System

Abstract

To provide a system for making an efficient and effective installation plan of a base station.SOLUTION: The system includes means for a user to enter information about the location, purpose, and conditions for the placement of base stations, means for the terminals to receive and transmit the entered information to a server, means for the server to analyze the received information and generate an optimal base-station placement plan using a generative AI model, and means for the server to return the generated plan to the terminals for display to the user.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] Installing and improving base stations requires advanced technical knowledge, but many on-site personnel lack this knowledge, making it difficult to develop efficient and effective installation plans. Furthermore, selecting installation locations and estimating costs are not straightforward and require a great deal of time and resources. A system that can solve this problem and develop optimal plans in a short period of time is needed. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for a user to input information regarding the installation location, purpose, and conditions of a base station, a means for a terminal to receive the input information and transmit it to a server, a means for the server to analyze the received information and generate an optimal base station installation plan using a generative AI model, and a means for the server to return the generated plan to the terminal and display it to the user. Furthermore, the server links the received data with a geographic information database and a population database to obtain additional data and perform analysis. Additionally, the server calculates a cost estimate and provides it to the user along with the installation plan, allowing even those lacking technical knowledge to efficiently and effectively create an optimal base station installation plan.

[0006] A "user" is an entity that inputs information regarding the installation and improvement of base stations and receives optimal installation plans and estimates.

[0007] A "terminal" is a device that receives information entered by a user and transmits it to a server.

[0008] The "server" is a computer system that analyzes information received from the terminal, generates an optimal base station installation plan using a generative AI model, and returns it to the terminal.

[0009] A "generative AI model" is an artificial intelligence model that generates optimal installation plans based on technical knowledge about base station installation.

[0010] A "base station" is a piece of equipment that constitutes a wireless communication network and is used to communicate between mobile terminals and other base stations.

[0011] "Location" refers to the geographic location for physically installing a base station.

[0012] "Purpose" refers to the specific results or uses that you want to achieve by installing or improving a base station.

[0013] "Conditions" refers to the constraints and requirements that must be considered regarding the installation or improvement of a base station.

[0014] "Analysis" is the process of using a generative AI model to derive an optimal installation plan based on information received from the user and additional data.

[0015] A "geographic information database" is a database that stores geographical information and is used to analyze location information.

[0016] A "population database" is a database that stores information on population density and demographics, and is used to select installation locations.

[0017] A "cost estimate" is the result of calculating the costs involved in installing and operating a base station.

[0018] An "installation plan" is a detailed plan for installing a base station in the optimal location. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] The present invention relates to a system that proposes optimal plans for installing and improving base stations. This system uses a generative AI model based on user input information to generate an optimal installation plan and presents it to the user.

[0041] A natural language description of the program's processing

[0042] 1. User Input

[0043] Through a web or mobile application, users input information about the base station installation, such as the city name (e.g., "Tokyo"), purpose (e.g., "expand communication coverage"), and conditions (e.g., "cost constraints are not strict").

[0044] 2. Data transmission

[0045] When the user finishes inputting information, the terminal transmits the input information to the server, including the city name, purpose, and conditions.

[0046] 3. Receiving data and preparing for analysis

[0047] The server receives the data sent by the device and formats it for analysis, possibly retrieving additional data from geographic and population databases as needed.

[0048] 4. Analysis using AI models

[0049] The server inputs the formatted data into a generative AI model to generate an optimal base station installation plan, including installation locations, technical requirements, estimated costs, etc.

[0050] 5. Calculating cost estimates

[0051] Based on the generated installation plan, the server calculates a cost estimate, which includes the installation costs, operating costs, and required hardware and software costs for each base station.

[0052] 6. Organizing and displaying results

[0053] The server formats the generated installation plan and cost estimate into a single response package and sends it back to the terminal, where the user can view this information.

[0054] Specific examples

[0055] As an example, consider the installation of a new 5G base station in Tokyo.

[0056] 1. User Input

[0057] The user inputs "Tokyo," "expanded communication coverage area," and "cost constraints are not strict."

[0058] 2. Data transmission

[0059] The terminal transmits the information to the server.

[0060] 3. Receiving data and preparing for analysis

[0061] The server receives the data and retrieves detailed geographic information and population data for Tokyo from geographic information and population databases.

[0062] 4. Analysis using AI models

[0063] The server uses the generated AI model to generate the optimal installation location (for example, the Shinjuku area) and the technical requirements for each base station.

[0064] 5. Calculating cost estimates

[0065] The server calculates installation costs (e.g., the cost of installing three 5G base stations in the Shinjuku area), operating costs, and hardware and software costs.

[0066] 6. Organizing and displaying results

[0067] The server formats the generated installation plan and cost estimate and returns it to the device, where the user can view the specific plan and cost estimate for installing three 5G base stations in the Shinjuku area.

[0068] As described above, the present invention is a system that allows users to easily obtain optimal plans for installing and improving base stations. By using a generative AI model, users can efficiently create optimal installation plans even without advanced technical knowledge.

[0069] The processing flow will be explained below.

[0070] Step 1:

[0071] The user launches a web or mobile application and enters the information required for base station installation, such as the desired installation location (e.g., "Tokyo"), the purpose of installation (e.g., "expanding communication coverage"), and conditions (e.g., "cost constraints are not strict").

[0072] Step 2:

[0073] The terminal receives the information entered by the user, including the city name, purpose, and conditions.

[0074] Step 3:

[0075] The terminal sends the received input information to the server, where the data is converted into a predetermined data format (e.g., JSON format).

[0076] Step 4:

[0077] The server receives the data sent from the device and formats it into the correct format, or performs any necessary conversions if the data format is different.

[0078] Step 5:

[0079] The server uses the formatted data to retrieve additional information from geographic and population databases, such as detailed geographic and demographic information for Tokyo.

[0080] Step 6:

[0081] The server inputs the received data and additional data into a generative AI model for analysis, which generates an optimal base station installation plan based on past data and current technical parameters.

[0082] Step 7:

[0083] The generative AI model generates analytical results and creates an optimal installation plan, including suitable installation locations, the number of base stations required, and technical specifications.

[0084] Step 8:

[0085] The server calculates a cost estimate based on the generated installation plan, including equipment costs, infrastructure costs, and operation and maintenance costs.

[0086] Step 9:

[0087] The server assembles the installation plan and cost estimate into a single response package that includes plan details, maps, and cost analysis.

[0088] Step 10:

[0089] The server returns a response package to the terminal, which receives the returned data and displays it to the user.

[0090] Step 11:

[0091] The user can use the terminal to view the installation plan and cost estimate displayed, and if necessary, enter new specifications to refine the plan and submit it again.

[0092] Example 1

[0093] 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."

[0094] Conventional base station installation plan generation systems often require a lot of time and effort to analyze user-entered information and create optimal installation plans based on that information. Furthermore, creating an installation plan can be difficult if the user does not have advanced technical knowledge. Furthermore, the process of integrating multiple data, such as calculating cost estimates and obtaining geographic information, is complicated, making it difficult to proceed efficiently.

[0095] 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.

[0096] In this invention, the server includes a means for formatting the received information into an analytical format, a means for acquiring additional data from a geographic information database and a population database, and a means for inputting the analytical data into a generative AI model to generate an optimal base station installation plan. This allows the base station installation plan to be generated quickly and efficiently based on the user's input information, enabling the user to obtain the optimal installation plan without advanced technical knowledge. Furthermore, cost estimates are calculated automatically, enhancing user convenience.

[0097] "User" refers to an individual or organization that uses the system to input base station installation information.

[0098] A "base station" is a type of wireless communication device that relays communications within a communication network.

[0099] "Installation location" refers to a specific geographic location where a base station is physically located.

[0100] "Purpose" refers to the specific goal or reason for installing a base station.

[0101] "Conditions" refers to restrictions and requirements related to the installation of a base station.

[0102] "Terminal" refers to a device used by a user to communicate input information to a system.

[0103] "Server" refers to the computer system that analyzes the received information and generates an optimal base station installation plan using a generative AI model.

[0104] A "generative AI model" refers to an artificial intelligence algorithm that learns from large amounts of data to perform specific tasks.

[0105] "Geographic information database" refers to a database containing geographic location information.

[0106] "Population Database" refers to a database containing demographic information for a particular geographic area.

[0107] "Analysis format" refers to the data format that converts received information into a format suitable for the generative AI model.

[0108] "Installation Plan" refers to a plan proposed by a generative AI model showing the specific base station placement and related technical requirements.

[0109] A "cost estimate" is a calculation of the costs required to install and operate a base station.

[0110] "Response Package" refers to the data returned to the User that includes an installation plan and cost estimate.

[0111] The present invention is a system that generates an optimal base station installation plan using a generative AI model, where a user inputs the installation location, purpose, and conditions of the base station. To implement this system, a web or mobile application is required, and these applications have the function of sending the user's input information to a server.

[0112] First, the user uses the application's input screen to input the city name, purpose of installation, and conditions. For example, they can enter information such as "Tokyo," "expanding communication coverage area," and "cost constraints are not strict." This input information is sent by the device to the server as a JSON-formatted data packet.

[0113] The server formats the received data for analysis. Specifically, it parses the JSON data and sends queries to geographic information and population databases to obtain the necessary geographic and population data. In this process, the server collects detailed geographic and population data related to the city name "Tokyo."

[0114] Next, the server generates a prompt to input the data into a generative AI model. For example, it generates a sentence such as, "Please propose the optimal base station installation plan to expand communication coverage in Tokyo. Cost constraints are not strict." The data for analysis is input into a generative AI model (e.g., GPT-4) along with this prompt.

[0115] The generative AI model proposes an optimal base station installation plan, which includes the installation location (for example, the Shinjuku area), technical requirements, estimated costs, etc. Based on the generated plan, the server calculates installation costs, operating costs, and the costs of the necessary hardware and software.

[0116] This information is formatted into a single response package and sent back from the server to the terminal. The user can then view the generated specific installation plan and cost estimate through the terminal. This system enables users without advanced technical knowledge to efficiently obtain the optimal base station installation plan.

[0117] This concludes the implementation of the present invention. As an example, consider the case of installing a new 5G base station in Tokyo. The user inputs "Tokyo," "expanded coverage area," and "low cost constraints." Based on this information, the server formats the data and collects any additional data needed. It then uses a generative AI model to generate an optimal installation plan and calculate a cost estimate. Finally, the user can view a specific plan and cost estimate for installing three 5G base stations in the Shinjuku area.

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1: User enters information

[0120] Specific behavior:

[0121] The user opens an input screen for a web or mobile application.

[0122] The user enters the city name "Tokyo," the purpose "expanding communication coverage area," and the conditions "cost constraints are not strict" into the application's input form.

[0123] The entered data is organized in JSON format.

[0124] input:

[0125] City name: "Tokyo"

[0126] Objective: "Expanding communication coverage"

[0127] Condition: "Cost constraints are not strict"

[0128] output:

[0129] JSON formatted data packet

[0130] Step 2: Sending data

[0131] Specific behavior:

[0132] The user clicks the "Submit" button.

[0133] The terminal transmits the input information to the server.

[0134] The transmitted data packets are checked for correct format.

[0135] input:

[0136] JSON formatted data packet

[0137] output:

[0138] Data sent to the server

[0139] Step 3: Receive data and prepare for analysis

[0140] Specific behavior:

[0141] The server parses the JSON data received from the terminal.

[0142] The data is formatted for analysis and queries are sent to geographic and population databases to obtain additional data.

[0143] For example, the server obtains geographic information and population data related to the city name "Tokyo."

[0144] input:

[0145] JSON data received from the terminal

[0146] output:

[0147] Formatted analytical data and additional geographic and demographic data

[0148] Step 4: Generate prompts and input them to the AI ​​model

[0149] Specific behavior:

[0150] The server generates a prompt for the generative AI model, such as "Please propose the optimal base station installation plan to expand the communication coverage area in Tokyo. Cost constraints are not strict."

[0151] The server inputs the prompt sentence and analysis data into a generative AI model (e.g., GPT-4).

[0152] input:

[0153] Formatted data for analysis

[0154] Generated prompt statement

[0155] output:

[0156] Input data for generative AI models

[0157] Step 5: Analyze and generate installation plans using generative AI models

[0158] Specific behavior:

[0159] The server receives the optimal base station installation plan proposed by the generative AI model.

[0160] For example, the generated installation plan includes the installation location (Shinjuku area), technical requirements, and estimated costs.

[0161] input:

[0162] Input data for generative AI models

[0163] output:

[0164] Proposed installation plan (location, technical requirements, estimated costs)

[0165] Step 6: Calculate cost estimates

[0166] Specific behavior:

[0167] The server calculates a detailed cost estimate based on the generated installation plan.

[0168] For example, we will calculate the installation costs, operating costs, and necessary hardware and software costs for installing three 5G base stations in the Shinjuku area.

[0169] input:

[0170] Proposed installation plan

[0171] output:

[0172] Detailed cost estimates

[0173] Step 7: Organizing and displaying results

[0174] Specific behavior:

[0175] The server formats the installation plan and cost estimate into a single response package.

[0176] The server returns the response package to the terminal.

[0177] The user can view the generated specific installation plan and cost estimate through the terminal.

[0178] input:

[0179] Detailed cost estimates

[0180] Proposed installation plan

[0181] output:

[0182] Response package (installation plan and cost estimate) returned to the user

[0183] (Application example 1)

[0184] 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."

[0185] In autonomous vehicles, communication interruptions have a significant impact on safety and efficiency. Furthermore, conventional base station installation planning systems face the problem of difficulty in optimizing communication areas taking real-time location information into account. This leads to frequent communication interruptions and information loss due to interruptions as the vehicle moves, creating a need for a system that ensures stable communication and provides an optimal communication environment even while the vehicle is moving.

[0186] 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.

[0187] In this invention, the server includes: means for a user to input information regarding the installation location, purpose, and conditions of a base station; means for a terminal to receive the input information and send it to the server; means for the server to analyze the received information and generate an optimal base station installation plan using a generative AI model; means for the server to return the generated plan to the terminal and display it to the user; means for acquiring vehicle location information in real time and determining the optimal communication area; and means for providing base station installation information based on the optimal communication area. This makes it possible to minimize communication interruptions even while an autonomous vehicle is moving, and to provide a stable communication environment.

[0188] definition statement

[0189] "User" refers to a person who inputs information about the location, purpose, and conditions of a base station.

[0190] "Terminal" refers to a device for receiving information entered by a user and transmitting it to a server.

[0191] "Server" refers to a device or system that analyzes the received information and generates an optimal base station installation plan using a generative AI model.

[0192] "Generative AI model" refers to an artificial intelligence model used to generate optimal base station installation plans based on user input information.

[0193] "Base station installation plan" refers to a plan that includes the installation location and technical requirements of base stations with the aim of optimizing communication areas.

[0194] "Real-time acquisition" refers to the immediate collection of current vehicle location information.

[0195] An "optimal communication area" refers to an area that minimizes communication interruptions and provides a stable communication environment.

[0196] "Installation information" refers to detailed information including the specific installation location and technical requirements of the base station.

[0197] MODE FOR CARRYING OUT THE INVENTION

[0198] The present invention is a system for minimizing communication interruptions caused by the movement of an autonomous vehicle and providing a stable communication environment. This system includes: means for a user to input information regarding the installation location, purpose, and conditions of a base station; means for a terminal to receive the input information and send it to a server; means for the server to analyze the received information and generate an optimal base station installation plan using a generative AI model; means for the server to return the generated plan to the terminal and display it to the user; means for acquiring vehicle location information in real time and determining the optimal communication area; and means for providing base station installation information based on the optimal communication area.

[0199] Specific Embodiments

[0200] 1. User input and its reception:

[0201] In this system, users input information about the location, purpose, and conditions of base stations through a dedicated application. Specifically, the driver of the autonomous vehicle uses a smartphone to input the desired location for base stations and requirements for maintaining stable communications.

[0202] 2. Server analyzes data and uses generated AI models:

[0203] The terminal sends the input information to a server, which then analyzes the received information. At this time, a generative AI model is used to generate an optimal base station installation plan. The generative AI model used includes the GPT model. This allows the optimal communication area to be determined based on the route the vehicle will travel.

[0204] 3. Real-time location information and optimal coverage:

[0205] The vehicle's GPS device and communication module acquire the vehicle's location information in real time. Based on this location information, the server determines the optimal communication area and provides that information to the vehicle's terminal. This minimizes communication interruptions even when the vehicle is moving.

[0206] 4. Providing optimal base station installation plans:

[0207] The system generates an optimal base station installation plan and provides it to the user with a cost estimate, including the base station installation location, technical requirements, estimated costs, etc. For example, based on the conditions "Tokyo," "expanded communication coverage area," and "no strict cost constraints," the system presents a plan for installing three 5G base stations in the Shinjuku area and displays the estimated cost.

[0208] Hardware and software used

[0209] Hardware: GPS receiver installed in the autonomous vehicle, smartphone

[0210] Software: Python as the execution environment, the requests library for receiving and sending data, the geopy library for geographic information processing, and a generative AI model (e.g., GPT model).

[0211] Examples of prompt statements

[0212] Based on the current location (35.6895, 139.6917) and the destination location (35.6895, 139.7000), propose an optimal base station installation plan. Communication stability is the most important factor, and cost constraints are not taken into consideration. The output should include the installation location and distance of each base station.

[0213] As described above, the present invention is effective as a means for ensuring the stability of communications for autonomous vehicles, and provides a system that optimizes the communications environment by providing users with base station installation plans.

[0214] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0215] Program processing flow

[0216] Step 1:

[0217] Users input information about the location, purpose, and conditions of the base station installation through a dedicated application. This input data includes the desired installation location and requirements for maintaining communication stability. The input data is expressed in text and numerical format. For example, information such as "Tokyo," "expanding communication coverage area," and "cost constraints are not strict" can be entered.

[0218] Step 2:

[0219] The terminal receives the information entered by the user and sends it to the server. The input data is formatted and sent over the network to the server. At this stage, it is confirmed that the input information reaches the server properly.

[0220] Step 3:

[0221] The server receives the received information and retrieves additional data as needed. This additional data is retrieved from geographic and population databases and combined with the input data. The input data and additional data are then used to format the data for analysis. Specifically, geographic location data and population density information are collected.

[0222] Step 4:

[0223] The server inputs the formatted data into a generative AI model to generate an optimal base station installation plan. The generative AI model (e.g., a GPT model) analyzes the input data and generates a detailed plan including installation locations, technical requirements, estimated costs, etc. In this process, the AI ​​model uses historical data and predictive algorithms to generate the most efficient plan.

[0224] Step 5:

[0225] The server calculates a cost estimate based on the generated installation plan, including installation costs, operating costs, and the costs of required hardware and software for each base station, using a predefined cost database for this calculation.

[0226] Step 6:

[0227] The server then formats the installation plan and cost estimate into a single response package and sends it back to the terminal, where the information is processed and displayed to the user, showing the installation location, estimated costs, technical requirements, and other information on the user's screen.

[0228] Step 7:

[0229] Vehicle location information is acquired in real time and sent to the server. The vehicle's current location is periodically acquired using the GPS device and communication module installed in the vehicle. Based on this location information, the server determines the optimal communication area.

[0230] Step 8:

[0231] The server uses the vehicle's location information to provide base station installation information based on the optimal communication area. The installation plan generated by the generative AI model is linked to real-time location information and sends instructions to the device to maintain the optimal communication area. The communication area is dynamically adjusted based on the input current location information, minimizing communication interruptions.

[0232] The above steps will enable the realization of a system that provides optimal base station installation plans while ensuring stable communications for autonomous vehicles, significantly improving the safety and efficiency of autonomous vehicles.

[0233] 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.

[0234] This invention relates to a system that proposes optimal plans for installing and improving base stations, and in particular, a system that provides more advanced services by combining an emotion engine that recognizes user emotions. This system uses a generative AI model based on the user's input information and emotional state to generate an optimal installation plan and present it to the user.

[0235] A natural language description of the program's processing

[0236] 1. User Input

[0237] Through a web or mobile application, users input information about the installation of base stations, such as the desired location (e.g., "Tokyo"), the purpose of the installation (e.g., "expanding communication coverage"), and conditions (e.g., "cost constraints are not strict").

[0238] 2. User Emotion Recognition

[0239] As users input information, the emotion engine detects emotions from their facial expressions and voice in real time, using specific algorithms to analyze their emotional states, such as joy, surprise, sadness, and anger.

[0240] 3. Data transmission

[0241] When the user finishes inputting information, the device sends the input information and the emotion data detected by the emotion engine to the server, including the city name, purpose, condition, and emotional state.

[0242] 4. Receiving data and preparing for analysis

[0243] The server receives the data sent by the device and formats it correctly, converting it if necessary if the data format is different, and retrieving additional information from geographic and population databases.

[0244] 5. Analysis using AI models

[0245] The server then analyzes the formatted data and the added emotion data using a generative AI model, which generates an optimal base station installation plan based on past data and current technical parameters.

[0246] 6. Generating the optimal plan

[0247] Based on the analysis results, the generative AI model creates an optimal installation plan, including suitable installation locations, the number of base stations required, technical specifications, cost estimates, etc. It also takes into account the user's emotional data to generate explanations and additional suggestions for the user.

[0248] 7. Calculating cost estimates

[0249] The server calculates a cost estimate based on the installation plan, which includes equipment costs, infrastructure costs, and operation and maintenance costs.

[0250] 8. Organizing and displaying results

[0251] The server assembles the installation plan, cost estimate, and additional suggestions based on the user's emotional state into a response package that includes details of the plan, a map, a cost analysis, and actions based on the emotional state.

[0252] 9. Return of results

[0253] The server returns a response package to the terminal, which receives the returned data and displays it to the user.

[0254] 10. User Confirmation

[0255] The user can use the terminal to view the installation plan, cost estimate, and additional proposals displayed, and can enter new criteria to refine the plan as needed and resubmit it.

[0256] Specific examples

[0257] As an example, consider the installation of a new 5G base station in Tokyo.

[0258] 1. User Input and Emotion Recognition

[0259] As the user types in "Tokyo," "expanded communication coverage," and "cost constraints are not strict," the emotion engine detects feelings of hope and relief from the user's facial expression.

[0260] 2. Data transmission

[0261] The terminal transmits the information and emotion data to the server.

[0262] 3. Receiving data and preparing for sentiment analysis

[0263] The server receives the data and retrieves detailed information about Tokyo from geographic information and population databases.

[0264] 4. Analysis using AI models

[0265] The server uses the generated AI model to generate an explanation based on the optimal installation location (for example, the Shinjuku area), the technical requirements of each base station, and user sentiment.

[0266] 5. Calculating cost estimates

[0267] The server calculates the installation costs, operating costs, hardware and software costs.

[0268] 6. Organizing and displaying results

[0269] The server generates an installation plan, a cost estimate, and additional emotion-based explanations, which are then sent back to the device.

[0270] 7. User Confirmation

[0271] The user views and confirms on the device the specific plan, cost estimate, and additional instructions for installing three 5G base stations in the Shinjuku area.

[0272] As described above, the present invention provides a system that allows users to easily obtain optimal plans for installing and improving base stations. In particular, by using an emotion engine, it is possible to deliver proposals optimized for the user's emotional state, improving the user experience.

[0273] The processing flow will be explained below.

[0274] Step 1:

[0275] The user launches a web or mobile application and inputs the necessary information for base station installation, such as "Tokyo," "expanded communication coverage area," and "cost constraints are not strict."

[0276] Step 2:

[0277] As users input information, the device's built-in emotion engine detects emotions in real time from the user's facial expressions and voice, using algorithms to analyze emotional states such as joy, surprise, sadness, and anger.

[0278] Step 3:

[0279] When the user finishes inputting, the device sends the input information and the emotion data detected by the emotion engine to the server. The data is formatted in a specific data format (e.g., JSON format).

[0280] Step 4:

[0281] The server receives the data sent from the device, including the city name, purpose, condition, and emotional state.

[0282] Step 5:

[0283] The server formats the received data for analysis, and performs any necessary conversions if the data format is different.

[0284] Step 6:

[0285] The server retrieves detailed geographic and population data for Tokyo from geographic and population databases, and in the process collects additional data using the necessary APIs.

[0286] Step 7:

[0287] The server inputs the formatted data and the added emotion data into a generative AI model for analysis. The generative AI model generates an optimal base station installation plan based on past data and current technical data.

[0288] Step 8:

[0289] Based on the analysis results, the generative AI model creates an optimal installation plan, including suitable installation locations, the number of base stations required, technical specifications, and estimated costs.

[0290] Step 9:

[0291] The server calculates a cost estimate based on the generated installation plan, including equipment costs, infrastructure costs, and operation and maintenance costs.

[0292] Step 10:

[0293] The server assembles the installation plan, cost estimate, and additional instructions and suggestions based on the user's emotional state into a response package that includes details of the plan, a map, a cost analysis, and actions based on the emotional state.

[0294] Step 11:

[0295] The server returns a response package to the terminal, which receives the returned data and displays it to the user.

[0296] Step 12:

[0297] The user can use the terminal to view the displayed installation plan, cost estimate, and additional instructions and suggestions, and can also enter new requirements to refine the plan if necessary and resubmit it.

[0298] Example 2

[0299] 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."

[0300] Conventional base station installation planning systems generate optimal installation plans based on user input information, but they are unable to consider the user's feelings and preferences, resulting in a lack of improvement in the user experience. Furthermore, simply collecting and analyzing information often does not allow for flexible adaptation to diverse environments and conditions. This can result in reduced installation plan accuracy and reduced user satisfaction.

[0301] 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.

[0302] In this invention, the server includes means for collecting emotional data along with input information using an emotion engine that recognizes the user's emotional state, means for analyzing the received information and emotional data and generating an optimal base station installation plan using a generative AI model, and means for acquiring and analyzing additional data by linking the received data with a geographic information database and a population database. This allows the server to generate an optimal installation plan that takes the user's emotions into consideration and is also able to flexibly respond to a variety of environments and conditions.

[0303] "User" refers to a person who inputs information about the location, purpose, and conditions of a base station.

[0304] "Emotion engine" refers to software or hardware technology for detecting and analyzing emotions from a user's facial expressions and voice in real time.

[0305] "Terminal" refers to an electronic device that runs web applications and mobile applications used by a user.

[0306] "Server" refers to a computer system for receiving and analyzing information and emotion data sent by a user.

[0307] "Generative AI model" refers to an artificial intelligence algorithm that generates optimal base station installation plans based on past data and technical parameters.

[0308] "Geographic information database" refers to a database that stores and provides geographic information about a specific area.

[0309] "Population Database" means a database that stores and provides demographic information for a particular geographic area.

[0310] "Installation Plan" means a plan for installing Base Stations that includes the optimum locations, the number of Base Stations required, technical specifications, and cost estimates.

[0311] "Cost estimate" refers to the total amount calculated based on equipment costs, infrastructure costs, operation and maintenance costs, etc. for installing a base station.

[0312] A "response package" refers to a data package that includes an optimal installation plan, cost estimates, and additional proposals based on sentiment data.

[0313] The present invention relates to a system that proposes an optimal plan for base station installation, incorporating an emotion engine that recognizes user emotions. This system utilizes a generative AI model to generate an optimal installation plan based on information such as installation location, purpose, and conditions entered by the user, as well as the user's emotional state, and presents the plan to the user. Specific embodiments of this system are described below.

[0314] First, the user uses a web or mobile application to input the necessary information regarding the installation of the base station. Specifically, the user inputs the desired installation location (e.g., "Tokyo"), the purpose of the installation (e.g., "expanding communication coverage"), and conditions (e.g., "cost constraints are not strict"), etc. During this process, an emotion engine built into the application analyzes the user's facial expressions and voice in real time to detect their emotional state. The emotion engine uses facial recognition and voice analysis technology to determine emotions such as joy or anxiety expressed by the user's facial expression.

[0315] When the user finishes entering information, the device sends the information entered by the user and the emotion data detected by the emotion engine to the server. The server receives the received information and emotion data and performs the necessary formatting and analysis. Specifically, the server unifies data formats and appropriately converts incomplete or data with different formats. It also obtains additional information from geographic information databases and population databases to prepare for comprehensive data analysis.

[0316] The server then uses a generative AI model to generate an optimal base station installation plan based on the formatted input information and additional emotional data. The generative AI model analyzes past installation data and current technical parameters to propose an installation plan optimized for the user's needs and emotional state. This plan includes the optimal installation location, the required number of base stations, technical specifications, and cost estimates. It also generates explanations and additional suggestions that take the user's emotional state into consideration.

[0317] The generated installation plan is sent back from the server to the device and displayed to the user. The device displays the received data in an easy-to-read format, allowing the user to review the installation plan, cost estimates, and additional proposals. If necessary, the user can adjust the plan by entering new conditions and submitting it again.

[0318] As a concrete example, consider the installation of a new 5G base station in Tokyo. The user inputs "Tokyo," "expanded communication coverage," and "cost constraints are not strict," and the emotion engine detects the emotion of joy from the user's facial expression while they are typing. The device then sends this data to the server, which uses a generative AI model to generate an optimal installation plan. For example, the server might suggest the Shinjuku area as the optimal installation location and build a plan that includes the required number of base stations, technical specifications, cost estimates, and more. Additionally, additional explanations are generated to provide a sense of security based on the user's emotional state. This information is then sent back to the device for the user to confirm.

[0319] An example of a prompt is as follows:

[0320] "I would like to install a new 5G base station in Tokyo. The goal is to expand communication coverage, and cost constraints are not strict." Also, if the user smiles while typing, the emotion engine will detect hopeful emotions.

[0321] The above is a specific embodiment for carrying out the present invention. This system can generate an optimal base station installation plan that takes into account the emotional state of the user, thereby improving the user experience.

[0322] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0323] Step 1:

[0324] The user launches a web or mobile application and inputs information about the base station installation, such as the desired installation location (e.g., "Tokyo"), the purpose of the installation (e.g., "expanding communication coverage"), and conditions (e.g., "cost constraints are not strict"). The input data is temporarily stored within the application.

[0325] Input: desired installation location, purpose, conditions

[0326] Output: Save input information

[0327] Step 2:

[0328] As the user enters information, the emotion engine analyzes the user's facial expressions and voice in real time to detect their emotional state. The emotion engine uses facial recognition and voice analysis technologies to determine emotions such as joy or anxiety from the user's facial expressions and voice. This generates emotion data based on the user's input behavior.

[0329] Input: User's facial expression, voice

[0330] Output: Detected emotion data

[0331] Step 3:

[0332] Once the user has finished inputting all the information, the terminal transmits the input information and the emotion data generated by the emotion engine to the server, where the data is packaged in an appropriate format.

[0333] Input: Input information, emotion data

[0334] Output: Send data to the server

[0335] Step 4:

[0336] The server receives the data sent from the devices and performs the necessary formatting and analysis, standardizing the data format and converting incomplete or differently formatted data, and obtaining additional information from geographic and population databases to prepare for comprehensive data analysis.

[0337] Input: Send data

[0338] Output: formatted data, additional data

[0339] Step 5:

[0340] The server generates an optimal base station installation plan using a generative AI model based on the formatted input information and additional emotional data. The generative AI model analyzes past installation data and current technical parameters to generate an installation plan optimized for the user's needs and emotional state.

[0341] Input: Formatted data, additional data

[0342] Output: Optimal installation plan

[0343] Step 6:

[0344] The generative AI model generates an installation plan based on the analysis, including the optimal installation location, the number of base stations required, technical specifications, and cost estimates. It also adds explanations and additional suggestions that take into account the user's emotional state. The plan is then stored on a server.

[0345] Input: Optimal installation plan

[0346] Output: Complete installation plan package

[0347] Step 7:

[0348] The server returns the completed installation plan package to the terminal.

[0349] Input: Completed installation plan package

[0350] Output: Sending data to the terminal

[0351] Step 8:

[0352] The terminal displays the received installation plan, cost estimate, and additional suggestions in an easy-to-view format for the user.

[0353] Input: Data from the server

[0354] Output: What is displayed to the user

[0355] Step 9:

[0356] The user can review the installation plan, cost estimate, and additional proposals provided, enter new requirements if necessary, and submit again, starting from step 1.

[0357] Input: Presented data, additional user input

[0358] Output: Re-adjusted installation plan

[0359] (Application example 2)

[0360] 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."

[0361] Conventional base station installation plans mainly analyze and propose plans based on user-entered information about installation locations and conditions. However, because these methods do not take into account the user's emotional state, it is difficult to improve the sense of security and satisfaction felt by the user. Furthermore, in industrial settings such as factories, the proposed optimization plans do not reflect the stress and fatigue of workers because they do not take their emotional state into account, which can lead to reduced production efficiency. There is a need for a new system that can solve these issues and improve the user experience.

[0362] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0363] In this invention, the server includes: means for a user to input information regarding the installation location, purpose, and conditions of a base station; means for a terminal to receive the input information and transmit it to the server; means for the server to analyze the received information and emotion data detected by an emotion engine and generate an optimal base station installation plan using a generative AI model; and means for returning the generated plan and cost estimate to the terminal and displaying it to the user. This makes it possible to generate an optimal installation plan that takes the user's emotional state into consideration, thereby improving security and satisfaction as well as production efficiency.

[0364] "User" means any person or entity that uses the System and inputs information.

[0365] "Base station location" means the specific location of new wireless communication equipment to provide communications.

[0366] "Purpose" refers to the specific intention or goal when installing a base station.

[0367] "Conditions" means the constraints, desires and requirements that must be taken into account when installing a base station.

[0368] "Terminal" refers to an electronic device used by a user to input information and display received installation plans.

[0369] "Input information" refers to data that a user provides to the system through a terminal, such as the installation location and conditions.

[0370] "Server" refers to the central processing unit that receives input data, analyzes it, and generates plans using generative AI models.

[0371] An "emotion engine" refers to an analysis system that has an algorithm that detects the emotional state of a user from their facial expressions and voice.

[0372] "Emotion Data" refers to data regarding a user's emotional state as detected by an emotion engine.

[0373] "Generative AI model" refers to an artificial intelligence model that generates optimal installation plans based on past data and current technical parameters.

[0374] "Installation Plan" refers to a plan for optimal base station installation methods and placement created by a generative AI model.

[0375] "Cost estimate" refers to the calculation of the costs involved in installing and maintaining a base station.

[0376] "Display" refers to the presentation of information to inform the user of the generated installation plan and cost estimate.

[0377] This invention relates to a system that proposes optimal plans for base station installation and improvement, and is particularly applicable to the optimization of factory robot production lines. This system uses a generative AI model to generate an optimal installation plan based on the user's input information and emotional state, and presents it to the user.

[0378] Hardware and software used

[0379] Hardware:

[0380] Head-mounted display (HMD) or smart glasses: collects and displays worker input and emotional state.

[0381] Camera and microphone style emotion recognition sensor: Detects the user's emotional state in real time from their facial expressions and voice.

[0382] software:

[0383] Emotion engine: An analysis system equipped with specific algorithms to analyze user emotions.

[0384] Generative AI model: An artificial intelligence model for generating optimal installation plans.

[0385] Geographic and population databases: Databases for capturing detailed information about installation locations.

[0386] Data processing and calculation

[0387] User input:

[0388] Users access the system via a head-mounted display (HMD) or smart glasses and input information about the base station's location, purpose, and conditions. Factory workers also input information about their current work and environment into an operating terminal.

[0389] Emotion recognition:

[0390] The emotion engine uses a camera and microphone to analyze the user's facial expressions and voice in real time during the input process to detect their emotional state, such as "happiness," "surprise," "sadness," "anger," and "relief."

[0391] Data transmission and analysis:

[0392] The device sends the input information and emotion data to the server, which formats the received data into the correct format, performs any necessary conversions, and retrieves additional information from geographic information and population databases to prepare for analysis.

[0393] Generative AI models analyze and plan:

[0394] The server analyzes the formatted data and the added emotion data using a generative AI model, which generates an optimal base station installation plan or production line layout plan based on past data and current technical parameters.

[0395] Viewing Results:

[0396] The server generates an installation plan, a cost estimate, and additional suggestions based on the user's emotional state, and sends it back to the device in a single response package, which is displayed to the user via a head-mounted display or smart glasses.

[0397] Specific examples

[0398] As an example, consider a production line optimization system for a factory that is introducing new machinery to Line C. A factory worker inputs "Introducing new equipment to Line C," and the emotion engine detects "joy" during the process. The generative AI model uses the detected emotion and input data to generate an optimal adjustment plan for the layout and operating hours of Line C and its surrounding areas, and suggests adding appropriate breaks. It also displays a cost estimate of 1,200,000 yen.

[0399] Example prompts to input to a generative AI model:

[0400] User Input: "We're going to install a new line machine."

[0401] Emotion data: "Joy"

[0402] Application example: "Planning the introduction of a new machine on Line C"

[0403] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0404] Step 1:

[0405] The user inputs information about the base station installation location, purpose, and conditions using a head-mounted display (HMD) or smart glasses. At this time, the user inputs specific information such as the "desired installation location," "purpose of installation," and "conditions" into the operating terminal. In the case of factory workers, they also input information about the "work content" and "environment."

[0406] Input: desired installation location, purpose of installation, conditions, work content, environment

[0407] Output: User input information

[0408] Step 2:

[0409] The emotion engine uses a camera and microphone to analyze the user's facial expressions and voice in real time to detect their emotional state, which is classified into categories such as "happiness," "surprise," "sadness," "anger," and "relief."

[0410] Input: User's facial expression, voice

[0411] Output: Emotion data (happiness, surprise, sadness, anger, relief, etc.)

[0412] Step 3:

[0413] The terminal transmits the user's input information and emotion data to the server, which converts the data into an appropriate format and transmits it.

[0414] Input: User input information, emotion data

[0415] Output: Sending formatted data

[0416] Step 4:

[0417] The server formats the received data and retrieves additional information from geographic and population databases. The formatted data and additional information are used to prepare for analysis.

[0418] Input: Received data (user input information, emotion data)

[0419] Output: Analysis-ready data

[0420] Step 5:

[0421] The server analyzes the incoming data and sentiment data using a generative AI model, which generates an optimal installation plan or production line layout plan based on historical data and current technical parameters.

[0422] Input: Analysis-ready data, additional information

[0423] Output: Generated installation plan or production line layout plan

[0424] Step 6:

[0425] The server then assembles the generated plan, cost estimates, and additional suggestions based on the emotional state into a single response package and sends it back to the device.

[0426] Input: Generated installation plan or production line layout plan

[0427] Output: Response package (installation plan, cost estimate, additional proposals)

[0428] Step 7:

[0429] The device receives the returned response package and displays it to the user via a head-mounted display or smart glasses. The user can then review the displayed plan, cost estimate, and additional proposals.

[0430] Input: Response package

[0431] Output: Show optimization plan

[0432] Step 8:

[0433] The user can re-enter the conditions based on the displayed installation plan and cost estimate and send it to the system to readjust the plan.

[0434] Input: Modified condition input

[0435] Output: Reworked installation plan and cost estimate

[0436] 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.

[0437] 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.

[0438] 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.

[0439] [Second embodiment]

[0440] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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."

[0452] The present invention relates to a system that proposes optimal plans for installing and improving base stations. This system uses a generative AI model based on user input information to generate an optimal installation plan and presents it to the user.

[0453] A natural language description of the program's processing

[0454] 1. User Input

[0455] Through a web or mobile application, users input information about the base station installation, such as the city name (e.g., "Tokyo"), purpose (e.g., "expand communication coverage"), and conditions (e.g., "cost constraints are not strict").

[0456] 2. Data transmission

[0457] When the user finishes inputting information, the terminal transmits the input information to the server, including the city name, purpose, and conditions.

[0458] 3. Receiving data and preparing for analysis

[0459] The server receives the data sent by the device and formats it for analysis, possibly retrieving additional data from geographic and population databases as needed.

[0460] 4. Analysis using AI models

[0461] The server inputs the formatted data into a generative AI model to generate an optimal base station installation plan, including installation locations, technical requirements, estimated costs, etc.

[0462] 5. Calculating cost estimates

[0463] Based on the generated installation plan, the server calculates a cost estimate, which includes the installation costs, operating costs, and required hardware and software costs for each base station.

[0464] 6. Organizing and displaying results

[0465] The server formats the generated installation plan and cost estimate into a single response package and sends it back to the terminal, where the user can view this information.

[0466] Specific examples

[0467] As an example, consider the installation of a new 5G base station in Tokyo.

[0468] 1. User Input

[0469] The user inputs "Tokyo," "expanded communication coverage area," and "cost constraints are not strict."

[0470] 2. Data transmission

[0471] The terminal transmits the information to the server.

[0472] 3. Receiving data and preparing for analysis

[0473] The server receives the data and retrieves detailed geographic information and population data for Tokyo from geographic information and population databases.

[0474] 4. Analysis using AI models

[0475] The server uses the generated AI model to generate the optimal installation location (for example, the Shinjuku area) and the technical requirements for each base station.

[0476] 5. Calculating cost estimates

[0477] The server calculates installation costs (e.g., the cost of installing three 5G base stations in the Shinjuku area), operating costs, and hardware and software costs.

[0478] 6. Organizing and displaying results

[0479] The server formats the generated installation plan and cost estimate and returns it to the device, where the user can view the specific plan and cost estimate for installing three 5G base stations in the Shinjuku area.

[0480] As described above, the present invention is a system that allows users to easily obtain optimal plans for installing and improving base stations. By using a generative AI model, users can efficiently create optimal installation plans even without advanced technical knowledge.

[0481] The processing flow will be explained below.

[0482] Step 1:

[0483] The user launches a web or mobile application and enters the information required for base station installation, such as the desired installation location (e.g., "Tokyo"), the purpose of installation (e.g., "expanding communication coverage"), and conditions (e.g., "cost constraints are not strict").

[0484] Step 2:

[0485] The terminal receives the information entered by the user, including the city name, purpose, and conditions.

[0486] Step 3:

[0487] The terminal sends the received input information to the server, where the data is converted into a predetermined data format (e.g., JSON format).

[0488] Step 4:

[0489] The server receives the data sent from the device and formats it into the correct format, or performs any necessary conversions if the data format is different.

[0490] Step 5:

[0491] The server uses the formatted data to retrieve additional information from geographic and population databases, such as detailed geographic and demographic information for Tokyo.

[0492] Step 6:

[0493] The server inputs the received data and additional data into a generative AI model for analysis, which generates an optimal base station installation plan based on past data and current technical parameters.

[0494] Step 7:

[0495] The generative AI model generates analytical results and creates an optimal installation plan, including suitable installation locations, the number of base stations required, and technical specifications.

[0496] Step 8:

[0497] The server calculates a cost estimate based on the generated installation plan, including equipment costs, infrastructure costs, and operation and maintenance costs.

[0498] Step 9:

[0499] The server assembles the installation plan and cost estimate into a single response package that includes plan details, maps, and cost analysis.

[0500] Step 10:

[0501] The server returns a response package to the terminal, which receives the returned data and displays it to the user.

[0502] Step 11:

[0503] The user can use the terminal to view the installation plan and cost estimate displayed, and if necessary, enter new specifications to refine the plan and submit it again.

[0504] Example 1

[0505] 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."

[0506] Conventional base station installation plan generation systems often require a lot of time and effort to analyze user-entered information and create optimal installation plans based on that information. Furthermore, creating an installation plan can be difficult if the user does not have advanced technical knowledge. Furthermore, the process of integrating multiple data, such as calculating cost estimates and obtaining geographic information, is complicated, making it difficult to proceed efficiently.

[0507] 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.

[0508] In this invention, the server includes a means for formatting the received information into an analytical format, a means for acquiring additional data from a geographic information database and a population database, and a means for inputting the analytical data into a generative AI model to generate an optimal base station installation plan. This allows the base station installation plan to be generated quickly and efficiently based on the user's input information, enabling the user to obtain the optimal installation plan without advanced technical knowledge. Furthermore, cost estimates are calculated automatically, enhancing user convenience.

[0509] "User" refers to an individual or organization that uses the system to input base station installation information.

[0510] A "base station" is a type of wireless communication device that relays communications within a communication network.

[0511] "Installation location" refers to a specific geographic location where a base station is physically located.

[0512] "Purpose" refers to the specific goal or reason for installing a base station.

[0513] "Conditions" refers to restrictions and requirements related to the installation of a base station.

[0514] "Terminal" refers to a device used by a user to communicate input information to a system.

[0515] "Server" refers to the computer system that analyzes the received information and generates an optimal base station installation plan using a generative AI model.

[0516] A "generative AI model" refers to an artificial intelligence algorithm that learns from large amounts of data to perform specific tasks.

[0517] "Geographic information database" refers to a database containing geographic location information.

[0518] "Population Database" refers to a database containing demographic information for a particular geographic area.

[0519] "Analysis format" refers to the data format that converts received information into a format suitable for the generative AI model.

[0520] "Installation Plan" refers to a plan proposed by a generative AI model showing the specific base station placement and related technical requirements.

[0521] A "cost estimate" is a calculation of the costs required to install and operate a base station.

[0522] "Response Package" refers to the data returned to the User that includes an installation plan and cost estimate.

[0523] The present invention is a system that generates an optimal base station installation plan using a generative AI model, where a user inputs the installation location, purpose, and conditions of the base station. To implement this system, a web or mobile application is required, and these applications have the function of sending the user's input information to a server.

[0524] First, the user uses the application's input screen to input the city name, purpose of installation, and conditions. For example, they can enter information such as "Tokyo," "expanding communication coverage area," and "cost constraints are not strict." This input information is sent by the device to the server as a JSON-formatted data packet.

[0525] The server formats the received data for analysis. Specifically, it parses the JSON data and sends queries to geographic information and population databases to obtain the necessary geographic and population data. In this process, the server collects detailed geographic and population data related to the city name "Tokyo."

[0526] Next, the server generates a prompt to input the data into a generative AI model. For example, it generates a sentence such as, "Please propose the optimal base station installation plan to expand communication coverage in Tokyo. Cost constraints are not strict." The data for analysis is input into a generative AI model (e.g., GPT-4) along with this prompt.

[0527] The generative AI model proposes an optimal base station installation plan, which includes the installation location (for example, the Shinjuku area), technical requirements, estimated costs, etc. Based on the generated plan, the server calculates installation costs, operating costs, and the costs of the necessary hardware and software.

[0528] This information is formatted into a single response package and sent back from the server to the terminal. The user can then view the generated specific installation plan and cost estimate through the terminal. This system enables users without advanced technical knowledge to efficiently obtain the optimal base station installation plan.

[0529] This concludes the implementation of the present invention. As an example, consider the case of installing a new 5G base station in Tokyo. The user inputs "Tokyo," "expanded coverage area," and "low cost constraints." Based on this information, the server formats the data and collects any additional data needed. It then uses a generative AI model to generate an optimal installation plan and calculate a cost estimate. Finally, the user can view a specific plan and cost estimate for installing three 5G base stations in the Shinjuku area.

[0530] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0531] Step 1: User enters information

[0532] Specific behavior:

[0533] The user opens an input screen for a web or mobile application.

[0534] The user enters the city name "Tokyo," the purpose "expanding communication coverage area," and the conditions "cost constraints are not strict" into the application's input form.

[0535] The entered data is organized in JSON format.

[0536] input:

[0537] City name: "Tokyo"

[0538] Objective: "Expanding communication coverage"

[0539] Condition: "Cost constraints are not strict"

[0540] output:

[0541] JSON formatted data packet

[0542] Step 2: Sending data

[0543] Specific behavior:

[0544] The user clicks the "Submit" button.

[0545] The terminal transmits the input information to the server.

[0546] The transmitted data packets are checked for correct format.

[0547] input:

[0548] JSON formatted data packet

[0549] output:

[0550] Data sent to the server

[0551] Step 3: Receive data and prepare for analysis

[0552] Specific behavior:

[0553] The server parses the JSON data received from the terminal.

[0554] The data is formatted for analysis and queries are sent to geographic and population databases to obtain additional data.

[0555] For example, the server obtains geographic information and population data related to the city name "Tokyo."

[0556] input:

[0557] JSON data received from the terminal

[0558] output:

[0559] Formatted analytical data and additional geographic and demographic data

[0560] Step 4: Generate prompts and input them to the AI ​​model

[0561] Specific behavior:

[0562] The server generates a prompt for the generative AI model, such as "Please propose the optimal base station installation plan to expand the communication coverage area in Tokyo. Cost constraints are not strict."

[0563] The server inputs the prompt sentence and analysis data into a generative AI model (e.g., GPT-4).

[0564] input:

[0565] Formatted data for analysis

[0566] Generated prompt statement

[0567] output:

[0568] Input data for generative AI models

[0569] Step 5: Analyze and generate installation plans using generative AI models

[0570] Specific behavior:

[0571] The server receives the optimal base station installation plan proposed by the generative AI model.

[0572] For example, the generated installation plan includes the installation location (Shinjuku area), technical requirements, and estimated costs.

[0573] input:

[0574] Input data for generative AI models

[0575] output:

[0576] Proposed installation plan (location, technical requirements, estimated costs)

[0577] Step 6: Calculate cost estimates

[0578] Specific behavior:

[0579] The server calculates a detailed cost estimate based on the generated installation plan.

[0580] For example, we will calculate the installation costs, operating costs, and necessary hardware and software costs for installing three 5G base stations in the Shinjuku area.

[0581] input:

[0582] Proposed installation plan

[0583] output:

[0584] Detailed cost estimates

[0585] Step 7: Organizing and displaying results

[0586] Specific behavior:

[0587] The server formats the installation plan and cost estimate into a single response package.

[0588] The server returns the response package to the terminal.

[0589] The user can view the generated specific installation plan and cost estimate through the terminal.

[0590] input:

[0591] Detailed cost estimates

[0592] Proposed installation plan

[0593] output:

[0594] Response package (installation plan and cost estimate) returned to the user

[0595] (Application example 1)

[0596] 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."

[0597] In autonomous vehicles, communication interruptions have a significant impact on safety and efficiency. Furthermore, conventional base station installation planning systems face the problem of difficulty in optimizing communication areas taking real-time location information into account. This leads to frequent communication interruptions and information loss due to interruptions as the vehicle moves, creating a need for a system that ensures stable communication and provides an optimal communication environment even while the vehicle is moving.

[0598] 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.

[0599] In this invention, the server includes: means for a user to input information regarding the installation location, purpose, and conditions of a base station; means for a terminal to receive the input information and send it to the server; means for the server to analyze the received information and generate an optimal base station installation plan using a generative AI model; means for the server to return the generated plan to the terminal and display it to the user; means for acquiring vehicle location information in real time and determining the optimal communication area; and means for providing base station installation information based on the optimal communication area. This makes it possible to minimize communication interruptions even while an autonomous vehicle is moving, and to provide a stable communication environment.

[0600] definition statement

[0601] "User" refers to a person who inputs information about the location, purpose, and conditions of a base station.

[0602] "Terminal" refers to a device for receiving information entered by a user and transmitting it to a server.

[0603] "Server" refers to a device or system that analyzes the received information and generates an optimal base station installation plan using a generative AI model.

[0604] "Generative AI model" refers to an artificial intelligence model used to generate optimal base station installation plans based on user input information.

[0605] "Base station installation plan" refers to a plan that includes the installation location and technical requirements of base stations with the aim of optimizing communication areas.

[0606] "Real-time acquisition" refers to the immediate collection of current vehicle location information.

[0607] An "optimal communication area" refers to an area that minimizes communication interruptions and provides a stable communication environment.

[0608] "Installation information" refers to detailed information including the specific installation location and technical requirements of the base station.

[0609] MODE FOR CARRYING OUT THE INVENTION

[0610] The present invention is a system for minimizing communication interruptions caused by the movement of an autonomous vehicle and providing a stable communication environment. This system includes: means for a user to input information regarding the installation location, purpose, and conditions of a base station; means for a terminal to receive the input information and send it to a server; means for the server to analyze the received information and generate an optimal base station installation plan using a generative AI model; means for the server to return the generated plan to the terminal and display it to the user; means for acquiring vehicle location information in real time and determining the optimal communication area; and means for providing base station installation information based on the optimal communication area.

[0611] Specific Embodiments

[0612] 1. User input and its reception:

[0613] In this system, users input information about the location, purpose, and conditions of base stations through a dedicated application. Specifically, the driver of the autonomous vehicle uses a smartphone to input the desired location for base stations and requirements for maintaining stable communications.

[0614] 2. Server analyzes data and uses generated AI models:

[0615] The terminal sends the input information to a server, which then analyzes the received information. At this time, a generative AI model is used to generate an optimal base station installation plan. The generative AI model used includes the GPT model. This allows the optimal communication area to be determined based on the route the vehicle will travel.

[0616] 3. Real-time location information and optimal coverage:

[0617] The vehicle's GPS device and communication module acquire the vehicle's location information in real time. Based on this location information, the server determines the optimal communication area and provides that information to the vehicle's terminal. This minimizes communication interruptions even when the vehicle is moving.

[0618] 4. Providing optimal base station installation plans:

[0619] The system generates an optimal base station installation plan and provides it to the user with a cost estimate, including the base station installation location, technical requirements, estimated costs, etc. For example, based on the conditions "Tokyo," "expanded communication coverage area," and "no strict cost constraints," the system presents a plan for installing three 5G base stations in the Shinjuku area and displays the estimated cost.

[0620] Hardware and software used

[0621] Hardware: GPS receiver installed in the autonomous vehicle, smartphone

[0622] Software: Python as the execution environment, the requests library for receiving and sending data, the geopy library for geographic information processing, and a generative AI model (e.g., GPT model).

[0623] Examples of prompt statements

[0624] Based on the current location (35.6895, 139.6917) and the destination location (35.6895, 139.7000), propose an optimal base station installation plan. Communication stability is the most important factor, and cost constraints are not taken into consideration. The output should include the installation location and distance of each base station.

[0625] As described above, the present invention is effective as a means for ensuring the stability of communications for autonomous vehicles, and provides a system that optimizes the communications environment by providing users with base station installation plans.

[0626] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0627] Program processing flow

[0628] Step 1:

[0629] Users input information about the location, purpose, and conditions of the base station installation through a dedicated application. This input data includes the desired installation location and requirements for maintaining communication stability. The input data is expressed in text and numerical format. For example, information such as "Tokyo," "expanding communication coverage area," and "cost constraints are not strict" can be entered.

[0630] Step 2:

[0631] The terminal receives the information entered by the user and sends it to the server. The input data is formatted and sent over the network to the server. At this stage, it is confirmed that the input information reaches the server properly.

[0632] Step 3:

[0633] The server receives the received information and retrieves additional data as needed. This additional data is retrieved from geographic and population databases and combined with the input data. The input data and additional data are then used to format the data for analysis. Specifically, geographic location data and population density information are collected.

[0634] Step 4:

[0635] The server inputs the formatted data into a generative AI model to generate an optimal base station installation plan. The generative AI model (e.g., a GPT model) analyzes the input data and generates a detailed plan including installation locations, technical requirements, estimated costs, etc. In this process, the AI ​​model uses historical data and predictive algorithms to generate the most efficient plan.

[0636] Step 5:

[0637] The server calculates a cost estimate based on the generated installation plan, including installation costs, operating costs, and the costs of required hardware and software for each base station, using a predefined cost database for this calculation.

[0638] Step 6:

[0639] The server then formats the installation plan and cost estimate into a single response package and sends it back to the terminal, where the information is processed and displayed to the user, showing the installation location, estimated costs, technical requirements, and other information on the user's screen.

[0640] Step 7:

[0641] Vehicle location information is acquired in real time and sent to the server. The vehicle's current location is periodically acquired using the GPS device and communication module installed in the vehicle. Based on this location information, the server determines the optimal communication area.

[0642] Step 8:

[0643] The server uses the vehicle's location information to provide base station installation information based on the optimal communication area. The installation plan generated by the generative AI model is linked to real-time location information and sends instructions to the device to maintain the optimal communication area. The communication area is dynamically adjusted based on the input current location information, minimizing communication interruptions.

[0644] The above steps will enable the realization of a system that provides optimal base station installation plans while ensuring stable communications for autonomous vehicles, significantly improving the safety and efficiency of autonomous vehicles.

[0645] 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.

[0646] This invention relates to a system that proposes optimal plans for installing and improving base stations, and in particular, a system that provides more advanced services by combining an emotion engine that recognizes user emotions. This system uses a generative AI model based on the user's input information and emotional state to generate an optimal installation plan and present it to the user.

[0647] A natural language description of the program's processing

[0648] 1. User Input

[0649] Through a web or mobile application, users input information about the installation of base stations, such as the desired location (e.g., "Tokyo"), the purpose of the installation (e.g., "expanding communication coverage"), and conditions (e.g., "cost constraints are not strict").

[0650] 2. User Emotion Recognition

[0651] As users input information, the emotion engine detects emotions from their facial expressions and voice in real time, using specific algorithms to analyze their emotional states, such as joy, surprise, sadness, and anger.

[0652] 3. Data transmission

[0653] When the user finishes inputting information, the device sends the input information and the emotion data detected by the emotion engine to the server, including the city name, purpose, condition, and emotional state.

[0654] 4. Receiving data and preparing for analysis

[0655] The server receives the data sent by the device and formats it correctly, converting it if necessary if the data format is different, and retrieving additional information from geographic and population databases.

[0656] 5. Analysis using AI models

[0657] The server then analyzes the formatted data and the added emotion data using a generative AI model, which generates an optimal base station installation plan based on past data and current technical parameters.

[0658] 6. Generating the optimal plan

[0659] Based on the analysis results, the generative AI model creates an optimal installation plan, including suitable installation locations, the number of base stations required, technical specifications, cost estimates, etc. It also takes into account the user's emotional data to generate explanations and additional suggestions for the user.

[0660] 7. Calculating cost estimates

[0661] The server calculates a cost estimate based on the installation plan, which includes equipment costs, infrastructure costs, and operation and maintenance costs.

[0662] 8. Organizing and displaying results

[0663] The server assembles the installation plan, cost estimate, and additional suggestions based on the user's emotional state into a response package that includes details of the plan, a map, a cost analysis, and actions based on the emotional state.

[0664] 9. Return of results

[0665] The server returns a response package to the terminal, which receives the returned data and displays it to the user.

[0666] 10. User Confirmation

[0667] The user can use the terminal to view the installation plan, cost estimate, and additional proposals displayed, and can enter new criteria to refine the plan as needed and resubmit it.

[0668] Specific examples

[0669] As an example, consider the installation of a new 5G base station in Tokyo.

[0670] 1. User Input and Emotion Recognition

[0671] As the user types in "Tokyo," "expanded communication coverage," and "cost constraints are not strict," the emotion engine detects feelings of hope and relief from the user's facial expression.

[0672] 2. Data transmission

[0673] The terminal transmits the information and emotion data to the server.

[0674] 3. Receiving data and preparing for sentiment analysis

[0675] The server receives the data and retrieves detailed information about Tokyo from geographic information and population databases.

[0676] 4. Analysis using AI models

[0677] The server uses the generated AI model to generate an explanation based on the optimal installation location (for example, the Shinjuku area), the technical requirements of each base station, and user sentiment.

[0678] 5. Calculating cost estimates

[0679] The server calculates the installation costs, operating costs, hardware and software costs.

[0680] 6. Organizing and displaying results

[0681] The server generates an installation plan, a cost estimate, and additional emotion-based explanations, which are then sent back to the device.

[0682] 7. User Confirmation

[0683] The user views and confirms on the device the specific plan, cost estimate, and additional instructions for installing three 5G base stations in the Shinjuku area.

[0684] As described above, the present invention provides a system that allows users to easily obtain optimal plans for installing and improving base stations. In particular, by using an emotion engine, it is possible to deliver proposals optimized for the user's emotional state, improving the user experience.

[0685] The processing flow will be explained below.

[0686] Step 1:

[0687] The user launches a web or mobile application and inputs the necessary information for base station installation, such as "Tokyo," "expanded communication coverage area," and "cost constraints are not strict."

[0688] Step 2:

[0689] As users input information, the device's built-in emotion engine detects emotions in real time from the user's facial expressions and voice, using algorithms to analyze emotional states such as joy, surprise, sadness, and anger.

[0690] Step 3:

[0691] When the user finishes inputting, the device sends the input information and the emotion data detected by the emotion engine to the server. The data is formatted in a specific data format (e.g., JSON format).

[0692] Step 4:

[0693] The server receives the data sent from the device, including the city name, purpose, condition, and emotional state.

[0694] Step 5:

[0695] The server formats the received data for analysis, and performs any necessary conversions if the data format is different.

[0696] Step 6:

[0697] The server retrieves detailed geographic and population data for Tokyo from geographic and population databases, and in the process collects additional data using the necessary APIs.

[0698] Step 7:

[0699] The server inputs the formatted data and the added emotion data into a generative AI model for analysis. The generative AI model generates an optimal base station installation plan based on past data and current technical data.

[0700] Step 8:

[0701] Based on the analysis results, the generative AI model creates an optimal installation plan, including suitable installation locations, the number of base stations required, technical specifications, and estimated costs.

[0702] Step 9:

[0703] The server calculates a cost estimate based on the generated installation plan, including equipment costs, infrastructure costs, and operation and maintenance costs.

[0704] Step 10:

[0705] The server assembles the installation plan, cost estimate, and additional instructions and suggestions based on the user's emotional state into a response package that includes details of the plan, a map, a cost analysis, and actions based on the emotional state.

[0706] Step 11:

[0707] The server returns a response package to the terminal, which receives the returned data and displays it to the user.

[0708] Step 12:

[0709] The user can use the terminal to view the displayed installation plan, cost estimate, and additional instructions and suggestions, and can also enter new requirements to refine the plan if necessary and resubmit it.

[0710] Example 2

[0711] 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."

[0712] Conventional base station installation planning systems generate optimal installation plans based on user input information, but they are unable to consider the user's feelings and preferences, resulting in a lack of improvement in the user experience. Furthermore, simply collecting and analyzing information often does not allow for flexible adaptation to diverse environments and conditions. This can result in reduced installation plan accuracy and reduced user satisfaction.

[0713] 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.

[0714] In this invention, the server includes means for collecting emotional data along with input information using an emotion engine that recognizes the user's emotional state, means for analyzing the received information and emotional data and generating an optimal base station installation plan using a generative AI model, and means for acquiring and analyzing additional data by linking the received data with a geographic information database and a population database. This allows the server to generate an optimal installation plan that takes the user's emotions into consideration and is also able to flexibly respond to a variety of environments and conditions.

[0715] "User" refers to a person who inputs information about the location, purpose, and conditions of a base station.

[0716] "Emotion engine" refers to software or hardware technology for detecting and analyzing emotions from a user's facial expressions and voice in real time.

[0717] "Terminal" refers to an electronic device that runs web applications and mobile applications used by a user.

[0718] "Server" refers to a computer system for receiving and analyzing information and emotion data sent by a user.

[0719] "Generative AI model" refers to an artificial intelligence algorithm that generates optimal base station installation plans based on past data and technical parameters.

[0720] "Geographic information database" refers to a database that stores and provides geographic information about a specific area.

[0721] "Population Database" means a database that stores and provides demographic information for a particular geographic area.

[0722] "Installation Plan" means a plan for installing Base Stations that includes the optimum locations, the number of Base Stations required, technical specifications, and cost estimates.

[0723] "Cost estimate" refers to the total amount calculated based on equipment costs, infrastructure costs, operation and maintenance costs, etc. for installing a base station.

[0724] A "response package" refers to a data package that includes an optimal installation plan, cost estimates, and additional proposals based on sentiment data.

[0725] The present invention relates to a system that proposes an optimal plan for base station installation, incorporating an emotion engine that recognizes user emotions. This system utilizes a generative AI model to generate an optimal installation plan based on information such as installation location, purpose, and conditions entered by the user, as well as the user's emotional state, and presents the plan to the user. Specific embodiments of this system are described below.

[0726] First, the user uses a web or mobile application to input the necessary information regarding the installation of the base station. Specifically, the user inputs the desired installation location (e.g., "Tokyo"), the purpose of the installation (e.g., "expanding communication coverage"), and conditions (e.g., "cost constraints are not strict"), etc. During this process, an emotion engine built into the application analyzes the user's facial expressions and voice in real time to detect their emotional state. The emotion engine uses facial recognition and voice analysis technology to determine emotions such as joy or anxiety expressed by the user's facial expression.

[0727] When the user finishes entering information, the device sends the information entered by the user and the emotion data detected by the emotion engine to the server. The server receives the received information and emotion data and performs the necessary formatting and analysis. Specifically, the server unifies data formats and appropriately converts incomplete or data with different formats. It also obtains additional information from geographic information databases and population databases to prepare for comprehensive data analysis.

[0728] The server then uses a generative AI model to generate an optimal base station installation plan based on the formatted input information and additional emotional data. The generative AI model analyzes past installation data and current technical parameters to propose an installation plan optimized for the user's needs and emotional state. This plan includes the optimal installation location, the required number of base stations, technical specifications, and cost estimates. It also generates explanations and additional suggestions that take the user's emotional state into consideration.

[0729] The generated installation plan is sent back from the server to the device and displayed to the user. The device displays the received data in an easy-to-read format, allowing the user to review the installation plan, cost estimates, and additional proposals. If necessary, the user can adjust the plan by entering new conditions and submitting it again.

[0730] As a concrete example, consider the installation of a new 5G base station in Tokyo. The user inputs "Tokyo," "expanded communication coverage," and "cost constraints are not strict," and the emotion engine detects the emotion of joy from the user's facial expression while they are typing. The device then sends this data to the server, which uses a generative AI model to generate an optimal installation plan. For example, the server might suggest the Shinjuku area as the optimal installation location and build a plan that includes the required number of base stations, technical specifications, cost estimates, and more. Additionally, additional explanations are generated to provide a sense of security based on the user's emotional state. This information is then sent back to the device for the user to confirm.

[0731] An example of a prompt is as follows:

[0732] "I would like to install a new 5G base station in Tokyo. The goal is to expand communication coverage, and cost constraints are not strict." Also, if the user smiles while typing, the emotion engine will detect hopeful emotions.

[0733] The above is a specific embodiment for carrying out the present invention. This system can generate an optimal base station installation plan that takes into account the emotional state of the user, thereby improving the user experience.

[0734] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0735] Step 1:

[0736] The user launches a web or mobile application and inputs information about the base station installation, such as the desired installation location (e.g., "Tokyo"), the purpose of the installation (e.g., "expanding communication coverage"), and conditions (e.g., "cost constraints are not strict"). The input data is temporarily stored within the application.

[0737] Input: desired installation location, purpose, conditions

[0738] Output: Save input information

[0739] Step 2:

[0740] As the user enters information, the emotion engine analyzes the user's facial expressions and voice in real time to detect their emotional state. The emotion engine uses facial recognition and voice analysis technologies to determine emotions such as joy or anxiety from the user's facial expressions and voice. This generates emotion data based on the user's input behavior.

[0741] Input: User's facial expression, voice

[0742] Output: Detected emotion data

[0743] Step 3:

[0744] Once the user has finished inputting all the information, the terminal transmits the input information and the emotion data generated by the emotion engine to the server, where the data is packaged in an appropriate format.

[0745] Input: Input information, emotion data

[0746] Output: Send data to the server

[0747] Step 4:

[0748] The server receives the data sent from the devices and performs the necessary formatting and analysis, standardizing the data format and converting incomplete or differently formatted data, and obtaining additional information from geographic and population databases to prepare for comprehensive data analysis.

[0749] Input: Send data

[0750] Output: formatted data, additional data

[0751] Step 5:

[0752] The server generates an optimal base station installation plan using a generative AI model based on the formatted input information and additional emotional data. The generative AI model analyzes past installation data and current technical parameters to generate an installation plan optimized for the user's needs and emotional state.

[0753] Input: Formatted data, additional data

[0754] Output: Optimal installation plan

[0755] Step 6:

[0756] The generative AI model generates an installation plan based on the analysis, including the optimal installation location, the number of base stations required, technical specifications, and cost estimates. It also adds explanations and additional suggestions that take into account the user's emotional state. The plan is then stored on a server.

[0757] Input: Optimal installation plan

[0758] Output: Complete installation plan package

[0759] Step 7:

[0760] The server returns the completed installation plan package to the terminal.

[0761] Input: Completed installation plan package

[0762] Output: Sending data to the terminal

[0763] Step 8:

[0764] The terminal displays the received installation plan, cost estimate, and additional suggestions in an easy-to-view format for the user.

[0765] Input: Data from the server

[0766] Output: What is displayed to the user

[0767] Step 9:

[0768] The user can review the installation plan, cost estimate, and additional proposals provided, enter new requirements if necessary, and submit again, starting from step 1.

[0769] Input: Presented data, additional user input

[0770] Output: Re-adjusted installation plan

[0771] (Application example 2)

[0772] 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."

[0773] Conventional base station installation plans mainly analyze and propose plans based on user-entered information about installation locations and conditions. However, because these methods do not take into account the user's emotional state, it is difficult to improve the sense of security and satisfaction felt by the user. Furthermore, in industrial settings such as factories, the proposed optimization plans do not reflect the stress and fatigue of workers because they do not take their emotional state into account, which can lead to reduced production efficiency. There is a need for a new system that can solve these issues and improve the user experience.

[0774] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0775] In this invention, the server includes: means for a user to input information regarding the installation location, purpose, and conditions of a base station; means for a terminal to receive the input information and transmit it to the server; means for the server to analyze the received information and emotion data detected by an emotion engine and generate an optimal base station installation plan using a generative AI model; and means for returning the generated plan and cost estimate to the terminal and displaying it to the user. This makes it possible to generate an optimal installation plan that takes the user's emotional state into consideration, thereby improving security and satisfaction as well as production efficiency.

[0776] "User" means any person or entity that uses the System and inputs information.

[0777] "Base station location" means the specific location of new wireless communication equipment to provide communications.

[0778] "Purpose" refers to the specific intention or goal when installing a base station.

[0779] "Conditions" means the constraints, desires and requirements that must be taken into account when installing a base station.

[0780] "Terminal" refers to an electronic device used by a user to input information and display received installation plans.

[0781] "Input information" refers to data that a user provides to the system through a terminal, such as the installation location and conditions.

[0782] "Server" refers to the central processing unit that receives input data, analyzes it, and generates plans using generative AI models.

[0783] An "emotion engine" refers to an analysis system that has an algorithm that detects the emotional state of a user from their facial expressions and voice.

[0784] "Emotion Data" refers to data regarding a user's emotional state as detected by an emotion engine.

[0785] "Generative AI model" refers to an artificial intelligence model that generates optimal installation plans based on past data and current technical parameters.

[0786] "Installation Plan" refers to a plan for optimal base station installation methods and placement created by a generative AI model.

[0787] "Cost estimate" refers to the calculation of the costs involved in installing and maintaining a base station.

[0788] "Display" refers to the presentation of information to inform the user of the generated installation plan and cost estimate.

[0789] This invention relates to a system that proposes optimal plans for base station installation and improvement, and is particularly applicable to the optimization of factory robot production lines. This system uses a generative AI model to generate an optimal installation plan based on the user's input information and emotional state, and presents it to the user.

[0790] Hardware and software used

[0791] Hardware:

[0792] Head-mounted display (HMD) or smart glasses: collects and displays worker input and emotional state.

[0793] Camera and microphone style emotion recognition sensor: Detects the user's emotional state in real time from their facial expressions and voice.

[0794] software:

[0795] Emotion engine: An analysis system equipped with specific algorithms to analyze user emotions.

[0796] Generative AI model: An artificial intelligence model for generating optimal installation plans.

[0797] Geographic and population databases: Databases for capturing detailed information about installation locations.

[0798] Data processing and calculation

[0799] User input:

[0800] Users access the system via a head-mounted display (HMD) or smart glasses and input information about the base station's location, purpose, and conditions. Factory workers also input information about their current work and environment into an operating terminal.

[0801] Emotion recognition:

[0802] The emotion engine uses a camera and microphone to analyze the user's facial expressions and voice in real time during the input process to detect their emotional state, such as "happiness," "surprise," "sadness," "anger," and "relief."

[0803] Data transmission and analysis:

[0804] The device sends the input information and emotion data to the server, which formats the received data into the correct format, performs any necessary conversions, and retrieves additional information from geographic information and population databases to prepare for analysis.

[0805] Generative AI models analyze and plan:

[0806] The server analyzes the formatted data and the added emotion data using a generative AI model, which generates an optimal base station installation plan or production line layout plan based on past data and current technical parameters.

[0807] Viewing Results:

[0808] The server generates an installation plan, a cost estimate, and additional suggestions based on the user's emotional state, and sends it back to the device in a single response package, which is displayed to the user via a head-mounted display or smart glasses.

[0809] Specific examples

[0810] As an example, consider a production line optimization system for a factory that is introducing new machinery to Line C. A factory worker inputs "Introducing new equipment to Line C," and the emotion engine detects "joy" during the process. The generative AI model uses the detected emotion and input data to generate an optimal adjustment plan for the layout and operating hours of Line C and its surrounding areas, and suggests adding appropriate breaks. It also displays a cost estimate of 1,200,000 yen.

[0811] Example prompts to input to a generative AI model:

[0812] User Input: "We're going to install a new line machine."

[0813] Emotion data: "Joy"

[0814] Application example: "Planning the introduction of a new machine on Line C"

[0815] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0816] Step 1:

[0817] The user inputs information about the base station installation location, purpose, and conditions using a head-mounted display (HMD) or smart glasses. At this time, the user inputs specific information such as the "desired installation location," "purpose of installation," and "conditions" into the operating terminal. In the case of factory workers, they also input information about the "work content" and "environment."

[0818] Input: desired installation location, purpose of installation, conditions, work content, environment

[0819] Output: User input information

[0820] Step 2:

[0821] The emotion engine uses a camera and microphone to analyze the user's facial expressions and voice in real time to detect their emotional state, which is classified into categories such as "happiness," "surprise," "sadness," "anger," and "relief."

[0822] Input: User's facial expression, voice

[0823] Output: Emotion data (happiness, surprise, sadness, anger, relief, etc.)

[0824] Step 3:

[0825] The terminal transmits the user's input information and emotion data to the server, which converts the data into an appropriate format and transmits it.

[0826] Input: User input information, emotion data

[0827] Output: Sending formatted data

[0828] Step 4:

[0829] The server formats the received data and retrieves additional information from geographic and population databases. The formatted data and additional information are used to prepare for analysis.

[0830] Input: Received data (user input information, emotion data)

[0831] Output: Analysis-ready data

[0832] Step 5:

[0833] The server analyzes the incoming data and sentiment data using a generative AI model, which generates an optimal installation plan or production line layout plan based on historical data and current technical parameters.

[0834] Input: Analysis-ready data, additional information

[0835] Output: Generated installation plan or production line layout plan

[0836] Step 6:

[0837] The server then assembles the generated plan, cost estimates, and additional suggestions based on the emotional state into a single response package and sends it back to the device.

[0838] Input: Generated installation plan or production line layout plan

[0839] Output: Response package (installation plan, cost estimate, additional proposals)

[0840] Step 7:

[0841] The device receives the returned response package and displays it to the user via a head-mounted display or smart glasses. The user can then review the displayed plan, cost estimate, and additional proposals.

[0842] Input: Response package

[0843] Output: Show optimization plan

[0844] Step 8:

[0845] The user can re-enter the conditions based on the displayed installation plan and cost estimate and send it to the system to readjust the plan.

[0846] Input: Modified condition input

[0847] Output: Reworked installation plan and cost estimate

[0848] 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.

[0849] 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.

[0850] 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.

[0851] [Third embodiment]

[0852] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0853] 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.

[0854] 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).

[0855] 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.

[0856] 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.

[0857] 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).

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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."

[0864] The present invention relates to a system that proposes optimal plans for installing and improving base stations. This system uses a generative AI model based on user input information to generate an optimal installation plan and presents it to the user.

[0865] A natural language description of the program's processing

[0866] 1. User Input

[0867] Through a web or mobile application, users input information about the base station installation, such as the city name (e.g., "Tokyo"), purpose (e.g., "expand communication coverage"), and conditions (e.g., "cost constraints are not strict").

[0868] 2. Data transmission

[0869] When the user finishes inputting information, the terminal transmits the input information to the server, including the city name, purpose, and conditions.

[0870] 3. Receiving data and preparing for analysis

[0871] The server receives the data sent by the device and formats it for analysis, possibly retrieving additional data from geographic and population databases as needed.

[0872] 4. Analysis using AI models

[0873] The server inputs the formatted data into a generative AI model to generate an optimal base station installation plan, including installation locations, technical requirements, estimated costs, etc.

[0874] 5. Calculating cost estimates

[0875] Based on the generated installation plan, the server calculates a cost estimate, which includes the installation costs, operating costs, and required hardware and software costs for each base station.

[0876] 6. Organizing and displaying results

[0877] The server formats the generated installation plan and cost estimate into a single response package and sends it back to the terminal, where the user can view this information.

[0878] Specific examples

[0879] As an example, consider the installation of a new 5G base station in Tokyo.

[0880] 1. User Input

[0881] The user inputs "Tokyo," "expanded communication coverage area," and "cost constraints are not strict."

[0882] 2. Data transmission

[0883] The terminal transmits the information to the server.

[0884] 3. Receiving data and preparing for analysis

[0885] The server receives the data and retrieves detailed geographic information and population data for Tokyo from geographic information and population databases.

[0886] 4. Analysis using AI models

[0887] The server uses the generated AI model to generate the optimal installation location (for example, the Shinjuku area) and the technical requirements for each base station.

[0888] 5. Calculating cost estimates

[0889] The server calculates installation costs (e.g., the cost of installing three 5G base stations in the Shinjuku area), operating costs, and hardware and software costs.

[0890] 6. Organizing and displaying results

[0891] The server formats the generated installation plan and cost estimate and returns it to the device, where the user can view the specific plan and cost estimate for installing three 5G base stations in the Shinjuku area.

[0892] As described above, the present invention is a system that allows users to easily obtain optimal plans for installing and improving base stations. By using a generative AI model, users can efficiently create optimal installation plans even without advanced technical knowledge.

[0893] The processing flow will be explained below.

[0894] Step 1:

[0895] The user launches a web or mobile application and enters the information required for base station installation, such as the desired installation location (e.g., "Tokyo"), the purpose of installation (e.g., "expanding communication coverage"), and conditions (e.g., "cost constraints are not strict").

[0896] Step 2:

[0897] The terminal receives the information entered by the user, including the city name, purpose, and conditions.

[0898] Step 3:

[0899] The terminal sends the received input information to the server, where the data is converted into a predetermined data format (e.g., JSON format).

[0900] Step 4:

[0901] The server receives the data sent from the device and formats it into the correct format, or performs any necessary conversions if the data format is different.

[0902] Step 5:

[0903] The server uses the formatted data to retrieve additional information from geographic and population databases, such as detailed geographic and demographic information for Tokyo.

[0904] Step 6:

[0905] The server inputs the received data and additional data into a generative AI model for analysis, which generates an optimal base station installation plan based on past data and current technical parameters.

[0906] Step 7:

[0907] The generative AI model generates analytical results and creates an optimal installation plan, including suitable installation locations, the number of base stations required, and technical specifications.

[0908] Step 8:

[0909] The server calculates a cost estimate based on the generated installation plan, including equipment costs, infrastructure costs, and operation and maintenance costs.

[0910] Step 9:

[0911] The server assembles the installation plan and cost estimate into a single response package that includes plan details, maps, and cost analysis.

[0912] Step 10:

[0913] The server returns a response package to the terminal, which receives the returned data and displays it to the user.

[0914] Step 11:

[0915] The user can use the terminal to view the installation plan and cost estimate displayed, and if necessary, enter new specifications to refine the plan and submit it again.

[0916] Example 1

[0917] 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."

[0918] Conventional base station installation plan generation systems often require a lot of time and effort to analyze user-entered information and create optimal installation plans based on that information. Furthermore, creating an installation plan can be difficult if the user does not have advanced technical knowledge. Furthermore, the process of integrating multiple data, such as calculating cost estimates and obtaining geographic information, is complicated, making it difficult to proceed efficiently.

[0919] 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.

[0920] In this invention, the server includes a means for formatting the received information into an analytical format, a means for acquiring additional data from a geographic information database and a population database, and a means for inputting the analytical data into a generative AI model to generate an optimal base station installation plan. This allows the base station installation plan to be generated quickly and efficiently based on the user's input information, enabling the user to obtain the optimal installation plan without advanced technical knowledge. Furthermore, cost estimates are calculated automatically, enhancing user convenience.

[0921] "User" refers to an individual or organization that uses the system to input base station installation information.

[0922] A "base station" is a type of wireless communication device that relays communications within a communication network.

[0923] "Installation location" refers to a specific geographic location where a base station is physically located.

[0924] "Purpose" refers to the specific goal or reason for installing a base station.

[0925] "Conditions" refers to restrictions and requirements related to the installation of a base station.

[0926] "Terminal" refers to a device used by a user to communicate input information to a system.

[0927] "Server" refers to the computer system that analyzes the received information and generates an optimal base station installation plan using a generative AI model.

[0928] A "generative AI model" refers to an artificial intelligence algorithm that learns from large amounts of data to perform specific tasks.

[0929] "Geographic information database" refers to a database containing geographic location information.

[0930] "Population Database" refers to a database containing demographic information for a particular geographic area.

[0931] "Analysis format" refers to the data format that converts received information into a format suitable for the generative AI model.

[0932] "Installation Plan" refers to a plan proposed by a generative AI model showing the specific base station placement and related technical requirements.

[0933] A "cost estimate" is a calculation of the costs required to install and operate a base station.

[0934] "Response Package" refers to the data returned to the User that includes an installation plan and cost estimate.

[0935] The present invention is a system that generates an optimal base station installation plan using a generative AI model, where a user inputs the installation location, purpose, and conditions of the base station. To implement this system, a web or mobile application is required, and these applications have the function of sending the user's input information to a server.

[0936] First, the user uses the application's input screen to input the city name, purpose of installation, and conditions. For example, they can enter information such as "Tokyo," "expanding communication coverage area," and "cost constraints are not strict." This input information is sent by the device to the server as a JSON-formatted data packet.

[0937] The server formats the received data for analysis. Specifically, it parses the JSON data and sends queries to geographic information and population databases to obtain the necessary geographic and population data. In this process, the server collects detailed geographic and population data related to the city name "Tokyo."

[0938] Next, the server generates a prompt to input the data into a generative AI model. For example, it generates a sentence such as, "Please propose the optimal base station installation plan to expand communication coverage in Tokyo. Cost constraints are not strict." The data for analysis is input into a generative AI model (e.g., GPT-4) along with this prompt.

[0939] The generative AI model proposes an optimal base station installation plan, which includes the installation location (for example, the Shinjuku area), technical requirements, estimated costs, etc. Based on the generated plan, the server calculates installation costs, operating costs, and the costs of the necessary hardware and software.

[0940] This information is formatted into a single response package and sent back from the server to the terminal. The user can then view the generated specific installation plan and cost estimate through the terminal. This system enables users without advanced technical knowledge to efficiently obtain the optimal base station installation plan.

[0941] This concludes the implementation of the present invention. As an example, consider the case of installing a new 5G base station in Tokyo. The user inputs "Tokyo," "expanded coverage area," and "low cost constraints." Based on this information, the server formats the data and collects any additional data needed. It then uses a generative AI model to generate an optimal installation plan and calculate a cost estimate. Finally, the user can view a specific plan and cost estimate for installing three 5G base stations in the Shinjuku area.

[0942] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0943] Step 1: User enters information

[0944] Specific behavior:

[0945] The user opens an input screen for a web or mobile application.

[0946] The user enters the city name "Tokyo," the purpose "expanding communication coverage area," and the conditions "cost constraints are not strict" into the application's input form.

[0947] The entered data is organized in JSON format.

[0948] input:

[0949] City name: "Tokyo"

[0950] Objective: "Expanding communication coverage"

[0951] Condition: "Cost constraints are not strict"

[0952] output:

[0953] JSON formatted data packet

[0954] Step 2: Sending data

[0955] Specific behavior:

[0956] The user clicks the "Submit" button.

[0957] The terminal transmits the input information to the server.

[0958] The transmitted data packets are checked for correct format.

[0959] input:

[0960] JSON formatted data packet

[0961] output:

[0962] Data sent to the server

[0963] Step 3: Receive data and prepare for analysis

[0964] Specific behavior:

[0965] The server parses the JSON data received from the terminal.

[0966] The data is formatted for analysis and queries are sent to geographic and population databases to obtain additional data.

[0967] For example, the server obtains geographic information and population data related to the city name "Tokyo."

[0968] input:

[0969] JSON data received from the terminal

[0970] output:

[0971] Formatted analytical data and additional geographic and demographic data

[0972] Step 4: Generate prompts and input them to the AI ​​model

[0973] Specific behavior:

[0974] The server generates a prompt for the generative AI model, such as "Please propose the optimal base station installation plan to expand the communication coverage area in Tokyo. Cost constraints are not strict."

[0975] The server inputs the prompt sentence and analysis data into a generative AI model (e.g., GPT-4).

[0976] input:

[0977] Formatted data for analysis

[0978] Generated prompt statement

[0979] output:

[0980] Input data for generative AI models

[0981] Step 5: Analyze and generate installation plans using generative AI models

[0982] Specific behavior:

[0983] The server receives the optimal base station installation plan proposed by the generative AI model.

[0984] For example, the generated installation plan includes the installation location (Shinjuku area), technical requirements, and estimated costs.

[0985] input:

[0986] Input data for generative AI models

[0987] output:

[0988] Proposed installation plan (location, technical requirements, estimated costs)

[0989] Step 6: Calculate cost estimates

[0990] Specific behavior:

[0991] The server calculates a detailed cost estimate based on the generated installation plan.

[0992] For example, we will calculate the installation costs, operating costs, and necessary hardware and software costs for installing three 5G base stations in the Shinjuku area.

[0993] input:

[0994] Proposed installation plan

[0995] output:

[0996] Detailed cost estimates

[0997] Step 7: Organizing and displaying results

[0998] Specific behavior:

[0999] The server formats the installation plan and cost estimate into a single response package.

[1000] The server returns the response package to the terminal.

[1001] The user can view the generated specific installation plan and cost estimate through the terminal.

[1002] input:

[1003] Detailed cost estimates

[1004] Proposed installation plan

[1005] output:

[1006] Response package (installation plan and cost estimate) returned to the user

[1007] (Application example 1)

[1008] 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."

[1009] In autonomous vehicles, communication interruptions have a significant impact on safety and efficiency. Furthermore, conventional base station installation planning systems face the problem of difficulty in optimizing communication areas taking real-time location information into account. This leads to frequent communication interruptions and information loss due to interruptions as the vehicle moves, creating a need for a system that ensures stable communication and provides an optimal communication environment even while the vehicle is moving.

[1010] 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.

[1011] In this invention, the server includes: means for a user to input information regarding the installation location, purpose, and conditions of a base station; means for a terminal to receive the input information and send it to the server; means for the server to analyze the received information and generate an optimal base station installation plan using a generative AI model; means for the server to return the generated plan to the terminal and display it to the user; means for acquiring vehicle location information in real time and determining the optimal communication area; and means for providing base station installation information based on the optimal communication area. This makes it possible to minimize communication interruptions even while an autonomous vehicle is moving, and to provide a stable communication environment.

[1012] definition statement

[1013] "User" refers to a person who inputs information about the location, purpose, and conditions of a base station.

[1014] "Terminal" refers to a device for receiving information entered by a user and transmitting it to a server.

[1015] "Server" refers to a device or system that analyzes the received information and generates an optimal base station installation plan using a generative AI model.

[1016] "Generative AI model" refers to an artificial intelligence model used to generate optimal base station installation plans based on user input information.

[1017] "Base station installation plan" refers to a plan that includes the installation location and technical requirements of base stations with the aim of optimizing communication areas.

[1018] "Real-time acquisition" refers to the immediate collection of current vehicle location information.

[1019] An "optimal communication area" refers to an area that minimizes communication interruptions and provides a stable communication environment.

[1020] "Installation information" refers to detailed information including the specific installation location and technical requirements of the base station.

[1021] MODE FOR CARRYING OUT THE INVENTION

[1022] The present invention is a system for minimizing communication interruptions caused by the movement of an autonomous vehicle and providing a stable communication environment. This system includes: means for a user to input information regarding the installation location, purpose, and conditions of a base station; means for a terminal to receive the input information and send it to a server; means for the server to analyze the received information and generate an optimal base station installation plan using a generative AI model; means for the server to return the generated plan to the terminal and display it to the user; means for acquiring vehicle location information in real time and determining the optimal communication area; and means for providing base station installation information based on the optimal communication area.

[1023] Specific Embodiments

[1024] 1. User input and its reception:

[1025] In this system, users input information about the location, purpose, and conditions of base stations through a dedicated application. Specifically, the driver of the autonomous vehicle uses a smartphone to input the desired location for base stations and requirements for maintaining stable communications.

[1026] 2. Server analyzes data and uses generated AI models:

[1027] The terminal sends the input information to a server, which then analyzes the received information. At this time, a generative AI model is used to generate an optimal base station installation plan. The generative AI model used includes the GPT model. This allows the optimal communication area to be determined based on the route the vehicle will travel.

[1028] 3. Real-time location information and optimal coverage:

[1029] The vehicle's GPS device and communication module acquire the vehicle's location information in real time. Based on this location information, the server determines the optimal communication area and provides that information to the vehicle's terminal. This minimizes communication interruptions even when the vehicle is moving.

[1030] 4. Providing optimal base station installation plans:

[1031] The system generates an optimal base station installation plan and provides it to the user with a cost estimate, including the base station installation location, technical requirements, estimated costs, etc. For example, based on the conditions "Tokyo," "expanded communication coverage area," and "no strict cost constraints," the system presents a plan for installing three 5G base stations in the Shinjuku area and displays the estimated cost.

[1032] Hardware and software used

[1033] Hardware: GPS receiver installed in the autonomous vehicle, smartphone

[1034] Software: Python as the execution environment, the requests library for receiving and sending data, the geopy library for geographic information processing, and a generative AI model (e.g., GPT model).

[1035] Examples of prompt statements

[1036] Based on the current location (35.6895, 139.6917) and the destination location (35.6895, 139.7000), propose an optimal base station installation plan. Communication stability is the most important factor, and cost constraints are not taken into consideration. The output should include the installation location and distance of each base station.

[1037] As described above, the present invention is effective as a means for ensuring the stability of communications for autonomous vehicles, and provides a system that optimizes the communications environment by providing users with base station installation plans.

[1038] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1039] Program processing flow

[1040] Step 1:

[1041] Users input information about the location, purpose, and conditions of the base station installation through a dedicated application. This input data includes the desired installation location and requirements for maintaining communication stability. The input data is expressed in text and numerical format. For example, information such as "Tokyo," "expanding communication coverage area," and "cost constraints are not strict" can be entered.

[1042] Step 2:

[1043] The terminal receives the information entered by the user and sends it to the server. The input data is formatted and sent over the network to the server. At this stage, it is confirmed that the input information reaches the server properly.

[1044] Step 3:

[1045] The server receives the received information and retrieves additional data as needed. This additional data is retrieved from geographic and population databases and combined with the input data. The input data and additional data are then used to format the data for analysis. Specifically, geographic location data and population density information are collected.

[1046] Step 4:

[1047] The server inputs the formatted data into a generative AI model to generate an optimal base station installation plan. The generative AI model (e.g., a GPT model) analyzes the input data and generates a detailed plan including installation locations, technical requirements, estimated costs, etc. In this process, the AI ​​model uses historical data and predictive algorithms to generate the most efficient plan.

[1048] Step 5:

[1049] The server calculates a cost estimate based on the generated installation plan, including installation costs, operating costs, and the costs of required hardware and software for each base station, using a predefined cost database for this calculation.

[1050] Step 6:

[1051] The server then formats the installation plan and cost estimate into a single response package and sends it back to the terminal, where the information is processed and displayed to the user, showing the installation location, estimated costs, technical requirements, and other information on the user's screen.

[1052] Step 7:

[1053] Vehicle location information is acquired in real time and sent to the server. The vehicle's current location is periodically acquired using the GPS device and communication module installed in the vehicle. Based on this location information, the server determines the optimal communication area.

[1054] Step 8:

[1055] The server uses the vehicle's location information to provide base station installation information based on the optimal communication area. The installation plan generated by the generative AI model is linked to real-time location information and sends instructions to the device to maintain the optimal communication area. The communication area is dynamically adjusted based on the input current location information, minimizing communication interruptions.

[1056] The above steps will enable the realization of a system that provides optimal base station installation plans while ensuring stable communications for autonomous vehicles, significantly improving the safety and efficiency of autonomous vehicles.

[1057] 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.

[1058] This invention relates to a system that proposes optimal plans for installing and improving base stations, and in particular, a system that provides more advanced services by combining an emotion engine that recognizes user emotions. This system uses a generative AI model based on the user's input information and emotional state to generate an optimal installation plan and present it to the user.

[1059] A natural language description of the program's processing

[1060] 1. User Input

[1061] Through a web or mobile application, users input information about the installation of base stations, such as the desired location (e.g., "Tokyo"), the purpose of the installation (e.g., "expanding communication coverage"), and conditions (e.g., "cost constraints are not strict").

[1062] 2. User Emotion Recognition

[1063] As users input information, the emotion engine detects emotions from their facial expressions and voice in real time, using specific algorithms to analyze their emotional states, such as joy, surprise, sadness, and anger.

[1064] 3. Data transmission

[1065] When the user finishes inputting information, the device sends the input information and the emotion data detected by the emotion engine to the server, including the city name, purpose, condition, and emotional state.

[1066] 4. Receiving data and preparing for analysis

[1067] The server receives the data sent by the device and formats it correctly, converting it if necessary if the data format is different, and retrieving additional information from geographic and population databases.

[1068] 5. Analysis using AI models

[1069] The server then analyzes the formatted data and the added emotion data using a generative AI model, which generates an optimal base station installation plan based on past data and current technical parameters.

[1070] 6. Generating the optimal plan

[1071] Based on the analysis results, the generative AI model creates an optimal installation plan, including suitable installation locations, the number of base stations required, technical specifications, cost estimates, etc. It also takes into account the user's emotional data to generate explanations and additional suggestions for the user.

[1072] 7. Calculating cost estimates

[1073] The server calculates a cost estimate based on the installation plan, which includes equipment costs, infrastructure costs, and operation and maintenance costs.

[1074] 8. Organizing and displaying results

[1075] The server assembles the installation plan, cost estimate, and additional suggestions based on the user's emotional state into a response package that includes details of the plan, a map, a cost analysis, and actions based on the emotional state.

[1076] 9. Return of results

[1077] The server returns a response package to the terminal, which receives the returned data and displays it to the user.

[1078] 10. User Confirmation

[1079] The user can use the terminal to view the installation plan, cost estimate, and additional proposals displayed, and can enter new criteria to refine the plan as needed and resubmit it.

[1080] Specific examples

[1081] As an example, consider the installation of a new 5G base station in Tokyo.

[1082] 1. User Input and Emotion Recognition

[1083] As the user types in "Tokyo," "expanded communication coverage," and "cost constraints are not strict," the emotion engine detects feelings of hope and relief from the user's facial expression.

[1084] 2. Data transmission

[1085] The terminal transmits the information and emotion data to the server.

[1086] 3. Receiving data and preparing for sentiment analysis

[1087] The server receives the data and retrieves detailed information about Tokyo from geographic information and population databases.

[1088] 4. Analysis using AI models

[1089] The server uses the generated AI model to generate an explanation based on the optimal installation location (for example, the Shinjuku area), the technical requirements of each base station, and user sentiment.

[1090] 5. Calculating cost estimates

[1091] The server calculates the installation costs, operating costs, hardware and software costs.

[1092] 6. Organizing and displaying results

[1093] The server generates an installation plan, a cost estimate, and additional emotion-based explanations, which are then sent back to the device.

[1094] 7. User Confirmation

[1095] The user views and confirms on the device the specific plan, cost estimate, and additional instructions for installing three 5G base stations in the Shinjuku area.

[1096] As described above, the present invention provides a system that allows users to easily obtain optimal plans for installing and improving base stations. In particular, by using an emotion engine, it is possible to deliver proposals optimized for the user's emotional state, improving the user experience.

[1097] The processing flow will be explained below.

[1098] Step 1:

[1099] The user launches a web or mobile application and inputs the necessary information for base station installation, such as "Tokyo," "expanded communication coverage area," and "cost constraints are not strict."

[1100] Step 2:

[1101] As users input information, the device's built-in emotion engine detects emotions in real time from the user's facial expressions and voice, using algorithms to analyze emotional states such as joy, surprise, sadness, and anger.

[1102] Step 3:

[1103] When the user finishes inputting, the device sends the input information and the emotion data detected by the emotion engine to the server. The data is formatted in a specific data format (e.g., JSON format).

[1104] Step 4:

[1105] The server receives the data sent from the device, including the city name, purpose, condition, and emotional state.

[1106] Step 5:

[1107] The server formats the received data for analysis, and performs any necessary conversions if the data format is different.

[1108] Step 6:

[1109] The server retrieves detailed geographic and population data for Tokyo from geographic and population databases, and in the process collects additional data using the necessary APIs.

[1110] Step 7:

[1111] The server inputs the formatted data and the added emotion data into a generative AI model for analysis. The generative AI model generates an optimal base station installation plan based on past data and current technical data.

[1112] Step 8:

[1113] Based on the analysis results, the generative AI model creates an optimal installation plan, including suitable installation locations, the number of base stations required, technical specifications, and estimated costs.

[1114] Step 9:

[1115] The server calculates a cost estimate based on the generated installation plan, including equipment costs, infrastructure costs, and operation and maintenance costs.

[1116] Step 10:

[1117] The server assembles the installation plan, cost estimate, and additional instructions and suggestions based on the user's emotional state into a response package that includes details of the plan, a map, a cost analysis, and actions based on the emotional state.

[1118] Step 11:

[1119] The server returns a response package to the terminal, which receives the returned data and displays it to the user.

[1120] Step 12:

[1121] The user can use the terminal to view the displayed installation plan, cost estimate, and additional instructions and suggestions, and can also enter new requirements to refine the plan if necessary and resubmit it.

[1122] Example 2

[1123] 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."

[1124] Conventional base station installation planning systems generate optimal installation plans based on user input information, but they are unable to consider the user's feelings and preferences, resulting in a lack of improvement in the user experience. Furthermore, simply collecting and analyzing information often does not allow for flexible adaptation to diverse environments and conditions. This can result in reduced installation plan accuracy and reduced user satisfaction.

[1125] 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.

[1126] In this invention, the server includes means for collecting emotional data along with input information using an emotion engine that recognizes the user's emotional state, means for analyzing the received information and emotional data and generating an optimal base station installation plan using a generative AI model, and means for acquiring and analyzing additional data by linking the received data with a geographic information database and a population database. This allows the server to generate an optimal installation plan that takes the user's emotions into consideration and is also able to flexibly respond to a variety of environments and conditions.

[1127] "User" refers to a person who inputs information about the location, purpose, and conditions of a base station.

[1128] "Emotion engine" refers to software or hardware technology for detecting and analyzing emotions from a user's facial expressions and voice in real time.

[1129] "Terminal" refers to an electronic device that runs web applications and mobile applications used by a user.

[1130] "Server" refers to a computer system for receiving and analyzing information and emotion data sent by a user.

[1131] "Generative AI model" refers to an artificial intelligence algorithm that generates optimal base station installation plans based on past data and technical parameters.

[1132] "Geographic information database" refers to a database that stores and provides geographic information about a specific area.

[1133] "Population Database" means a database that stores and provides demographic information for a particular geographic area.

[1134] "Installation Plan" means a plan for installing Base Stations that includes the optimum locations, the number of Base Stations required, technical specifications, and cost estimates.

[1135] "Cost estimate" refers to the total amount calculated based on equipment costs, infrastructure costs, operation and maintenance costs, etc. for installing a base station.

[1136] A "response package" refers to a data package that includes an optimal installation plan, cost estimates, and additional proposals based on sentiment data.

[1137] The present invention relates to a system that proposes an optimal plan for base station installation, incorporating an emotion engine that recognizes user emotions. This system utilizes a generative AI model to generate an optimal installation plan based on information such as installation location, purpose, and conditions entered by the user, as well as the user's emotional state, and presents the plan to the user. Specific embodiments of this system are described below.

[1138] First, the user uses a web or mobile application to input the necessary information regarding the installation of the base station. Specifically, the user inputs the desired installation location (e.g., "Tokyo"), the purpose of the installation (e.g., "expanding communication coverage"), and conditions (e.g., "cost constraints are not strict"), etc. During this process, an emotion engine built into the application analyzes the user's facial expressions and voice in real time to detect their emotional state. The emotion engine uses facial recognition and voice analysis technology to determine emotions such as joy or anxiety expressed by the user's facial expression.

[1139] When the user finishes entering information, the device sends the information entered by the user and the emotion data detected by the emotion engine to the server. The server receives the received information and emotion data and performs the necessary formatting and analysis. Specifically, the server unifies data formats and appropriately converts incomplete or data with different formats. It also obtains additional information from geographic information databases and population databases to prepare for comprehensive data analysis.

[1140] The server then uses a generative AI model to generate an optimal base station installation plan based on the formatted input information and additional emotional data. The generative AI model analyzes past installation data and current technical parameters to propose an installation plan optimized for the user's needs and emotional state. This plan includes the optimal installation location, the required number of base stations, technical specifications, and cost estimates. It also generates explanations and additional suggestions that take the user's emotional state into consideration.

[1141] The generated installation plan is sent back from the server to the device and displayed to the user. The device displays the received data in an easy-to-read format, allowing the user to review the installation plan, cost estimates, and additional proposals. If necessary, the user can adjust the plan by entering new conditions and submitting it again.

[1142] As a concrete example, consider the installation of a new 5G base station in Tokyo. The user inputs "Tokyo," "expanded communication coverage," and "cost constraints are not strict," and the emotion engine detects the emotion of joy from the user's facial expression while they are typing. The device then sends this data to the server, which uses a generative AI model to generate an optimal installation plan. For example, the server might suggest the Shinjuku area as the optimal installation location and build a plan that includes the required number of base stations, technical specifications, cost estimates, and more. Additionally, additional explanations are generated to provide a sense of security based on the user's emotional state. This information is then sent back to the device for the user to confirm.

[1143] An example of a prompt is as follows:

[1144] "I would like to install a new 5G base station in Tokyo. The goal is to expand communication coverage, and cost constraints are not strict." Also, if the user smiles while typing, the emotion engine will detect hopeful emotions.

[1145] The above is a specific embodiment for carrying out the present invention. This system can generate an optimal base station installation plan that takes into account the emotional state of the user, thereby improving the user experience.

[1146] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1147] Step 1:

[1148] The user launches a web or mobile application and inputs information about the base station installation, such as the desired installation location (e.g., "Tokyo"), the purpose of the installation (e.g., "expanding communication coverage"), and conditions (e.g., "cost constraints are not strict"). The input data is temporarily stored within the application.

[1149] Input: desired installation location, purpose, conditions

[1150] Output: Save input information

[1151] Step 2:

[1152] As the user enters information, the emotion engine analyzes the user's facial expressions and voice in real time to detect their emotional state. The emotion engine uses facial recognition and voice analysis technologies to determine emotions such as joy or anxiety from the user's facial expressions and voice. This generates emotion data based on the user's input behavior.

[1153] Input: User's facial expression, voice

[1154] Output: Detected emotion data

[1155] Step 3:

[1156] Once the user has finished inputting all the information, the terminal transmits the input information and the emotion data generated by the emotion engine to the server, where the data is packaged in an appropriate format.

[1157] Input: Input information, emotion data

[1158] Output: Send data to the server

[1159] Step 4:

[1160] The server receives the data sent from the devices and performs the necessary formatting and analysis, standardizing the data format and converting incomplete or differently formatted data, and obtaining additional information from geographic and population databases to prepare for comprehensive data analysis.

[1161] Input: Send data

[1162] Output: formatted data, additional data

[1163] Step 5:

[1164] The server generates an optimal base station installation plan using a generative AI model based on the formatted input information and additional emotional data. The generative AI model analyzes past installation data and current technical parameters to generate an installation plan optimized for the user's needs and emotional state.

[1165] Input: Formatted data, additional data

[1166] Output: Optimal installation plan

[1167] Step 6:

[1168] The generative AI model generates an installation plan based on the analysis, including the optimal installation location, the number of base stations required, technical specifications, and cost estimates. It also adds explanations and additional suggestions that take into account the user's emotional state. The plan is then stored on a server.

[1169] Input: Optimal installation plan

[1170] Output: Complete installation plan package

[1171] Step 7:

[1172] The server returns the completed installation plan package to the terminal.

[1173] Input: Completed installation plan package

[1174] Output: Sending data to the terminal

[1175] Step 8:

[1176] The terminal displays the received installation plan, cost estimate, and additional suggestions in an easy-to-view format for the user.

[1177] Input: Data from the server

[1178] Output: What is displayed to the user

[1179] Step 9:

[1180] The user can review the installation plan, cost estimate, and additional proposals provided, enter new requirements if necessary, and submit again, starting from step 1.

[1181] Input: Presented data, additional user input

[1182] Output: Re-adjusted installation plan

[1183] (Application example 2)

[1184] 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."

[1185] Conventional base station installation plans mainly analyze and propose plans based on user-entered information about installation locations and conditions. However, because these methods do not take into account the user's emotional state, it is difficult to improve the sense of security and satisfaction felt by the user. Furthermore, in industrial settings such as factories, the proposed optimization plans do not reflect the stress and fatigue of workers because they do not take their emotional state into account, which can lead to reduced production efficiency. There is a need for a new system that can solve these issues and improve the user experience.

[1186] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1187] In this invention, the server includes: means for a user to input information regarding the installation location, purpose, and conditions of a base station; means for a terminal to receive the input information and transmit it to the server; means for the server to analyze the received information and emotion data detected by an emotion engine and generate an optimal base station installation plan using a generative AI model; and means for returning the generated plan and cost estimate to the terminal and displaying it to the user. This makes it possible to generate an optimal installation plan that takes the user's emotional state into consideration, thereby improving security and satisfaction as well as production efficiency.

[1188] "User" means any person or entity that uses the System and inputs information.

[1189] "Base station location" means the specific location of new wireless communication equipment to provide communications.

[1190] "Purpose" refers to the specific intention or goal when installing a base station.

[1191] "Conditions" means the constraints, desires and requirements that must be taken into account when installing a base station.

[1192] "Terminal" refers to an electronic device used by a user to input information and display received installation plans.

[1193] "Input information" refers to data that a user provides to the system through a terminal, such as the installation location and conditions.

[1194] "Server" refers to the central processing unit that receives input data, analyzes it, and generates plans using generative AI models.

[1195] An "emotion engine" refers to an analysis system that has an algorithm that detects the emotional state of a user from their facial expressions and voice.

[1196] "Emotion Data" refers to data regarding a user's emotional state as detected by an emotion engine.

[1197] "Generative AI model" refers to an artificial intelligence model that generates optimal installation plans based on past data and current technical parameters.

[1198] "Installation Plan" refers to a plan for optimal base station installation methods and placement created by a generative AI model.

[1199] "Cost estimate" refers to the calculation of the costs involved in installing and maintaining a base station.

[1200] "Display" refers to the presentation of information to inform the user of the generated installation plan and cost estimate.

[1201] This invention relates to a system that proposes optimal plans for base station installation and improvement, and is particularly applicable to the optimization of factory robot production lines. This system uses a generative AI model to generate an optimal installation plan based on the user's input information and emotional state, and presents it to the user.

[1202] Hardware and software used

[1203] Hardware:

[1204] Head-mounted display (HMD) or smart glasses: collects and displays worker input and emotional state.

[1205] Camera and microphone style emotion recognition sensor: Detects the user's emotional state in real time from their facial expressions and voice.

[1206] software:

[1207] Emotion engine: An analysis system equipped with specific algorithms to analyze user emotions.

[1208] Generative AI model: An artificial intelligence model for generating optimal installation plans.

[1209] Geographic and population databases: Databases for capturing detailed information about installation locations.

[1210] Data processing and calculation

[1211] User input:

[1212] Users access the system via a head-mounted display (HMD) or smart glasses and input information about the base station's location, purpose, and conditions. Factory workers also input information about their current work and environment into an operating terminal.

[1213] Emotion recognition:

[1214] The emotion engine uses a camera and microphone to analyze the user's facial expressions and voice in real time during the input process to detect their emotional state, such as "happiness," "surprise," "sadness," "anger," and "relief."

[1215] Data transmission and analysis:

[1216] The device sends the input information and emotion data to the server, which formats the received data into the correct format, performs any necessary conversions, and retrieves additional information from geographic information and population databases to prepare for analysis.

[1217] Generative AI models analyze and plan:

[1218] The server analyzes the formatted data and the added emotion data using a generative AI model, which generates an optimal base station installation plan or production line layout plan based on past data and current technical parameters.

[1219] Viewing Results:

[1220] The server generates an installation plan, a cost estimate, and additional suggestions based on the user's emotional state, and sends it back to the device in a single response package, which is displayed to the user via a head-mounted display or smart glasses.

[1221] Specific examples

[1222] As an example, consider a production line optimization system for a factory that is introducing new machinery to Line C. A factory worker inputs "Introducing new equipment to Line C," and the emotion engine detects "joy" during the process. The generative AI model uses the detected emotion and input data to generate an optimal adjustment plan for the layout and operating hours of Line C and its surrounding areas, and suggests adding appropriate breaks. It also displays a cost estimate of 1,200,000 yen.

[1223] Example prompts to input to a generative AI model:

[1224] User Input: "We're going to install a new line machine."

[1225] Emotion data: "Joy"

[1226] Application example: "Planning the introduction of a new machine on Line C"

[1227] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1228] Step 1:

[1229] The user inputs information about the base station installation location, purpose, and conditions using a head-mounted display (HMD) or smart glasses. At this time, the user inputs specific information such as the "desired installation location," "purpose of installation," and "conditions" into the operating terminal. In the case of factory workers, they also input information about the "work content" and "environment."

[1230] Input: desired installation location, purpose of installation, conditions, work content, environment

[1231] Output: User input information

[1232] Step 2:

[1233] The emotion engine uses a camera and microphone to analyze the user's facial expressions and voice in real time to detect their emotional state, which is classified into categories such as "happiness," "surprise," "sadness," "anger," and "relief."

[1234] Input: User's facial expression, voice

[1235] Output: Emotion data (happiness, surprise, sadness, anger, relief, etc.)

[1236] Step 3:

[1237] The terminal transmits the user's input information and emotion data to the server, which converts the data into an appropriate format and transmits it.

[1238] Input: User input information, emotion data

[1239] Output: Sending formatted data

[1240] Step 4:

[1241] The server formats the received data and retrieves additional information from geographic and population databases. The formatted data and additional information are used to prepare for analysis.

[1242] Input: Received data (user input information, emotion data)

[1243] Output: Analysis-ready data

[1244] Step 5:

[1245] The server analyzes the incoming data and sentiment data using a generative AI model, which generates an optimal installation plan or production line layout plan based on historical data and current technical parameters.

[1246] Input: Analysis-ready data, additional information

[1247] Output: Generated installation plan or production line layout plan

[1248] Step 6:

[1249] The server then assembles the generated plan, cost estimates, and additional suggestions based on the emotional state into a single response package and sends it back to the device.

[1250] Input: Generated installation plan or production line layout plan

[1251] Output: Response package (installation plan, cost estimate, additional proposals)

[1252] Step 7:

[1253] The device receives the returned response package and displays it to the user via a head-mounted display or smart glasses. The user can then review the displayed plan, cost estimate, and additional proposals.

[1254] Input: Response package

[1255] Output: Show optimization plan

[1256] Step 8:

[1257] The user can re-enter the conditions based on the displayed installation plan and cost estimate and send it to the system to readjust the plan.

[1258] Input: Modified condition input

[1259] Output: Reworked installation plan and cost estimate

[1260] 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.

[1261] 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.

[1262] 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.

[1263] [Fourth embodiment]

[1264] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1265] 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.

[1266] 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).

[1267] 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.

[1268] 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.

[1269] 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).

[1270] 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.

[1271] 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.

[1272] 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.

[1273] 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.

[1274] 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.

[1275] 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.

[1276] 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."

[1277] The present invention relates to a system that proposes optimal plans for installing and improving base stations. This system uses a generative AI model based on user input information to generate an optimal installation plan and presents it to the user.

[1278] A natural language description of the program's processing

[1279] 1. User Input

[1280] Through a web or mobile application, users input information about the base station installation, such as the city name (e.g., "Tokyo"), purpose (e.g., "expand communication coverage"), and conditions (e.g., "cost constraints are not strict").

[1281] 2. Data transmission

[1282] When the user finishes inputting information, the terminal transmits the input information to the server, including the city name, purpose, and conditions.

[1283] 3. Receiving data and preparing for analysis

[1284] The server receives the data sent by the device and formats it for analysis, possibly retrieving additional data from geographic and population databases as needed.

[1285] 4. Analysis using AI models

[1286] The server inputs the formatted data into a generative AI model to generate an optimal base station installation plan, including installation locations, technical requirements, estimated costs, etc.

[1287] 5. Calculating cost estimates

[1288] Based on the generated installation plan, the server calculates a cost estimate, which includes the installation costs, operating costs, and required hardware and software costs for each base station.

[1289] 6. Organizing and displaying results

[1290] The server formats the generated installation plan and cost estimate into a single response package and sends it back to the terminal, where the user can view this information.

[1291] Specific examples

[1292] As an example, consider the installation of a new 5G base station in Tokyo.

[1293] 1. User Input

[1294] The user inputs "Tokyo," "expanded communication coverage area," and "cost constraints are not strict."

[1295] 2. Data transmission

[1296] The terminal transmits the information to the server.

[1297] 3. Receiving data and preparing for analysis

[1298] The server receives the data and retrieves detailed geographic information and population data for Tokyo from geographic information and population databases.

[1299] 4. Analysis using AI models

[1300] The server uses the generated AI model to generate the optimal installation location (for example, the Shinjuku area) and the technical requirements for each base station.

[1301] 5. Calculating cost estimates

[1302] The server calculates installation costs (e.g., the cost of installing three 5G base stations in the Shinjuku area), operating costs, and hardware and software costs.

[1303] 6. Organizing and displaying results

[1304] The server formats the generated installation plan and cost estimate and returns it to the device, where the user can view the specific plan and cost estimate for installing three 5G base stations in the Shinjuku area.

[1305] As described above, the present invention is a system that allows users to easily obtain optimal plans for installing and improving base stations. By using a generative AI model, users can efficiently create optimal installation plans even without advanced technical knowledge.

[1306] The processing flow will be explained below.

[1307] Step 1:

[1308] The user launches a web or mobile application and enters the information required for base station installation, such as the desired installation location (e.g., "Tokyo"), the purpose of installation (e.g., "expanding communication coverage"), and conditions (e.g., "cost constraints are not strict").

[1309] Step 2:

[1310] The terminal receives the information entered by the user, including the city name, purpose, and conditions.

[1311] Step 3:

[1312] The terminal sends the received input information to the server, where the data is converted into a predetermined data format (e.g., JSON format).

[1313] Step 4:

[1314] The server receives the data sent from the device and formats it into the correct format, or performs any necessary conversions if the data format is different.

[1315] Step 5:

[1316] The server uses the formatted data to retrieve additional information from geographic and population databases, such as detailed geographic and demographic information for Tokyo.

[1317] Step 6:

[1318] The server inputs the received data and additional data into a generative AI model for analysis, which generates an optimal base station installation plan based on past data and current technical parameters.

[1319] Step 7:

[1320] The generative AI model generates analytical results and creates an optimal installation plan, including suitable installation locations, the number of base stations required, and technical specifications.

[1321] Step 8:

[1322] The server calculates a cost estimate based on the generated installation plan, including equipment costs, infrastructure costs, and operation and maintenance costs.

[1323] Step 9:

[1324] The server assembles the installation plan and cost estimate into a single response package that includes plan details, maps, and cost analysis.

[1325] Step 10:

[1326] The server returns a response package to the terminal, which receives the returned data and displays it to the user.

[1327] Step 11:

[1328] The user can use the terminal to view the installation plan and cost estimate displayed, and if necessary, enter new specifications to refine the plan and submit it again.

[1329] Example 1

[1330] 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."

[1331] Conventional base station installation plan generation systems often require a lot of time and effort to analyze user-entered information and create optimal installation plans based on that information. Furthermore, creating an installation plan can be difficult if the user does not have advanced technical knowledge. Furthermore, the process of integrating multiple data, such as calculating cost estimates and obtaining geographic information, is complicated, making it difficult to proceed efficiently.

[1332] 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.

[1333] In this invention, the server includes a means for formatting the received information into an analytical format, a means for acquiring additional data from a geographic information database and a population database, and a means for inputting the analytical data into a generative AI model to generate an optimal base station installation plan. This allows the base station installation plan to be generated quickly and efficiently based on the user's input information, enabling the user to obtain the optimal installation plan without advanced technical knowledge. Furthermore, cost estimates are calculated automatically, enhancing user convenience.

[1334] "User" refers to an individual or organization that uses the system to input base station installation information.

[1335] A "base station" is a type of wireless communication device that relays communications within a communication network.

[1336] "Installation location" refers to a specific geographic location where a base station is physically located.

[1337] "Purpose" refers to the specific goal or reason for installing a base station.

[1338] "Conditions" refers to restrictions and requirements related to the installation of a base station.

[1339] "Terminal" refers to a device used by a user to communicate input information to a system.

[1340] "Server" refers to the computer system that analyzes the received information and generates an optimal base station installation plan using a generative AI model.

[1341] A "generative AI model" refers to an artificial intelligence algorithm that learns from large amounts of data to perform specific tasks.

[1342] "Geographic information database" refers to a database containing geographic location information.

[1343] "Population Database" refers to a database containing demographic information for a particular geographic area.

[1344] "Analysis format" refers to the data format that converts received information into a format suitable for the generative AI model.

[1345] "Installation Plan" refers to a plan proposed by a generative AI model showing the specific base station placement and related technical requirements.

[1346] A "cost estimate" is a calculation of the costs required to install and operate a base station.

[1347] "Response Package" refers to the data returned to the User that includes an installation plan and cost estimate.

[1348] The present invention is a system that generates an optimal base station installation plan using a generative AI model, where a user inputs the installation location, purpose, and conditions of the base station. To implement this system, a web or mobile application is required, and these applications have the function of sending the user's input information to a server.

[1349] First, the user uses the application's input screen to input the city name, purpose of installation, and conditions. For example, they can enter information such as "Tokyo," "expanding communication coverage area," and "cost constraints are not strict." This input information is sent by the device to the server as a JSON-formatted data packet.

[1350] The server formats the received data for analysis. Specifically, it parses the JSON data and sends queries to geographic information and population databases to obtain the necessary geographic and population data. In this process, the server collects detailed geographic and population data related to the city name "Tokyo."

[1351] Next, the server generates a prompt to input the data into a generative AI model. For example, it generates a sentence such as, "Please propose the optimal base station installation plan to expand communication coverage in Tokyo. Cost constraints are not strict." The data for analysis is input into a generative AI model (e.g., GPT-4) along with this prompt.

[1352] The generative AI model proposes an optimal base station installation plan, which includes the installation location (for example, the Shinjuku area), technical requirements, estimated costs, etc. Based on the generated plan, the server calculates installation costs, operating costs, and the costs of the necessary hardware and software.

[1353] This information is formatted into a single response package and sent back from the server to the terminal. The user can then view the generated specific installation plan and cost estimate through the terminal. This system enables users without advanced technical knowledge to efficiently obtain the optimal base station installation plan.

[1354] This concludes the implementation of the present invention. As an example, consider the case of installing a new 5G base station in Tokyo. The user inputs "Tokyo," "expanded coverage area," and "low cost constraints." Based on this information, the server formats the data and collects any additional data needed. It then uses a generative AI model to generate an optimal installation plan and calculate a cost estimate. Finally, the user can view a specific plan and cost estimate for installing three 5G base stations in the Shinjuku area.

[1355] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1356] Step 1: User enters information

[1357] Specific behavior:

[1358] The user opens an input screen for a web or mobile application.

[1359] The user enters the city name "Tokyo," the purpose "expanding communication coverage area," and the conditions "cost constraints are not strict" into the application's input form.

[1360] The entered data is organized in JSON format.

[1361] input:

[1362] City name: "Tokyo"

[1363] Objective: "Expanding communication coverage"

[1364] Condition: "Cost constraints are not strict"

[1365] output:

[1366] JSON formatted data packet

[1367] Step 2: Sending data

[1368] Specific behavior:

[1369] The user clicks the "Submit" button.

[1370] The terminal transmits the input information to the server.

[1371] The transmitted data packets are checked for correct format.

[1372] input:

[1373] JSON formatted data packet

[1374] output:

[1375] Data sent to the server

[1376] Step 3: Receive data and prepare for analysis

[1377] Specific behavior:

[1378] The server parses the JSON data received from the terminal.

[1379] The data is formatted for analysis and queries are sent to geographic and population databases to obtain additional data.

[1380] For example, the server obtains geographic information and population data related to the city name "Tokyo."

[1381] input:

[1382] JSON data received from the terminal

[1383] output:

[1384] Formatted analytical data and additional geographic and demographic data

[1385] Step 4: Generate prompts and input them to the AI ​​model

[1386] Specific behavior:

[1387] The server generates a prompt for the generative AI model, such as "Please propose the optimal base station installation plan to expand the communication coverage area in Tokyo. Cost constraints are not strict."

[1388] The server inputs the prompt sentence and analysis data into a generative AI model (e.g., GPT-4).

[1389] input:

[1390] Formatted data for analysis

[1391] Generated prompt statement

[1392] output:

[1393] Input data for generative AI models

[1394] Step 5: Analyze and generate installation plans using generative AI models

[1395] Specific behavior:

[1396] The server receives the optimal base station installation plan proposed by the generative AI model.

[1397] For example, the generated installation plan includes the installation location (Shinjuku area), technical requirements, and estimated costs.

[1398] input:

[1399] Input data for generative AI models

[1400] output:

[1401] Proposed installation plan (location, technical requirements, estimated costs)

[1402] Step 6: Calculate cost estimates

[1403] Specific behavior:

[1404] The server calculates a detailed cost estimate based on the generated installation plan.

[1405] For example, we will calculate the installation costs, operating costs, and necessary hardware and software costs for installing three 5G base stations in the Shinjuku area.

[1406] input:

[1407] Proposed installation plan

[1408] output:

[1409] Detailed cost estimates

[1410] Step 7: Organizing and displaying results

[1411] Specific behavior:

[1412] The server formats the installation plan and cost estimate into a single response package.

[1413] The server returns the response package to the terminal.

[1414] The user can view the generated specific installation plan and cost estimate through the terminal.

[1415] input:

[1416] Detailed cost estimates

[1417] Proposed installation plan

[1418] output:

[1419] Response package (installation plan and cost estimate) returned to the user

[1420] (Application example 1)

[1421] 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."

[1422] In autonomous vehicles, communication interruptions have a significant impact on safety and efficiency. Furthermore, conventional base station installation planning systems face the problem of difficulty in optimizing communication areas taking real-time location information into account. This leads to frequent communication interruptions and information loss due to interruptions as the vehicle moves, creating a need for a system that ensures stable communication and provides an optimal communication environment even while the vehicle is moving.

[1423] 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.

[1424] In this invention, the server includes: means for a user to input information regarding the installation location, purpose, and conditions of a base station; means for a terminal to receive the input information and send it to the server; means for the server to analyze the received information and generate an optimal base station installation plan using a generative AI model; means for the server to return the generated plan to the terminal and display it to the user; means for acquiring vehicle location information in real time and determining the optimal communication area; and means for providing base station installation information based on the optimal communication area. This makes it possible to minimize communication interruptions even while an autonomous vehicle is moving, and to provide a stable communication environment.

[1425] definition statement

[1426] "User" refers to a person who inputs information about the location, purpose, and conditions of a base station.

[1427] "Terminal" refers to a device for receiving information entered by a user and transmitting it to a server.

[1428] "Server" refers to a device or system that analyzes the received information and generates an optimal base station installation plan using a generative AI model.

[1429] "Generative AI model" refers to an artificial intelligence model used to generate optimal base station installation plans based on user input information.

[1430] "Base station installation plan" refers to a plan that includes the installation location and technical requirements of base stations with the aim of optimizing communication areas.

[1431] "Real-time acquisition" refers to the immediate collection of current vehicle location information.

[1432] An "optimal communication area" refers to an area that minimizes communication interruptions and provides a stable communication environment.

[1433] "Installation information" refers to detailed information including the specific installation location and technical requirements of the base station.

[1434] MODE FOR CARRYING OUT THE INVENTION

[1435] The present invention is a system for minimizing communication interruptions caused by the movement of an autonomous vehicle and providing a stable communication environment. This system includes: means for a user to input information regarding the installation location, purpose, and conditions of a base station; means for a terminal to receive the input information and send it to a server; means for the server to analyze the received information and generate an optimal base station installation plan using a generative AI model; means for the server to return the generated plan to the terminal and display it to the user; means for acquiring vehicle location information in real time and determining the optimal communication area; and means for providing base station installation information based on the optimal communication area.

[1436] Specific Embodiments

[1437] 1. User input and its reception:

[1438] In this system, users input information about the location, purpose, and conditions of base stations through a dedicated application. Specifically, the driver of the autonomous vehicle uses a smartphone to input the desired location for base stations and requirements for maintaining stable communications.

[1439] 2. Server analyzes data and uses generated AI models:

[1440] The terminal sends the input information to a server, which then analyzes the received information. At this time, a generative AI model is used to generate an optimal base station installation plan. The generative AI model used includes the GPT model. This allows the optimal communication area to be determined based on the route the vehicle will travel.

[1441] 3. Real-time location information and optimal coverage:

[1442] The vehicle's GPS device and communication module acquire the vehicle's location information in real time. Based on this location information, the server determines the optimal communication area and provides that information to the vehicle's terminal. This minimizes communication interruptions even when the vehicle is moving.

[1443] 4. Providing optimal base station installation plans:

[1444] The system generates an optimal base station installation plan and provides it to the user with a cost estimate, including the base station installation location, technical requirements, estimated costs, etc. For example, based on the conditions "Tokyo," "expanded communication coverage area," and "no strict cost constraints," the system presents a plan for installing three 5G base stations in the Shinjuku area and displays the estimated cost.

[1445] Hardware and software used

[1446] Hardware: GPS receiver installed in the autonomous vehicle, smartphone

[1447] Software: Python as the execution environment, the requests library for receiving and sending data, the geopy library for geographic information processing, and a generative AI model (e.g., GPT model).

[1448] Examples of prompt statements

[1449] Based on the current location (35.6895, 139.6917) and the destination location (35.6895, 139.7000), propose an optimal base station installation plan. Communication stability is the most important factor, and cost constraints are not taken into consideration. The output should include the installation location and distance of each base station.

[1450] As described above, the present invention is effective as a means for ensuring the stability of communications for autonomous vehicles, and provides a system that optimizes the communications environment by providing users with base station installation plans.

[1451] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1452] Program processing flow

[1453] Step 1:

[1454] Users input information about the location, purpose, and conditions of the base station installation through a dedicated application. This input data includes the desired installation location and requirements for maintaining communication stability. The input data is expressed in text and numerical format. For example, information such as "Tokyo," "expanding communication coverage area," and "cost constraints are not strict" can be entered.

[1455] Step 2:

[1456] The terminal receives the information entered by the user and sends it to the server. The input data is formatted and sent over the network to the server. At this stage, it is confirmed that the input information reaches the server properly.

[1457] Step 3:

[1458] The server receives the received information and retrieves additional data as needed. This additional data is retrieved from geographic and population databases and combined with the input data. The input data and additional data are then used to format the data for analysis. Specifically, geographic location data and population density information are collected.

[1459] Step 4:

[1460] The server inputs the formatted data into a generative AI model to generate an optimal base station installation plan. The generative AI model (e.g., a GPT model) analyzes the input data and generates a detailed plan including installation locations, technical requirements, estimated costs, etc. In this process, the AI ​​model uses historical data and predictive algorithms to generate the most efficient plan.

[1461] Step 5:

[1462] The server calculates a cost estimate based on the generated installation plan, including installation costs, operating costs, and the costs of required hardware and software for each base station, using a predefined cost database for this calculation.

[1463] Step 6:

[1464] The server then formats the installation plan and cost estimate into a single response package and sends it back to the terminal, where the information is processed and displayed to the user, showing the installation location, estimated costs, technical requirements, and other information on the user's screen.

[1465] Step 7:

[1466] Vehicle location information is acquired in real time and sent to the server. The vehicle's current location is periodically acquired using the GPS device and communication module installed in the vehicle. Based on this location information, the server determines the optimal communication area.

[1467] Step 8:

[1468] The server uses the vehicle's location information to provide base station installation information based on the optimal communication area. The installation plan generated by the generative AI model is linked to real-time location information and sends instructions to the device to maintain the optimal communication area. The communication area is dynamically adjusted based on the input current location information, minimizing communication interruptions.

[1469] The above steps will enable the realization of a system that provides optimal base station installation plans while ensuring stable communications for autonomous vehicles, significantly improving the safety and efficiency of autonomous vehicles.

[1470] 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.

[1471] This invention relates to a system that proposes optimal plans for installing and improving base stations, and in particular, a system that provides more advanced services by combining an emotion engine that recognizes user emotions. This system uses a generative AI model based on the user's input information and emotional state to generate an optimal installation plan and present it to the user.

[1472] A natural language description of the program's processing

[1473] 1. User Input

[1474] Through a web or mobile application, users input information about the installation of base stations, such as the desired location (e.g., "Tokyo"), the purpose of the installation (e.g., "expanding communication coverage"), and conditions (e.g., "cost constraints are not strict").

[1475] 2. User Emotion Recognition

[1476] As users input information, the emotion engine detects emotions from their facial expressions and voice in real time, using specific algorithms to analyze their emotional states, such as joy, surprise, sadness, and anger.

[1477] 3. Data transmission

[1478] When the user finishes inputting information, the device sends the input information and the emotion data detected by the emotion engine to the server, including the city name, purpose, condition, and emotional state.

[1479] 4. Receiving data and preparing for analysis

[1480] The server receives the data sent by the device and formats it correctly, converting it if necessary if the data format is different, and retrieving additional information from geographic and population databases.

[1481] 5. Analysis using AI models

[1482] The server then analyzes the formatted data and the added emotion data using a generative AI model, which generates an optimal base station installation plan based on past data and current technical parameters.

[1483] 6. Generating the optimal plan

[1484] Based on the analysis results, the generative AI model creates an optimal installation plan, including suitable installation locations, the number of base stations required, technical specifications, cost estimates, etc. It also takes into account the user's emotional data to generate explanations and additional suggestions for the user.

[1485] 7. Calculating cost estimates

[1486] The server calculates a cost estimate based on the installation plan, which includes equipment costs, infrastructure costs, and operation and maintenance costs.

[1487] 8. Organizing and displaying results

[1488] The server assembles the installation plan, cost estimate, and additional suggestions based on the user's emotional state into a response package that includes details of the plan, a map, a cost analysis, and actions based on the emotional state.

[1489] 9. Return of results

[1490] The server returns a response package to the terminal, which receives the returned data and displays it to the user.

[1491] 10. User Confirmation

[1492] The user can use the terminal to view the installation plan, cost estimate, and additional proposals displayed, and can enter new criteria to refine the plan as needed and resubmit it.

[1493] Specific examples

[1494] As an example, consider the installation of a new 5G base station in Tokyo.

[1495] 1. User Input and Emotion Recognition

[1496] As the user types in "Tokyo," "expanded communication coverage," and "cost constraints are not strict," the emotion engine detects feelings of hope and relief from the user's facial expression.

[1497] 2. Data transmission

[1498] The terminal transmits the information and emotion data to the server.

[1499] 3. Receiving data and preparing for sentiment analysis

[1500] The server receives the data and retrieves detailed information about Tokyo from geographic information and population databases.

[1501] 4. Analysis using AI models

[1502] The server uses the generated AI model to generate an explanation based on the optimal installation location (for example, the Shinjuku area), the technical requirements of each base station, and user sentiment.

[1503] 5. Calculating cost estimates

[1504] The server calculates the installation costs, operating costs, hardware and software costs.

[1505] 6. Organizing and displaying results

[1506] The server generates an installation plan, a cost estimate, and additional emotion-based explanations, which are then sent back to the device.

[1507] 7. User Confirmation

[1508] The user views and confirms on the device the specific plan, cost estimate, and additional instructions for installing three 5G base stations in the Shinjuku area.

[1509] As described above, the present invention provides a system that allows users to easily obtain optimal plans for installing and improving base stations. In particular, by using an emotion engine, it is possible to deliver proposals optimized for the user's emotional state, improving the user experience.

[1510] The processing flow will be explained below.

[1511] Step 1:

[1512] The user launches a web or mobile application and inputs the necessary information for base station installation, such as "Tokyo," "expanded communication coverage area," and "cost constraints are not strict."

[1513] Step 2:

[1514] As users input information, the device's built-in emotion engine detects emotions in real time from the user's facial expressions and voice, using algorithms to analyze emotional states such as joy, surprise, sadness, and anger.

[1515] Step 3:

[1516] When the user finishes inputting, the device sends the input information and the emotion data detected by the emotion engine to the server. The data is formatted in a specific data format (e.g., JSON format).

[1517] Step 4:

[1518] The server receives the data sent from the device, including the city name, purpose, condition, and emotional state.

[1519] Step 5:

[1520] The server formats the received data for analysis, and performs any necessary conversions if the data format is different.

[1521] Step 6:

[1522] The server retrieves detailed geographic and population data for Tokyo from geographic and population databases, and in the process collects additional data using the necessary APIs.

[1523] Step 7:

[1524] The server inputs the formatted data and the added emotion data into a generative AI model for analysis. The generative AI model generates an optimal base station installation plan based on past data and current technical data.

[1525] Step 8:

[1526] Based on the analysis results, the generative AI model creates an optimal installation plan, including suitable installation locations, the number of base stations required, technical specifications, and estimated costs.

[1527] Step 9:

[1528] The server calculates a cost estimate based on the generated installation plan, including equipment costs, infrastructure costs, and operation and maintenance costs.

[1529] Step 10:

[1530] The server assembles the installation plan, cost estimate, and additional instructions and suggestions based on the user's emotional state into a response package that includes details of the plan, a map, a cost analysis, and actions based on the emotional state.

[1531] Step 11:

[1532] The server returns a response package to the terminal, which receives the returned data and displays it to the user.

[1533] Step 12:

[1534] The user can use the terminal to view the displayed installation plan, cost estimate, and additional instructions and suggestions, and can also enter new requirements to refine the plan if necessary and resubmit it.

[1535] Example 2

[1536] 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."

[1537] Conventional base station installation planning systems generate optimal installation plans based on user input information, but they are unable to consider the user's feelings and preferences, resulting in a lack of improvement in the user experience. Furthermore, simply collecting and analyzing information often does not allow for flexible adaptation to diverse environments and conditions. This can result in reduced installation plan accuracy and reduced user satisfaction.

[1538] 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.

[1539] In this invention, the server includes means for collecting emotional data along with input information using an emotion engine that recognizes the user's emotional state, means for analyzing the received information and emotional data and generating an optimal base station installation plan using a generative AI model, and means for acquiring and analyzing additional data by linking the received data with a geographic information database and a population database. This allows the server to generate an optimal installation plan that takes the user's emotions into consideration and is also able to flexibly respond to a variety of environments and conditions.

[1540] "User" refers to a person who inputs information about the location, purpose, and conditions of a base station.

[1541] "Emotion engine" refers to software or hardware technology for detecting and analyzing emotions from a user's facial expressions and voice in real time.

[1542] "Terminal" refers to an electronic device that runs web applications and mobile applications used by a user.

[1543] "Server" refers to a computer system for receiving and analyzing information and emotion data sent by a user.

[1544] "Generative AI model" refers to an artificial intelligence algorithm that generates optimal base station installation plans based on past data and technical parameters.

[1545] "Geographic information database" refers to a database that stores and provides geographic information about a specific area.

[1546] "Population Database" means a database that stores and provides demographic information for a particular geographic area.

[1547] "Installation Plan" means a plan for installing Base Stations that includes the optimum locations, the number of Base Stations required, technical specifications, and cost estimates.

[1548] "Cost estimate" refers to the total amount calculated based on equipment costs, infrastructure costs, operation and maintenance costs, etc. for installing a base station.

[1549] A "response package" refers to a data package that includes an optimal installation plan, cost estimates, and additional proposals based on sentiment data.

[1550] The present invention relates to a system that proposes an optimal plan for base station installation, incorporating an emotion engine that recognizes user emotions. This system utilizes a generative AI model to generate an optimal installation plan based on information such as installation location, purpose, and conditions entered by the user, as well as the user's emotional state, and presents the plan to the user. Specific embodiments of this system are described below.

[1551] First, the user uses a web or mobile application to input the necessary information regarding the installation of the base station. Specifically, the user inputs the desired installation location (e.g., "Tokyo"), the purpose of the installation (e.g., "expanding communication coverage"), and conditions (e.g., "cost constraints are not strict"), etc. During this process, an emotion engine built into the application analyzes the user's facial expressions and voice in real time to detect their emotional state. The emotion engine uses facial recognition and voice analysis technology to determine emotions such as joy or anxiety expressed by the user's facial expression.

[1552] When the user finishes entering information, the device sends the information entered by the user and the emotion data detected by the emotion engine to the server. The server receives the received information and emotion data and performs the necessary formatting and analysis. Specifically, the server unifies data formats and appropriately converts incomplete or data with different formats. It also obtains additional information from geographic information databases and population databases to prepare for comprehensive data analysis.

[1553] The server then uses a generative AI model to generate an optimal base station installation plan based on the formatted input information and additional emotional data. The generative AI model analyzes past installation data and current technical parameters to propose an installation plan optimized for the user's needs and emotional state. This plan includes the optimal installation location, the required number of base stations, technical specifications, and cost estimates. It also generates explanations and additional suggestions that take the user's emotional state into consideration.

[1554] The generated installation plan is sent back from the server to the device and displayed to the user. The device displays the received data in an easy-to-read format, allowing the user to review the installation plan, cost estimates, and additional proposals. If necessary, the user can adjust the plan by entering new conditions and submitting it again.

[1555] As a concrete example, consider the installation of a new 5G base station in Tokyo. The user inputs "Tokyo," "expanded communication coverage," and "cost constraints are not strict," and the emotion engine detects the emotion of joy from the user's facial expression while they are typing. The device then sends this data to the server, which uses a generative AI model to generate an optimal installation plan. For example, the server might suggest the Shinjuku area as the optimal installation location and build a plan that includes the required number of base stations, technical specifications, cost estimates, and more. Additionally, additional explanations are generated to provide a sense of security based on the user's emotional state. This information is then sent back to the device for the user to confirm.

[1556] An example of a prompt is as follows:

[1557] "I would like to install a new 5G base station in Tokyo. The goal is to expand communication coverage, and cost constraints are not strict." Also, if the user smiles while typing, the emotion engine will detect hopeful emotions.

[1558] The above is a specific embodiment for carrying out the present invention. This system can generate an optimal base station installation plan that takes into account the emotional state of the user, thereby improving the user experience.

[1559] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1560] Step 1:

[1561] The user launches a web or mobile application and inputs information about the base station installation, such as the desired installation location (e.g., "Tokyo"), the purpose of the installation (e.g., "expanding communication coverage"), and conditions (e.g., "cost constraints are not strict"). The input data is temporarily stored within the application.

[1562] Input: desired installation location, purpose, conditions

[1563] Output: Save input information

[1564] Step 2:

[1565] As the user enters information, the emotion engine analyzes the user's facial expressions and voice in real time to detect their emotional state. The emotion engine uses facial recognition and voice analysis technologies to determine emotions such as joy or anxiety from the user's facial expressions and voice. This generates emotion data based on the user's input behavior.

[1566] Input: User's facial expression, voice

[1567] Output: Detected emotion data

[1568] Step 3:

[1569] Once the user has finished inputting all the information, the terminal transmits the input information and the emotion data generated by the emotion engine to the server, where the data is packaged in an appropriate format.

[1570] Input: Input information, emotion data

[1571] Output: Send data to the server

[1572] Step 4:

[1573] The server receives the data sent from the devices and performs the necessary formatting and analysis, standardizing the data format and converting incomplete or differently formatted data, and obtaining additional information from geographic and population databases to prepare for comprehensive data analysis.

[1574] Input: Send data

[1575] Output: formatted data, additional data

[1576] Step 5:

[1577] The server generates an optimal base station installation plan using a generative AI model based on the formatted input information and additional emotional data. The generative AI model analyzes past installation data and current technical parameters to generate an installation plan optimized for the user's needs and emotional state.

[1578] Input: Formatted data, additional data

[1579] Output: Optimal installation plan

[1580] Step 6:

[1581] The generative AI model generates an installation plan based on the analysis, including the optimal installation location, the number of base stations required, technical specifications, and cost estimates. It also adds explanations and additional suggestions that take into account the user's emotional state. The plan is then stored on a server.

[1582] Input: Optimal installation plan

[1583] Output: Complete installation plan package

[1584] Step 7:

[1585] The server returns the completed installation plan package to the terminal.

[1586] Input: Completed installation plan package

[1587] Output: Sending data to the terminal

[1588] Step 8:

[1589] The terminal displays the received installation plan, cost estimate, and additional suggestions in an easy-to-view format for the user.

[1590] Input: Data from the server

[1591] Output: What is displayed to the user

[1592] Step 9:

[1593] The user can review the installation plan, cost estimate, and additional proposals provided, enter new requirements if necessary, and submit again, starting from step 1.

[1594] Input: Presented data, additional user input

[1595] Output: Re-adjusted installation plan

[1596] (Application example 2)

[1597] 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."

[1598] Conventional base station installation plans mainly analyze and propose plans based on user-entered information about installation locations and conditions. However, because these methods do not take into account the user's emotional state, it is difficult to improve the sense of security and satisfaction felt by the user. Furthermore, in industrial settings such as factories, the proposed optimization plans do not reflect the stress and fatigue of workers because they do not take their emotional state into account, which can lead to reduced production efficiency. There is a need for a new system that can solve these issues and improve the user experience.

[1599] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1600] In this invention, the server includes: means for a user to input information regarding the installation location, purpose, and conditions of a base station; means for a terminal to receive the input information and transmit it to the server; means for the server to analyze the received information and emotion data detected by an emotion engine and generate an optimal base station installation plan using a generative AI model; and means for returning the generated plan and cost estimate to the terminal and displaying it to the user. This makes it possible to generate an optimal installation plan that takes the user's emotional state into consideration, thereby improving security and satisfaction as well as production efficiency.

[1601] "User" means any person or entity that uses the System and inputs information.

[1602] "Base station location" means the specific location of new wireless communication equipment to provide communications.

[1603] "Purpose" refers to the specific intention or goal when installing a base station.

[1604] "Conditions" means the constraints, desires and requirements that must be taken into account when installing a base station.

[1605] "Terminal" refers to an electronic device used by a user to input information and display received installation plans.

[1606] "Input information" refers to data that a user provides to the system through a terminal, such as the installation location and conditions.

[1607] "Server" refers to the central processing unit that receives input data, analyzes it, and generates plans using generative AI models.

[1608] An "emotion engine" refers to an analysis system that has an algorithm that detects the emotional state of a user from their facial expressions and voice.

[1609] "Emotion Data" refers to data regarding a user's emotional state as detected by an emotion engine.

[1610] "Generative AI model" refers to an artificial intelligence model that generates optimal installation plans based on past data and current technical parameters.

[1611] "Installation Plan" refers to a plan for optimal base station installation methods and placement created by a generative AI model.

[1612] "Cost estimate" refers to the calculation of the costs involved in installing and maintaining a base station.

[1613] "Display" refers to the presentation of information to inform the user of the generated installation plan and cost estimate.

[1614] This invention relates to a system that proposes optimal plans for base station installation and improvement, and is particularly applicable to the optimization of factory robot production lines. This system uses a generative AI model to generate an optimal installation plan based on the user's input information and emotional state, and presents it to the user.

[1615] Hardware and software used

[1616] Hardware:

[1617] Head-mounted display (HMD) or smart glasses: collects and displays worker input and emotional state.

[1618] Camera and microphone style emotion recognition sensor: Detects the user's emotional state in real time from their facial expressions and voice.

[1619] software:

[1620] Emotion engine: An analysis system equipped with specific algorithms to analyze user emotions.

[1621] Generative AI model: An artificial intelligence model for generating optimal installation plans.

[1622] Geographic and population databases: Databases for capturing detailed information about installation locations.

[1623] Data processing and calculation

[1624] User input:

[1625] Users access the system via a head-mounted display (HMD) or smart glasses and input information about the base station's location, purpose, and conditions. Factory workers also input information about their current work and environment into an operating terminal.

[1626] Emotion recognition:

[1627] The emotion engine uses a camera and microphone to analyze the user's facial expressions and voice in real time during the input process to detect their emotional state, such as "happiness," "surprise," "sadness," "anger," and "relief."

[1628] Data transmission and analysis:

[1629] The device sends the input information and emotion data to the server, which formats the received data into the correct format, performs any necessary conversions, and retrieves additional information from geographic information and population databases to prepare for analysis.

[1630] Generative AI models analyze and plan:

[1631] The server analyzes the formatted data and the added emotion data using a generative AI model, which generates an optimal base station installation plan or production line layout plan based on past data and current technical parameters.

[1632] Viewing Results:

[1633] The server generates an installation plan, a cost estimate, and additional suggestions based on the user's emotional state, and sends it back to the device in a single response package, which is displayed to the user via a head-mounted display or smart glasses.

[1634] Specific examples

[1635] As an example, consider a production line optimization system for a factory that is introducing new machinery to Line C. A factory worker inputs "Introducing new equipment to Line C," and the emotion engine detects "joy" during the process. The generative AI model uses the detected emotion and input data to generate an optimal adjustment plan for the layout and operating hours of Line C and its surrounding areas, and suggests adding appropriate breaks. It also displays a cost estimate of 1,200,000 yen.

[1636] Example prompts to input to a generative AI model:

[1637] User Input: "We're going to install a new line machine."

[1638] Emotion data: "Joy"

[1639] Application example: "Planning the introduction of a new machine on Line C"

[1640] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1641] Step 1:

[1642] The user inputs information about the base station installation location, purpose, and conditions using a head-mounted display (HMD) or smart glasses. At this time, the user inputs specific information such as the "desired installation location," "purpose of installation," and "conditions" into the operating terminal. In the case of factory workers, they also input information about the "work content" and "environment."

[1643] Input: desired installation location, purpose of installation, conditions, work content, environment

[1644] Output: User input information

[1645] Step 2:

[1646] The emotion engine uses a camera and microphone to analyze the user's facial expressions and voice in real time to detect their emotional state, which is classified into categories such as "happiness," "surprise," "sadness," "anger," and "relief."

[1647] Input: User's facial expression, voice

[1648] Output: Emotion data (happiness, surprise, sadness, anger, relief, etc.)

[1649] Step 3:

[1650] The terminal transmits the user's input information and emotion data to the server, which converts the data into an appropriate format and transmits it.

[1651] Input: User input information, emotion data

[1652] Output: Sending formatted data

[1653] Step 4:

[1654] The server formats the received data and retrieves additional information from geographic and population databases. The formatted data and additional information are used to prepare for analysis.

[1655] Input: Received data (user input information, emotion data)

[1656] Output: Analysis-ready data

[1657] Step 5:

[1658] The server analyzes the incoming data and sentiment data using a generative AI model, which generates an optimal installation plan or production line layout plan based on historical data and current technical parameters.

[1659] Input: Analysis-ready data, additional information

[1660] Output: Generated installation plan or production line layout plan

[1661] Step 6:

[1662] The server then assembles the generated plan, cost estimates, and additional suggestions based on the emotional state into a single response package and sends it back to the device.

[1663] Input: Generated installation plan or production line layout plan

[1664] Output: Response package (installation plan, cost estimate, additional proposals)

[1665] Step 7:

[1666] The device receives the returned response package and displays it to the user via a head-mounted display or smart glasses. The user can then review the displayed plan, cost estimate, and additional proposals.

[1667] Input: Response package

[1668] Output: Show optimization plan

[1669] Step 8:

[1670] The user can re-enter the conditions based on the displayed installation plan and cost estimate and send it to the system to readjust the plan.

[1671] Input: Modified condition input

[1672] Output: Reworked installation plan and cost estimate

[1673] 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.

[1674] 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.

[1675] 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.

[1676] 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.

[1677] 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.

[1678] 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.

[1679] 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).

[1680] 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.

[1681] 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."

[1682] 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.

[1683] 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).

[1684] 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.

[1685] 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.

[1686] 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.

[1687] 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.

[1688] 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.

[1689] 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.

[1690] 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.

[1691] 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.

[1692] 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.

[1693] 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.

[1694] The following is further disclosed regarding the above embodiment.

[1695] (Claim 1)

[1696] a means for a user to input information regarding the location, purpose, and conditions of a base station;

[1697] a means for receiving the input information in the terminal and transmitting it to a server;

[1698] A means for the server to analyze the received information and generate an optimal base station installation plan using a generative AI model;

[1699] means for returning the plan generated by the server to the terminal and displaying it to the user;

[1700] A system including:

[1701] (Claim 2)

[1702] 10. The system of claim 1, wherein the received data is interlocked with a geographic information database and a population database to obtain and analyze additional data.

[1703] (Claim 3)

[1704] 10. The system of claim 1, wherein a cost estimate is calculated and provided to the user along with an installation plan.

[1705] "Example 1"

[1706] (Claim 1)

[1707] a means for a user to input information regarding the location, purpose, and conditions of a base station;

[1708] a means for receiving the input information in the terminal and transmitting it to a server;

[1709] means for formatting the information received by the server into a format for analysis;

[1710] means for obtaining additional data from geographic information databases and population databases;

[1711] The server inputs analytical data into the generated AI model to generate an optimal base station installation plan;

[1712] a means for calculating a cost estimate based on the server-generated installation plan;

[1713] means for returning the plan and cost estimate generated by the server to the terminal for display to the user;

[1714] A system including:

[1715] (Claim 2)

[1716] means for formatting the information received by the server into a format for analysis;

[1717] and means for obtaining additional data from a geographic information database and a population database.

[1718] 10. The system of claim 1.

[1719] (Claim 3)

[1720] Calculate a cost estimate based on the generated installation plan and provide it to the user along with the plan.

[1721] 10. The system of claim 1.

[1722] "Application Example 1"

[1723] (Claim 1)

[1724] a means for a user to input information regarding the location, purpose, and conditions of a base station;

[1725] a means for receiving the input information in the terminal and transmitting it to a server;

[1726] A means for the server to analyze the received information and generate an optimal base station installation plan using a generative AI model;

[1727] means for returning the plan generated by the server to the terminal and displaying it to the user;

[1728] A means of obtaining vehicle location information in real time and determining the optimal communication area;

[1729] means for providing base station installation information based on an optimal communication area;

[1730] A system including:

[1731] (Claim 2)

[1732] 10. The system of claim 1, wherein the received data is interlocked with a geographic information database and a population database to obtain and analyze additional data.

[1733] (Claim 3)

[1734] 10. The system of claim 1, wherein a cost estimate is calculated and provided to the user along with an installation plan.

[1735] ---

[1736] That's it. The claims have been rewritten to fit the new invention.

[1737] "Example 2: Combining Emotion Engines"

[1738] (Claim 1)

[1739] a means for a user to input information regarding the location, purpose, and conditions of a base station;

[1740] means for collecting emotion data along with the input information using an emotion engine that recognizes the user's emotional state;

[1741] a means for receiving the input information and emotion data in a terminal and transmitting the input information and emotion data to a server;

[1742] A means for analyzing the received information and emotion data by the server and generating an optimal base station installation plan using a generative AI model;

[1743] means for returning the plan generated by the server to the terminal and displaying it to the user;

[1744] A system including:

[1745] (Claim 2)

[1746] 10. The system of claim 1, wherein the received data is interlocked with a geographic information database and a population database to obtain and analyze additional data.

[1747] (Claim 3)

[1748] 10. The system of claim 1, wherein a cost estimate is calculated and provided to the user along with an installation plan.

[1749] "Application example 2 when combining emotion engines"

[1750] (Claim 1)

[1751] a means for a user to input information regarding the location, purpose, and conditions of a base station;

[1752] a means for receiving the input information in the terminal and transmitting it to a server;

[1753] A means for analyzing the information received by the server and the emotion data detected by the emotion engine, and generating an optimal base station installation plan using a generative AI model;

[1754] means for returning the plan and cost estimate generated by the server to a terminal for display to a user;

[1755] A system including:

[1756] (Claim 2)

[1757] 10. The system of claim 1, wherein the received data is interlocked with a geographic information database and a population database to obtain and analyze additional data.

[1758] (Claim 3)

[1759] 10. The system of claim 1, wherein a cost estimate is calculated and provided along with recommendations based on the emotional state. [Explanation of symbols]

[1760] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input information regarding the location, purpose, and conditions of a base station; a means for receiving the input information in the terminal and transmitting it to a server; A means for the server to analyze the received information and generate an optimal base station installation plan using a generative AI model; means for returning the plan generated by the server to the terminal and displaying it to the user; A system including:

2. 10. The system of claim 1, wherein the received data is analyzed in conjunction with a geographic information database and a population database to obtain additional data.

3. The system of claim 1 , wherein a cost estimate is calculated and provided to the user along with an installation plan.

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