

[1044] As a concrete example, the generative AI model is designed by inputting the following prompt sentence:

[1045] "Design an optimal green space for a city with a population density of 5000 people / km², available space of 10000m², and a temperate climate. The user wants to relax."

[1046] This will provide a concrete and practical system that effectively uses available space in urban areas as green space, contributing to the improvement of residents' welfare and the urban environment.

[1047] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1048] Step 1:

[1049] The server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data, green space design, and user emotion data. This completes the necessary initial setup.

[1050] Input: None

[1051] Output: An instance of the GreenAISystem class, city data properties, green space design properties, and emotion data properties.

[1052] Step 2:

[1053] The server loads the city data provided by the user into the server, which includes population density, available space, and climate information, and stores this data in properties of the city data.

[1054] Inputs: population density, available space, climate information

[1055] Output: Properties where city data is stored

[1056] Step 3:

[1057] The server calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data, using a generative artificial intelligence model to calculate plant types and placement.

[1058] Input: City data

[1059] Output: Optimal green space design (plant types and placement)

[1060] Step 4:

[1061] The server saves the optimal green space design in the property, and this design result is used in subsequent processing.

[1062] Input: Optimal green space design

[1063] Output: Saved green space design

[1064] Step 5:

[1065] The server uses an emotion engine to receive the user's emotional state as input, and the emotion engine analyzes the user's emotional data and stores the results in the properties of the emotional data.

[1066] Input: User's emotional state (e.g., "I want to relax")

[1067] Output: Parsed emotion data

[1068] Step 6:

[1069] The server adjusts the green space design based on the emotional data analyzed by the emotion engine. For example, if the user wants to relax, the server changes the design to include more plants that have a relaxing effect.

[1070] Input: Analyzed emotion data, optimal green space design

[1071] Output: Coordinated green space design

[1072] Step 7:

[1073] The device displays the adjusted green space design retrieved from the server in augmented reality (AR) or three-dimensional (3D) models, allowing users to check the design overlaid on the real space.

[1074] Input: Coordinated green space design

[1075] Output: Augmented reality or 3D model display

[1076] Step 8:

[1077] The user visually checks the green space design displayed on the device and, if necessary, sends further adjustment requests and feedback to the server, which then completes the final green space design that reflects the user's specific wishes.

[1078] Input: User feedback, adjustment requests

[1079] Output: Final design reflecting user wishes

[1080] 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.

[1081] 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.

[1082] 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.

[1083] [Fourth embodiment]

[1084] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1085] 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.

[1086] 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).

[1087] 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.

[1088] 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.

[1089] 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).

[1090] 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.

[1091] 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.

[1092] 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.

[1093] 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.

[1094] 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.

[1095] 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.

[1096] 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."

[1097] This invention relates to a system for designing urban green spaces using a generative artificial intelligence model. This system calculates optimal plant species and placement based on the city's population density, available space, and climate information, and then stores and outputs the resulting design. The program process and specific examples are described below.

[1098] Program processing

[1099] Server Processing

[1100] The server first initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data and green space designs. Then, it loads the city data provided by the user into the server. This data includes population density, available space, climate information, and more.

[1101] The server then calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data. Specifically, it uses a generative artificial intelligence model to calculate the optimal number of trees, shrubs, and flowers for the city's conditions, as well as their placement. The generated design is stored as a property on the server.

[1102] Finally, the server retrieves the generated green space design using the get_green_space_design method and outputs the design content.

[1103] Specific examples

[1104] For example, consider a Japanese city with a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate. When a user inputs this city data into the system, the server loads it and uses a generative AI model to generate an optimal green space design. This design could include, for example:

[1105] Trees: 75

[1106] Shrubs: 150

[1107] Flowers: 250 stems

[1108] Arrangement: 10x10 matrix

[1109] In this way, the present invention provides a green space design in urban areas that is sustainable and contributes to biodiversity, and also contributes to improving the welfare of residents.

[1110] The processing flow will be explained below.

[1111] Step 1:

[1112] The server creates an instance of the GreenAISystem class, which sets up properties to hold the city data and green space design.

[1113] Step 2:

[1114] Users input information such as the city's population density, available space, and climate information. This data is the basic information needed to design urban green spaces.

[1115] Step 3:

[1116] The server loads the city data provided by the user using the load_city_data method, which stores the entered data in the server.

[1117] Step 4:

[1118] The server calls the generate_green_space_design method, which generates a green space design based on the loaded city data.

[1119] Step 5:

[1120] The server uses a generative artificial intelligence model to calculate the optimal plant types and placement for urban conditions, specifically generating a matrix of tree numbers, shrub numbers, flower numbers, and placement.

[1121] Step 6:

[1122] The server stores the generated green space design in its properties, allowing you to retrieve the design information later.

[1123] Step 7:

[1124] The server uses the get_green_space_design method to retrieve the generated green space design, which includes the types of plants and their specific placement.

[1125] Step 8:

[1126] The server outputs the acquired design information to a terminal or other output means, allowing the user to check the generated design content.

[1127] Example 1

[1128] 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."

[1129] In recent years, environmental problems in urban areas have become more serious, making the design of sustainable green spaces an important issue. However, calculating the optimal plant types and placement requires a comprehensive analysis of various data, which presents a problem of difficulty in doing so efficiently and accurately. There is also a lack of systems that can memorize and quickly output calculated designs. This has led to a delay in the automation of green space design in urban planning.

[1130] 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.

[1131] In this invention, the server includes a means for initializing the system, a means for loading user-provided city data into the server, and a means for using a generative AI model to calculate the optimal plant types and placements for the city's conditions. This enables efficient and accurate automation of urban green space design. Furthermore, this invention includes a means for storing and quickly outputting the calculated green space design, thereby providing green space design information that can be immediately used in urban planning.

[1132] A "server" is a computer system or device that processes data, and is a device that processes requests from clients via a network.

[1133] "Initialization" is the process of preparing the necessary settings and resources when a system or program is started.

[1134] "User" refers to the person or entity that operates the system and inputs the required data.

[1135] "City data" refers to data that includes information about the state and characteristics of a city, including, for example, population density, available space, and climate information.

[1136] A "generative AI model" is an algorithm or mechanism that uses artificial intelligence technology to generate appropriate results or predictions based on specific input data.

[1137] "Green space design" is the process of calculating and determining the placement and type of plants in urban environments, with the aim of promoting urban greening.

[1138] "Memory" is the act or means of saving data or calculation results once obtained so that they can be reused.

[1139] "Output" is the act of providing calculation results or data in a form that can be displayed, printed, or sent to another system.

[1140] This invention relates to a system for designing urban green spaces using a generative artificial intelligence model. The system calculates optimal plant species and placement based on city data provided by the user, and stores and outputs the design.

[1141] First, the server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold the city data and green space design, preferably using the Python programming language and a database management system.

[1142] Next, the user inputs city data through the terminal. This data includes population density, available space, climate information, etc. The user inputs this data and sends it from the terminal to the server. The terminal interface provides an input form and is configured to ensure that the input data is accurately transferred to the server.

[1143] The server receives and loads city data provided by the user. Based on the loaded city data, the server uses a generative AI model to calculate the optimal green space design. This generative AI model has an algorithm for predicting the optimal plant types and placement in an urban environment. Here, the generative AI model receives city data as input and calculates the green space design.

[1144] For example, consider a Japanese city with a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate. When a user inputs this city data into the system, the server loads it and uses a generative AI model to generate an optimal green space design. This design could include, for example:

[1145] Trees: 75

[1146] Shrubs: 150

[1147] Flowers: 250 stems

[1148] Arrangement: 10x10 matrix

[1149] The green space design thus generated is stored in the server, and finally, the server can output the design content and provide it to users and other related systems.

[1150] An example of a prompt to input to a generative AI model is as follows:

[1151] The city has a population density of 5000 people / km², available space of 10000m², and a temperate climate. Generate an optimal green space design for these conditions.

[1152] This system will automate the design of sustainable green spaces in urban areas, contributing to increased biodiversity and improved welfare for residents.

[1153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1154] Step 1: Initialize the system

[1155] The server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data and green space designs. The input is the initial settings and program startup information, and the output is the initialized system instance.

[1156] Specific behavior:

[1157] The server initializes the GreenAISystem class as follows:

[1158] python

[1159] green_ai_system = GreenAISystem()

[1160] urban_data and green_space_design are initialized as empty properties:

[1161] python

[1162] green_ai_system.urban_data = {}

[1163] green_ai_system.green_space_design = None

[1164] Step 2: Enter city data

[1165] The user inputs city data such as population density, available space, and climate information through the terminal. The input is the city data provided by the user, and the output is the data sent from the terminal to the server.

[1166] Specific behavior:

[1167] The user inputs data into an input form on the terminal.

[1168] Example: Population density: 5000 people / km², Available space: 10000m², Climate: Temperate

[1169] The user presses the send button to send the data to the server.

[1170] Step 3: Loading city data

[1171] The server receives and loads city data provided by the user. The input is the city data provided by the user, and the output is the data stored in the urban_data property in the server.

[1172] Specific behavior:

[1173] The server receives the data sent by the user:

[1174] python

[1175] provided_data = receive_data_from_user()

[1176] Store the data in the urban_data property:

[1177] python

[1178] green_ai_system.urban_data = provided_data

[1179] Step 4: Generate green space design

[1180] The server calls the generate_green_space_design method to generate an optimal green space design using a generative AI model. The input is the loaded city data, and the output is the generated green space design.

[1181] Specific behavior:

[1182] The server calls the generate_green_space_design method:

[1183] python

[1184] green_ai_system.generate_green_space_design()

[1185] Generative AI model uses city data to calculate optimal plant types and placement:

[1186] python

[1187] model_input = green_ai_system.urban_data

[1188] green_ai_system.green_space_design = ai_model.generate_design(model_input)

[1189] Step 5: Obtain and output the green space design

[1190] The server retrieves the generated green space design using the get_green_space_design method and outputs the design. The input is the calculated green space design, and the output is the design information presented to users and related systems.

[1191] Specific behavior:

[1192] Call the get_green_space_design method:

[1193] python

[1194] design = green_ai_system.get_green_space_design()

[1195] Export the design:

[1196] python

[1197] print(design)

[1198] (Application example 1)

[1199] 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."

[1200] In recent years, the design of urban green spaces has become increasingly important in order to address urban environmental issues and improve the quality of life for residents. However, conventional green space design methods are unable to properly utilize a wide range of data, making effective design difficult. In addition, selecting and arranging plants appropriate for the exterior and interior of physical stores requires specialized knowledge, which is time-consuming and laborious. This presents a challenge in that improvements to the urban environment and increased customer attraction are not being fully achieved.

[1201] 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.

[1202] In this invention, the server includes means for designing urban green spaces using a generative artificial intelligence model, means for calculating optimal plant types and arrangements based on the urban green space design, means for storing the calculated plant types and arrangements, means for recognizing spatial information about the exterior and interior of the store through smart glasses, and means for displaying the green space design on the smart glasses based on the recognized spatial information. This allows for the rapid and efficient provision of optimal green space designs based on urban conditions, thereby improving the environment of physical stores and increasing their customer attraction.

[1203] A "generative artificial intelligence model" is an artificial intelligence model that is built to generate a specific result or design based on input data.

[1204] "Urban green space" is an area of ​​vegetation and green space within a city that is designed to improve the environment and the well-being of residents.

[1205] "Calculation" refers to the process of finding the optimal result based on the data required for a specific purpose, and in this case refers to determining the types and placement of plants that are suitable for urban green spaces.

[1206] "Storage" is the process of saving calculated data or design information for later access.

[1207] "Smart glasses" are eyeglass-type devices that have built-in cameras and sensors and have the ability to overlay information on the user's field of vision.

[1208] "Spatial information" is data including the dimensions, shape, and layout of a specific area, and in this case refers to the external and internal location information of a store captured by smart glasses.

[1209] "Perception" is the process of processing information obtained through devices such as cameras and sensors to identify specific objects or situations.

[1210] "Display" refers to the visual output of information using an electronic device, and in this case refers to the overlay of a green space design on the display of smart glasses.

[1211] To implement this invention, the server must first initialize the system. During the system initialization phase, an instance of the GreenAISystem class is created and properties are set up to hold city data and green space designs. These properties include the city's population density, available space, and climate information.

[1212] Next, the server is loaded with user-provided city data, for example, by using smart glasses to capture spatial information about the exterior and interior of the store, and the cloud server retrieves the city data along with related data, including the city's climate and population density data.

[1213] The server then calls the generate_green_space_design method to generate an optimal green space design based on the city data previously loaded. This generation process uses a generative artificial intelligence model to calculate the number and placement of trees, shrubs, and flowers that are optimal for the city's conditions. The generated design is saved as a property on the server.

[1214] The server then retrieves the generated green space design using the get_green_space_design method and displays it on the smart glasses' display. The smart glasses visually show the user the placement of plants based on spatial data acquired through cameras and sensors. This display function allows the smart glasses to provide optimal green space designs for the exterior and interior of stores.

[1215] As a concrete example, let's take a scenario that applies to a "nature-inspired cafe" in a certain city. The cafe's exterior space is 12,000 square meters, the population density is 4,500 people / km², and the climate is temperate. When a user provides this information as city data, the cloud server retrieves the information and generates an optimal green space design. As a result, the following plant placements are proposed, for example:

[1216] Trees: 85

[1217] Shrubs: 170

[1218] Flowers: 280 stems

[1219] Arrangement: 12x12 matrix

[1220] Users can visually check these layouts through smart glasses and adjust them as needed. By efficiently and effectively designing green spaces based on the generated green space design, it is possible to improve the urban environment and increase the number of customers visiting stores.

[1221] Example prompt sentence:

[1222] Population density: 4500

[1223] Available space: 12000

[1224] Climate: Temperate

[1225] Store name: Natural Style Cafe

[1226] Store type: Restaurant

[1227] External design information: Image / video data from camera

[1228] Internal design information: spatial data from sensors

[1229] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1230] Step 1:

[1231] The server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data and green space design. The properties to be initialized include the city's population density, available space, and climate information. The input requires system configuration information, and the output is an initialized GreenAISystem instance.

[1232] Step 2:

[1233] The city data provided by the user (for example, the exterior and interior spatial information of a store acquired by smart glasses) is loaded into the server. The server receives this data and stores it in internal properties. The input requires city data and spatial information, and the output is the city data loaded internally.

[1234] Step 3:

[1235] The server calls the generate_green_space_design method to generate a green space design based on the loaded city data. During this process, it uses a generative artificial intelligence model to calculate the number and placement of trees, shrubs, and flowers appropriate for the city's conditions. The loaded city data is required as input, and the calculated green space design is obtained as output.

[1236] Step 4:

[1237] The server uses the get_green_space_design method to retrieve the generated green space design and convert it into a data format for display on the smart glasses. This conversion process sets the appropriate coordinates and size for display on the smart glasses display. The input requires the green space design data, and the output is the formatted data for display.

[1238] Step 5:

[1239] The smart glasses receive the formatted data from the server and overlay the green space design on the display. The user can visually confirm the proposed plant placement through the smart glasses. The input requires display data sent from the server, and the output is visual design information that the user can recognize.

[1240] Step 6:

[1241] The user can check the design through the smart glasses and provide feedback if necessary. If there is feedback, it is sent to the server, which then calls the generate_green_space_design method again to adjust the green space design. The input requires the user's feedback data, and the output is the adjusted green space design.

[1242] This series of processes enables the system to quickly and efficiently provide optimal green space designs based on urban conditions, improving the environment of physical stores and increasing their ability to attract customers.

[1243] 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.

[1244] The present invention relates to a system for designing urban green spaces using a generative artificial intelligence model. The system calculates optimal plant species and placement based on a city's population density, available space, and climate information, and then stores and outputs the design. The present invention also includes an emotion engine that recognizes a user's emotions and adjusts the green space design accordingly.

[1245] Program processing

[1246] Server Processing

[1247] The server first initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data, green space design, and user emotion data. Next, it loads the city data provided by the user into the server. This data includes population density, available space, and weather information.

[1248] The server then calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data. Specifically, it uses a generative artificial intelligence model to calculate the optimal number of trees, shrubs, and flowers for the city's conditions, as well as their placement. The generated design is stored as a property on the server.

[1249] The server also uses an emotion engine that recognizes the user's emotions. The emotion engine analyzes emotions from user input and sensor data and adjusts the green space design based on those emotions, taking into account past user emotion data. For example, if the user wants to relax, the server generates a design that includes many plants with a relaxing effect.

[1250] Finally, the server retrieves the generated green space design using the get_green_space_design method and outputs the design content.

[1251] Specific examples

[1252] For example, consider a Japanese city with a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate. When a user inputs this city data into the system, the server loads it and uses a generative AI model to generate an optimal green space design. This design could include, for example:

[1253] Trees: 75

[1254] Shrubs: 150

[1255] Flowers: 250 stems

[1256] Arrangement: 10x10 matrix

[1257] Furthermore, if the user expresses their desire to relax through sensor input or direct input, the emotion engine will recognize this and adjust the design to include more plants with relaxing effects, such as lavender and jasmine, based on the user's emotions.

[1258] This system provides sustainable green space designs in urban areas that contribute to biodiversity, and by combining a generative artificial intelligence model with an emotion engine, it also contributes to improving the welfare of residents.

[1259] The processing flow will be explained below.

[1260] Step 1:

[1261] The server creates an instance of the GreenAISystem class, which sets up properties to hold city data, green space design, and user emotion data.

[1262] Step 2:

[1263] Users input information about the city's population density, available space, and climate, which are the basic information for green space design.

[1264] Step 3:

[1265] The server loads the city data provided by the user using the load_city_data method, which saves the entered data in the server.

[1266] Step 4:

[1267] The server invokes the generate_green_space_design method, which generates an optimal green space design based on the loaded city data using a generative artificial intelligence model.

[1268] Step 5:

[1269] The server uses a generative artificial intelligence model to calculate the number of trees, shrubs, and flowers, as well as a placement matrix based on urban conditions, which then generates a specific green space design.

[1270] Step 6:

[1271] The server stores the generated green space design in a property, which allows the design information to be output later.

[1272] Step 7:

[1273] The user inputs emotion data, which indicates the user's feelings such as a desire to relax or to be energized.

[1274] Step 8:

[1275] The server analyzes the user's emotional data using an emotion engine, which recognizes emotions from user input and sensor data and adjusts the green space design accordingly.

[1276] Step 9:

[1277] The server adjusts the green space design based on the analysis results of the emotion engine. For example, if the user wants to relax, the design will be revised to include more plants with relaxing effects, such as lavender and jasmine.

[1278] Step 10:

[1279] The server retrieves the generated green space design using the get_green_space_design method, which saves the final design within the server.

[1280] Step 11:

[1281] The server outputs the acquired design information to the terminal, allowing the user to check the generated green space design.

[1282] Example 2

[1283] 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."

[1284] Until now, there has been no system that can efficiently and effectively design urban green spaces. In particular, there is a need for a system that can take into account various data such as the city's population density, available space, and climate information, and can also adjust green space design based on user emotional data. However, existing systems have had the problem of being unable to design individual green spaces based on the user's emotions and preferences.

[1285] 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.

[1286] In this invention, the server includes means for designing urban green spaces using a generative artificial intelligence model, means for calculating optimal plant types and arrangements based on the urban green space design, means for analyzing user emotion data and adjusting the calculated design, means for storing the calculated plant types and arrangements, and means for outputting the design and adjusted arrangement information. This enables optimal urban green space design that takes into account the diverse conditions of the city and the user's emotions.

[1287] A "generative artificial intelligence model" is a computer program that is trained to automatically perform specific tasks from data using machine learning algorithms.

[1288] "Urban green space" refers to a green area established within a city to protect or provide a natural environment.

[1289] "Design" is the process of planning and documenting a configuration or arrangement based on specific goals and requirements.

[1290] "Plant types" refer to different plant species (trees, shrubs, flowering plants, etc.) in biological classification.

[1291] "Arrangement" refers to how elements or objects are arranged and positioned within a particular area.

[1292] "User emotion data" is information that quantitatively or qualitatively expresses the user's emotional state.

[1293] An "emotion engine" is a software component that analyzes sensor data and user input data to identify a user's emotions.

[1294] "Storage" is the process or function of storing data so that it can be retrieved at a later time.

[1295] "Output" is the process of displaying, printing, or transmitting processed data or information for viewing by an external system or user.

[1296] "Population density" is the number of people living in a particular geographic area.

[1297] "Available space" is the physical area that can be used for a particular purpose.

[1298] "Climate information" is data on weather conditions such as temperature, precipitation, and humidity.

[1299] "Property" is a concept that refers to the characteristics or attributes of a particular object or data element.

[1300] "Adjustment" is the act of changing or modifying something to suit particular conditions or requirements.

[1301] An "instance" is a specific instance of a class or object, a concrete object created within a program based on the definition of that class.

[1302] The present invention relates to a system for designing urban green spaces using a generative artificial intelligence model. The system calculates optimal plant species and placement based on a city's population density, available space, and climate information, and stores and outputs the design. It also includes an emotion engine that recognizes user emotions and adjusts the design based on the emotion data.

[1303] Server Processing

[1304] The server first initializes the system. At this stage, it creates an instance of the GreenAISystem class and sets up properties to hold city data, green space design, and user emotion data. It also loads the necessary libraries and generative AI models.

[1305] The server then loads the city data provided by the user, including population density, available space, climate information, etc. The server validates this data and stores it in the correct format.

[1306] The server then calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data. It uses a generative artificial intelligence model to calculate the optimal number of trees, shrubs, and flowers, and their placement, for the city's conditions. The results of this calculation are stored in a property on the server.

[1307] Additionally, the server uses an emotion engine that recognizes the user's emotions. The emotion engine analyzes emotions from user input and sensor data and adjusts the green space design based on those emotions. Past user emotion data is also taken into account. For example, if the user wants to relax, the server generates a design that includes many plants with a relaxing effect.

[1308] Finally, the server retrieves the generated green space design using the get_green_space_design method and outputs the design to the user, who can then view the final green space design on their terminal. The design is displayed in text and graphical formats.

[1309] Hardware and software used

[1310] Hardware: Server (e.g., general-purpose computer system), user device (e.g., PC, smartphone), emotion recognition sensor (e.g., video camera, microphone)

[1311] Software: Generative AI models (e.g., GPT-3), emotion engines (e.g., Affectiva SDK)

[1312] Specific examples

[1313] For example, consider a Japanese city with a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate. When a user inputs this city data into the system, the server loads it and uses a generative artificial intelligence model to generate an optimal green space design. This design includes:

[1314] Trees: 75

[1315] Shrubs: 150

[1316] Flowers: 250 stems

[1317] Arrangement: 10x10 matrix

[1318] When the user expresses their desire to relax through sensor input or direct input, the emotion engine recognizes this and adjusts the design to include more plants with relaxing effects, such as lavender and jasmine.

[1319] Prompt Sentence Examples

[1320] For example:

[1321] "I would like to design a green space for a city. The population density of the city is 5,000 people / km², the available space is 10,000 m², and the climate is temperate. Users want to relax. Based on these conditions, please propose the optimal green space design."

[1322] In this way, the present invention provides a sustainable green space design in urban areas that contributes to biodiversity and also contributes to improving the welfare of residents.

[1323] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1324] Step 1:

[1325] Initializing the Server

[1326] The server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data, green space design, and user emotion data. It also loads the necessary libraries and generative AI models.

[1327] input:

[1328] Initial setting data

[1329] output:

[1330] A set up GreenAISystem instance

[1331] Specific behavior:

[1332] GreenAISystem = new GreenAISystem(); This code creates an instance.

[1333] Step 2:

[1334] Entering and Loading City Data

[1335] Users input city data (e.g., population density, available space, climate information) through a terminal. The server receives this data, loads it into the system, and verifies that it is in the correct format.

[1336] input:

[1337] Population density, available space, and climate information

[1338] output:

[1339] Loaded city data

[1340] Specific behavior:

[1341] GreenAISystem.load_city_data(5000, 10000, 'Temperate'); This method loads the data.

[1342] Step 3:

[1343] Generate green space designs

[1344] The server calls the generate_green_space_design method to generate an optimal green space design based on city data. It uses a generative artificial intelligence model to calculate the number of trees, shrubs, and flowers, and their placement.

[1345] input:

[1346] Loaded city data

[1347] output:

[1348] Generated green space design data (number of trees, number of shrubs, number of flowers, and their placement)

[1349] Specific behavior:

[1350] GreenAISystem.generate_green_space_design(); This method is called and the AI ​​model performs the calculations.

[1351] Step 4:

[1352] Emotion data input and analysis

[1353] The user inputs their emotion (e.g., "I want to relax") through a device or sensor. The server receives this emotion data and analyzes it using an emotion engine.

[1354] input:

[1355] User emotion data

[1356] output:

[1357] Analyzed emotion data

[1358] Specific behavior:

[1359] GreenAISystem.analyze_emotion('I want to relax'); This method analyzes the sensor data.

[1360] Step 5:

[1361] Coordination of green space design

[1362] The server adjusts the green space design based on the analysis results of the emotion engine, for example by changing the design to include more plants that have a relaxing effect.

[1363] input:

[1364] Analyzed emotion data, generated green space design data

[1365] output:

[1366] Adjusted green space design data

[1367] Specific behavior:

[1368] GreenAISystem.adjust_design_based_on_emotion('I want to relax'); This method adjusts the design based on emotion.

[1369] Step 6:

[1370] Final green space design output

[1371] The server retrieves the final green space design using the get_green_space_design method and sends it to the user's device, where the user can view the design.

[1372] input:

[1373] Adjusted green space design data

[1374] output:

[1375] Final green space design data

[1376] Specific behavior:

[1377] final_design = GreenAISystem.get_green_space_design(); This method retrieves the final design and outputs it to the user.

[1378] These are the specific processing steps of this system, which enables optimal urban green space design that takes into account the diverse conditions of the city and the emotions of users.

[1379] (Application example 2)

[1380] 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."

[1381] There is a need to effectively utilize available space in urban areas as green spaces, contributing to the welfare of residents and improving the urban environment. However, while conventional green space design systems can optimally select and arrange plants based on urban data, they are unable to adjust the plantings based on the user's emotions or specific preferences. To address these issues, the present invention provides a system that realizes green space designs that reflect the user's emotions and visually displays the designs using augmented reality or a three-dimensional model.

[1382] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for designing urban green space using a generative artificial intelligence model, means for calculating optimal plant types and arrangements based on the urban green space design, means for storing the calculated plant types and arrangements, means for adjusting the plant arrangements based on the user's emotional state, means for inputting and analyzing the user's emotions, and means for displaying the design results in augmented reality or a three-dimensional model. This makes it possible to provide customized green space designs using city data and the user's emotional state.

[1383] A "generative artificial intelligence model" is an algorithm or computational model that analyzes the complex data required for designing urban green spaces and generates optimal plant types and placements.

[1384] "Urban green space design" is the planning of plant types and placements to effectively use available space in urban areas as green spaces.

[1385] "Calculation of plant types and placement" refers to calculations based on city data and climate information to ensure that plants selected for urban green space design are optimally placed.

[1386] "Storing the calculated plant types and arrangements" means saving the generated green space design information as digital data.

[1387] "Adjustment based on the user's emotional state" means optimizing the initial green space design based on emotions analyzed from user input and sensor data, and providing a green space design that takes the user's emotional state into account.

[1388] "Emotion input and analysis" refers to acquiring a user's emotional data and analyzing it to identify a specific emotional state.

[1389] "Augmented reality or three-dimensional model display" is a technology that displays the generated green space design in a virtual space using a smartphone or head-mounted display, allowing the user to visually confirm the design.

[1390] "City data" refers to data such as a city's population density, available space, and climate information.

[1391] A "prompt sentence format" is a sentence that is presented to the user in a format that is easy for the user to understand as input to a generative AI model.

[1392] To implement this invention, a system for designing urban green spaces is required. This system includes a server, a user's device (such as a smartphone, smart glasses, or a head-mounted display), a generative artificial intelligence model, and an emotion engine.

[1393] The server first initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data, green space design, and user emotion data. Next, it loads the city data provided by the user into the server. This data includes population density, available space, and weather information.

[1394] The server then calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data. Specifically, it uses a generative artificial intelligence model to calculate the optimal plant types and placement for the city's conditions. The generated design is stored in a property on the server.

[1395] The server also uses an emotion engine that recognizes the user's emotions. The emotion engine analyzes emotions from user input and sensor data and adjusts the green space design based on those emotions, taking into account past user emotion data. For example, if the user wants to relax, the server generates a design that includes many plants with a relaxing effect.

[1396] The final design results can be visually confirmed on the user's device, which has the ability to display the design results in augmented reality (AR) and three-dimensional models (3D models), allowing users to overlay the green space design on the real space.

[1397] For example, if a city's data shows a population density of 5,000 people / km², available space of 10,000 m², and a temperate climate, the generative AI model will provide an optimal green space design based on this data. If a user inputs their emotional state of "I want to relax," the emotion engine will recognize this and generate a green space design that includes many plants with a relaxing effect.

[1398] As a concrete example, the generative AI model is designed by inputting the following prompt sentence:

[1399] "Design an optimal green space for a city with a population density of 5000 people / km², available space of 10000m², and a temperate climate. The user wants to relax."

[1400] This will provide a concrete and practical system that effectively uses available space in urban areas as green space, contributing to the improvement of residents' welfare and the urban environment.

[1401] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1402] Step 1:

[1403] The server initializes the system by creating an instance of the GreenAISystem class and setting up properties to hold city data, green space design, and user emotion data. This completes the necessary initial setup.

[1404] Input: None

[1405] Output: An instance of the GreenAISystem class, city data properties, green space design properties, and emotion data properties.

[1406] Step 2:

[1407] The server loads the city data provided by the user into the server, which includes population density, available space, and climate information, and stores this data in properties of the city data.

[1408] Inputs: population density, available space, climate information

[1409] Output: Properties where city data is stored

[1410] Step 3:

[1411] The server calls the generate_green_space_design method to generate an optimal green space design based on the loaded city data, using a generative artificial intelligence model to calculate plant types and placement.

[1412] Input: City data

[1413] Output: Optimal green space design (plant types and placement)

[1414] Step 4:

[1415] The server saves the optimal green space design in the property, and this design result is used in subsequent processing.

[1416] Input: Optimal green space design

[1417] Output: Saved green space design

[1418] Step 5:

[1419] The server uses an emotion engine to receive the user's emotional state as input, and the emotion engine analyzes the user's emotional data and stores the results in the properties of the emotional data.

[1420] Input: User's emotional state (e.g., "I want to relax")

[1421] Output: Parsed emotion data

[1422] Step 6:

[1423] The server adjusts the green space design based on the emotional data analyzed by the emotion engine. For example, if the user wants to relax, the server changes the design to include more plants that have a relaxing effect.

[1424] Input: Analyzed emotion data, optimal green space design

[1425] Output: Coordinated green space design

[1426] Step 7:

[1427] The device displays the adjusted green space design retrieved from the server in augmented reality (AR) or three-dimensional (3D) models, allowing users to check the design overlaid on the real space.

[1428] Input: Coordinated green space design

[1429] Output: Augmented reality or 3D model display

[1430] Step 8:

[1431] The user visually checks the green space design displayed on the device and, if necessary, sends further adjustment requests and feedback to the server, which then completes the final green space design that reflects the user's specific wishes.

[1432] Input: User feedback, adjustment requests

[1433] Output: Final design reflecting user wishes

[1434] 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.

[1435] 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.

[1436] 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.

[1437] 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.

[1438] 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.

[1439] 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.

[1440] 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).

[1441] 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.

[1442] 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."

[1443] 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.

[1444] 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).

[1445] 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.

[1446] 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.

[1447] 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.

[1448] 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.

[1449] 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.

[1450] 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.

[1451] 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.

[1452] 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.

[1453] 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.

[1454] 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.

[1455] The following is further disclosed regarding the above embodiment.

[1456] (Claim 1)

[1457] A means for designing urban green spaces using a generative artificial intelligence model;

[1458] A means for calculating optimal plant types and arrangements based on the design of the urban green space;

[1459] a means for storing the calculated plant types and arrangements;

[1460] A system including:

[1461] (Claim 2)

[1462] The system of claim 1 , further comprising means for using city population density, available space, and climate information to design the urban green space.

[1463] (Claim 3)

[1464] The system of claim 1 , further comprising: means for outputting the designed and calculated placement information.

[1465] "Example 1"

[1466] (Claim 1)

[1467] A means for initializing the system on the server;

[1468] means for loading user-provided city data onto a server;

[1469] A means to use generative AI models to calculate optimal plant types and placements for urban conditions; and

[1470] a means for storing the calculated plant types and arrangements;

[1471] A system including:

[1472] (Claim 2)

[1473] 10. The system of claim 1, further comprising means for the city data to include population density, available space, and climate information.

[1474] (Claim 3)

[1475] 10. The system of claim 1, further comprising: means for outputting the calculated placement information.

[1476] "Application Example 1"

[1477] (Claim 1)

[1478] A means for designing urban green spaces using a generative artificial intelligence model;

[1479] A means for calculating optimal plant types and arrangements based on the design of the urban green space;

[1480] a means for storing the calculated plant types and arrangements;

[1481] A means for recognizing spatial information inside and outside the store through smart glasses;

[1482] a means for displaying a green space design on the smart glasses based on the recognized spatial information;

[1483] A system including:

[1484] (Claim 2)

[1485] The system of claim 1 , further comprising means for using city population density, available space, and climate information to design the urban green space.

[1486] (Claim 3)

[1487] The system of claim 1 , further comprising: means for outputting the designed and calculated placement information.

[1488] "Example 2: Combining Emotion Engines"

[1489] (Claim 1)

[1490] A means for designing urban green spaces using a generative artificial intelligence model;

[1491] A means for calculating optimal plant types and arrangements based on the design of the urban green space;

[1492] means for analyzing user emotion data to adjust the calculated design;

[1493] a means for storing the calculated plant types and arrangements;

[1494] means for outputting the designed and adjusted placement information;

[1495] A system including:

[1496] (Claim 2)

[1497] 10. The system of claim 1, further comprising means for designing the urban green space using urban population density, available space, and climate information.

[1498] (Claim 3)

[1499] 10. The system of claim 1, further comprising means for adjusting the calculated design based on user emotional data.

[1500] "Application example 2 when combining emotion engines"

[1501] (Claim 1)

[1502] A means for designing urban green spaces using a generative artificial intelligence model;

[1503] A means for calculating optimal plant types and arrangements based on the design of the urban green space;

[1504] a means for storing the calculated plant types and arrangements;

[1505] means for adjusting the placement of the plants based on the emotional state of the user;

[1506] means for inputting and analyzing user emotions;

[1507] means for displaying the design results in an augmented reality or three-dimensional model;

[1508] A system including:

[1509] (Claim 2)

[1510] 10. The system of claim 1, further comprising means for designing urban green space using a city's population density, available space, climate information, and a user's emotional state.

[1511] (Claim 3)

[1512] The system of claim 1 , further comprising: means for outputting the designed and calculated placement information in the form of a prompt sentence. [Explanation of symbols]

[1513] 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 designing urban green spaces using a generative artificial intelligence model; A means for calculating optimal plant types and arrangements based on the design of the urban green space; a means for storing the calculated plant types and arrangements; A system including:

2. The system of claim 1 , further comprising means for using urban population density, available space, and climate information to design the urban green space.

3. The system of claim 1 further comprising means for outputting the designed and calculated placement information.

Images