System

Abstract

A system is provided.SOLUTION: This system is provided with a means for allowing users to input the installation places, purposes and conditions of base stations, a processing means including a generation AI model for automatically generating optimal installation / improvement plans, and a means for providing the generated plans and estimated results to the users.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] The installation and improvement of mobile phone base stations requires advanced expertise and experience, and those lacking this knowledge have difficulty creating appropriate plans. Selecting appropriate installation locations and formulating efficient improvement plans are particularly challenging. It is even more difficult to accomplish these tasks within a limited budget. The present invention aims to solve these technical challenges and support efficient and optimal installation and improvement of base stations. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system comprising the following means: First, a means is provided for users to input the installation location, purpose, and conditions of a base station. Next, a processing means is provided, including a generative AI model that analyzes data based on the input information and automatically generates an optimal installation and improvement plan. Finally, a means is provided for providing the generated plan and estimate results to the user. Furthermore, by including a means for searching for similar past cases and reference data and using them to analyze the generative AI model, and a means for receiving feedback on the generated plan and reevaluating and correcting it, it is possible to provide a more refined plan.

[0006] A "base station" is a wireless communication facility that transmits and receives signals for mobile phones and communication devices and connects them to a communication network.

[0007] "Installation location" refers to the geographical location or area where a base station is physically installed, and includes specific latitude and longitude information.

[0008] "Purpose" refers to the specific goal that you want to achieve by installing or improving base stations, such as improving communication performance or expanding the service area.

[0009] "Conditions" refers to the various requirements that must be taken into consideration when installing or improving a base station, including budget, environmental conditions, and legal regulations.

[0010] A "user" is an entity that inputs information regarding the installation and improvement of base stations and receives proposed plans and estimate results.

[0011] A "generative AI model" is an artificial intelligence algorithm that analyzes input information from users and automatically generates optimal installation and improvement plans.

[0012] "Processing means" refers to a computer system and software for processing input information and performing any necessary analysis or calculations.

[0013] "Past similar cases" refer to cases related to the installation or improvement of base stations that have been carried out in the past, including both successful and unsuccessful cases.

[0014] "Reference data" refers to existing data that is useful when planning the installation or improvement of base stations, and includes population distribution, communication traffic data, geographic information, etc.

[0015] "Feedback" refers to opinions or requests for revisions provided by a user regarding a proposed plan.

[0016] "Reevaluation" is the process of reexamining existing plans based on feedback and making modifications and improvements as necessary. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention relates to a system for proposing an optimal plan for installing and improving a mobile phone base station. This system is implemented in the following specific form.

[0039] First, the user uses a terminal to input the location, purpose, and conditions for installing the base station. Specifically, the user inputs the desired installation location (latitude and longitude), installation purpose (e.g., improving communication speed or expanding the coverage area), and installation conditions (e.g., budget limits, environmental conditions, legal regulations, etc.) on the terminal screen.

[0040] The entered data is converted into a digital format by the terminal and sent to the server, where the data format is verified and missing data is completed. For example, if the user forgets to enter information about the budget or a specific legal regulation, the server will send another request to the terminal to complete the information.

[0041] The server analyzes the received information and extracts the necessary data. Specifically, it investigates geographic information, population density data, and the state of the existing communications infrastructure based on the installation location. Next, the server references its internal database to search for similar past cases and reference data. This allows it to perform an analysis based on past successes and failures.

[0042] Once the analysis is complete, the server uses a generative AI model to automatically generate an optimal installation and improvement plan. This generative AI model calculates the most efficient plan by comprehensively considering a wide range of factors, including geographical characteristics, communication traffic data, budget, and legal regulations. For example, when generating a plan to improve communication speeds in Shinjuku Ward, the model proposes the optimal base station placement based on the area's peak communication traffic times and population distribution.

[0043] The generated plan includes detailed information such as the installation location, the type and number of required equipment, estimated installation and operating costs, implementation schedule, and notes on legal regulations and environmental conditions. The plan is then sent from the server to the terminal and provided to the user.

[0044] The user checks the plan received on their device and reviews its contents. If there are any questions or corrections to the plan or estimate, the user can send feedback from their device to the server. The server reevaluates the plan based on the received feedback, regenerates a revised plan as necessary, and provides it to the user.

[0045] As a concrete example, consider a base station installation plan aimed at improving communication speeds in urban areas. If a user inputs that they would like to install the base station in Shinjuku Ward, that their budget is within 50 million yen, and that they would like to consider environmental conditions, the server will use that information to propose the optimal layout and equipment. This proposal includes specific candidate locations for the base station, the necessary equipment, estimated installation and operating costs, and an implementation schedule, allowing the user to refer to it to proceed with a specific installation plan.

[0046] In this way, even if one lacks technical knowledge or experience, by using the system of the present invention, it is possible to efficiently and optimally install and improve base stations.

[0047] The processing flow will be explained below.

[0048] Step 1:

[0049] The user inputs information into the terminal, such as the location, purpose, and conditions of the base station installation. For example, detailed information such as the latitude and longitude of the installation location, purpose (improving communication speed or expanding the coverage area), and conditions (budget, legal regulations) is entered.

[0050] Step 2:

[0051] The terminal converts the user's input into a digital format, which includes cleaning and formatting the entered text and numeric data.

[0052] Step 3:

[0053] The device converts the information into a digital format and sends it to the server, using the necessary protocols to ensure that the data arrives at the server without being lost or tampered with.

[0054] Step 4:

[0055] The server receives the information sent from the device and verifies the data format. If any information is missing, the server sends another request to the device to complete the missing data.

[0056] Step 5:

[0057] The server references the database to search and obtain geographical information, population density data, and the status of existing communications infrastructure for the entered installation location.

[0058] Step 6:

[0059] The server searches for similar cases and reference data from the past, analyzes the necessary data, and prepares input data for the generative AI model based on the results of this analysis.

[0060] Step 7:

[0061] The server runs the AI ​​model to automatically generate optimal installation and improvement plans. The model takes into consideration geographical characteristics, communication traffic data, budget, legal regulations, etc. to calculate the optimal solution.

[0062] Step 8:

[0063] The server generates a detailed report of the generated plan, including specific installation locations, required equipment, estimated installation and operating costs, implementation schedules, and regulatory and environmental considerations.

[0064] Step 9:

[0065] The server generates a report and sends it to the terminal, including visual elements and brief explanations to make it easier for the user to understand.

[0066] Step 10:

[0067] The user checks the received plan on the device and reviews the contents. If there are any questions or corrections regarding the plan or estimate, the user can send feedback from the device to the server.

[0068] Step 11:

[0069] The server receives the feedback, re-evaluates and modifies the plan, and then generates a new optimal plan to serve to the user.

[0070] Example 1

[0071] 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."

[0072] With conventional base station installation planning systems, when generating optimal installation and improvement plans based on a variety of information, it took time to collect and analyze data, making it difficult to quickly provide highly accurate plans. Furthermore, they lacked the functionality to reevaluate and revise plans based on user feedback, making it impossible to provide appropriate plans even in situations where flexible responses were required.

[0073] 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.

[0074] In this invention, the server includes: a means for a user to input the installation location, installation purpose, and installation conditions of a communication base station; a means for converting the input information into a digital format and transmitting it; a means for analyzing the data based on the received information and extracting necessary data; a means for automatically generating an optimal installation and improvement plan using a generative AI model based on the analyzed data; and a means for providing the generated plan and estimate results to the user. This makes it possible to quickly and accurately collect and analyze a variety of information, provide highly accurate plans, and flexibly reevaluate and modify plans based on user feedback.

[0075] A "communications base station" is a wireless transmitting / receiving device installed to carry out mobile phone and wireless communications.

[0076] The "installation location" is information indicating the geographical location where the communication base station is physically installed.

[0077] "Installation purpose" is information indicating the reason or goal for installing a communication base station, such as improving communication speed or expanding the coverage area.

[0078] "Installation conditions" refers to information indicating constraints and conditions that must be considered when installing a communication base station, such as budget, environmental conditions, and legal regulations.

[0079] "Digital format" means information entered by a user that has been converted into a form that can be processed by a computer.

[0080] "Received information" refers to data regarding the installation location, installation purpose, and installation conditions that are input by the user and received by the server.

[0081] "Analyzing data" is the process of extracting and structuring the necessary data based on the received information.

[0082] "Extracting the necessary data" means extracting from the analyzed information the information necessary to generate optimal installation and improvement plans for communication base stations.

[0083] A "generative AI model" is an algorithm that uses artificial intelligence to generate optimal installation and improvement plans based on input data.

[0084] The "optimal installation and improvement plan" is a plan for the most efficient and effective placement and installation of communication base stations, calculated by comprehensively taking into account multiple factors.

[0085] "Plans and Estimates" refers to the installation plans and associated cost estimates created by the generative AI model.

[0086] "User" refers to a person who uses this system to create installation and improvement plans for communication base stations.

[0087] The "internal database" is a storage system that stores similar past cases and reference data.

[0088] "Feedback" refers to opinions and requests for corrections that a user provides to a generated plan.

[0089] "Plan reassessment and revision" is the process of reviewing the generated plan based on the feedback received and making changes as necessary.

[0090] This invention relates to a system that proposes optimal plans for installing and improving base stations in mobile phone communication networks. This system analyzes data based on the installation location, installation purpose, and installation conditions entered by the user, and generates optimal installation and improvement plans.

[0091] First, the user uses a terminal to input the desired installation location (e.g., the latitude and longitude of Shinjuku Ward), the purpose of installation (improving communication speed), and installation conditions (e.g., a budget of 50 million yen, consideration of environmental conditions and legal regulations). This information is converted into a digital format by the terminal and sent to the server. A secure protocol (e.g., HTTPS) is used for communication between the server and the terminal.

[0092] The server first verifies the received data and, if any information is missing, sends a completion request to the terminal, allowing the user to re-enter the missing information. The server then searches the system's internal database based on the information to obtain geographical information, population density data, the status of existing communications infrastructure, and other information. The internal database contains similar past cases and reference data, which are used for analysis.

[0093] The server then uses the analyzed data to apply a generative AI model to automatically generate an optimal installation and improvement plan. The generative AI model considers a wide range of factors, including geographical characteristics, communication traffic data, budget, and legal regulations, to propose an efficient and effective plan. The generated plan includes the installation location, the type and number of required equipment, estimated installation and operating costs, an implementation schedule, and notes on legal regulations and environmental conditions. This plan is then sent from the server to the terminal and provided to the user.

[0094] The user checks the plan received on their device and reviews its contents. If there are any questions or corrections to the plan, the user sends feedback from their device to the server. The server reevaluates the plan based on the received feedback, generates a revised plan as necessary, and provides it to the user again.

[0095] As a concrete example, if a base station is to be installed to improve communication speeds in Shinjuku Ward, the user inputs "Improve communication speeds in Shinjuku Ward, budget 50 million yen, considering environmental conditions" into the terminal. The terminal sends this information to the server, which then proposes the optimal base station placement based on geographical information and population data. This proposal includes candidate base station installation sites, required equipment, installation and operating costs, implementation schedule, etc.

[0096] An example prompt is, "Generate a base station installation plan to improve communication speeds in Shinjuku Ward. The budget should be within 50 million yen, and environmental conditions and legal regulations should be taken into consideration. Please provide a proposal that includes the installation location, required equipment, cost estimate, and implementation schedule."

[0097] This system enables efficient and appropriate installation and improvement of base stations, even for those with a lack of technical knowledge or experience.

[0098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0099] Specific flow of system program processing

[0100] Step 1:

[0101] The user uses a terminal to input the desired installation location, purpose, and installation conditions. For example, they might input the latitude and longitude of Shinjuku Ward, the need to improve communication speed, a budget of 50 million yen, and environmental conditions to be taken into consideration. This input data is sent to the next step.

[0102] Input: desired installation location, installation purpose, installation conditions

[0103] Output: Input data (desired installation location, installation purpose, installation conditions)

[0104] Step 2:

[0105] The terminal converts the input data into a digital format. Specifically, it identifies the text information entered by the user as structured data and converts it into, for example, JSON format. This data conversion makes it easier for the server to process in the next step.

[0106] Input: Input data (desired installation location, installation purpose, installation conditions)

[0107] Output: Data converted to digital format

[0108] Step 3:

[0109] The device then transmits the data in digital format to the server, using a secure protocol (e.g., HTTPS) to protect the integrity and privacy of the data.

[0110] Input: Data converted into digital format

[0111] Output: Data sent to the server

[0112] Step 4:

[0113] The server validates the received data, checking for missing data or formatting issues, and generates a completion request if necessary. For example, if regulatory information is missing, a completion request is generated and sent to the device.

[0114] Input: Data sent to the server

[0115] Output: Validation results and completion request (if necessary)

[0116] Step 5:

[0117] The terminal receives the completion request and prompts the user for additional information. The user enters the missing information (e.g., regulatory details) into the terminal and resubmits the data.

[0118] Input: Completion request

[0119] Output: Imputed data

[0120] Step 6:

[0121] The server performs analysis based on the supplemented data. Specifically, it retrieves geographic information system (GIS) data and population density data and compares them with the input information. It also searches its internal database for similar cases and reference data from the past.

[0122] Input: Imputed data

[0123] Output: Analysis results (geographical information, population density data, past similar cases)

[0124] Step 7:

[0125] Based on the analysis results, the server uses a generative AI model to generate an optimal plan, taking into consideration geographical characteristics, communication traffic data, budget, legal regulations, etc. For example, it proposes the optimal placement of base stations by taking into account peak communication traffic times and population distribution in Shinjuku Ward.

[0126] Input: Analysis results (geographical information, population density data, past similar cases)

[0127] Output: The generated optimal plan

[0128] Step 8:

[0129] The server then sends the generated plan to the terminal, which includes the installation location, the type and number of required equipment, estimated installation and operation costs, an implementation schedule, and notes on legal regulations and environmental conditions.

[0130] Input: Generated optimal plan

[0131] Output: Plan sent to the device

[0132] Step 9:

[0133] The user checks the plan received on the device and reviews its contents. If there are any questions or corrections to the plan, the user can send feedback from the device to the server. Specifically, if the user is not satisfied with the cost estimate items, the user can include the reasons in the feedback.

[0134] Input: Plan sent to device

[0135] Output: User feedback

[0136] Step 10:

[0137] The server receives the feedback and re-evaluates the plan, reusing the generative AI model as needed to generate a new plan with adjustments and modifications, such as revising equipment and layout to stay within budget.

[0138] Input: User feedback

[0139] Output: revised plan

[0140] Step 11:

[0141] The server sends the revised plan to the terminal and provides it to the user, allowing the user to review the optimized plan again and make a final decision.

[0142] Input: revised plan

[0143] Output: The revised plan sent to the device

[0144] (Application example 1)

[0145] 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."

[0146] Determining efficient product placement and delivery routes is extremely important in the operation of a logistics center. However, previous methods required manual determination of optimal placement and routes while taking into account multiple factors such as the type and quantity of goods, budget, and delivery date, which required a significant amount of time and effort. Furthermore, the accuracy of optimization was insufficient, making it difficult to improve the efficiency of the entire logistics center. To solve these issues, a system that utilizes existing data to automatically and optimally propose product placement and delivery routes is needed.

[0147] 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.

[0148] In this invention, the server includes a means for a user to input placement locations, purposes, and conditions, a processing means including a generation AI model that analyzes data based on the input information and automatically generates an optimal placement and route plan, a means for providing the generated plan and estimate results to the user, and a means for proposing optimal item placement and delivery routes based on the placement data, thereby enabling efficient and automatic optimization of item placement and delivery routes in a logistics center.

[0149] "User" refers to a person who uses the system to optimize the placement of goods and delivery routes.

[0150] "Location" refers to the specific location within the logistics center where the item is to be placed.

[0151] "Objective" refers to the goal you want to achieve by optimizing the placement of goods and delivery routes, such as improving efficiency or reducing costs.

[0152] "Conditions" refer to constraints or requirements that must be taken into account when optimizing the placement of goods and delivery routes, such as budget, weight, and legal regulations.

[0153] "Data" refers to the information entered by the User and any additional information related thereto.

[0154] "Analysis" refers to the calculations and evaluations that are carried out to derive optimal layout and route plans based on input data.

[0155] "Optimal placement and route plan" refers to a plan that designs the placement of goods and delivery routes in the most efficient way.

[0156] "Generative AI model" refers to an artificial intelligence algorithm that automatically generates optimal placement and route plans based on input information.

[0157] "Processing means" refers to the function that analyzes the information entered by the user and automatically generates the optimal plan using a generative AI model.

[0158] "Estimation results" refers to cost and schedule predictions based on the generated optimal layout and route plan.

[0159] "Feedback" refers to the evaluation or opinion provided by a user regarding a generated plan.

[0160] "Plan reevaluation and revision" refers to the process of reviewing the generated plan based on feedback from users and revising it as necessary.

[0161] "System" refers to the entire set of hardware and software including the above means and models.

[0162] The present invention relates to a system for automatically optimizing the allocation of goods and delivery routes in a logistics center. Specific embodiments of the system are described below.

[0163] First, the user uses a device such as a smartphone or tablet to input the type, quantity, weight, delivery area, budget, etc. The input information is converted into a digital format and sent to the server.

[0164] The server analyzes the data based on the received information and extracts the necessary data. Specifically, based on the input product information, budget, and date and time, it searches for similar past cases and reference data and uses them to analyze the generated AI model.

[0165] Next, the server automatically generates optimal product placement and delivery routes using a generative AI model that comprehensively considers multiple factors (product type, quantity, weight, budget, delivery area, date, etc.) to calculate the most efficient placement plan and delivery route.

[0166] The generated plan includes information on the location, the type and number of required facilities, estimated installation and operation costs, implementation schedule, and notes on legal regulations and environmental conditions. This information is sent from the server to the terminal and provided to the user.

[0167] The user checks the plan received on their device and reviews its contents. If there are any questions or corrections to the plan or estimate, the user can send feedback from their device to the server. The server reevaluates the plan based on the received feedback, regenerates a revised plan as necessary, and provides it to the user.

[0168] The system's program uses the requests library (Python) to send API requests and executes data analysis using a generative AI model on the server. The main hardware used is devices such as smartphones and tablets. The generative AI model also runs on the server, calculating optimal placement and route plans.

[0169] As a specific example, we will explain the case where we propose the optimal product placement and delivery route in a logistics center under the following conditions.

[0170] Logistics Center ID: 12345A

[0171] Goods:

[0172] Material ID: A111, Quantity: 100, Weight: 2.5

[0173] Material ID: B222, Quantity: 200, Weight: 3.0

[0174] Budget: 500,000 yen

[0175] Date: 2023-10-01

[0176] Example prompt sentence:

[0177] Please propose the optimal product layout and delivery route for the distribution center under the following conditions:

[0178] Logistics Center ID: 12345A

[0179] Goods:

[0180] Material ID: A111, Quantity: 100, Weight: 2.5

[0181] Material ID: B222, Quantity: 200, Weight: 3.0

[0182] Budget: 500,000 yen

[0183] Date: 2023-10-01

[0184] This type of system makes it possible to propose efficient and optimal product placement and delivery routes even for those lacking technical knowledge or experience.

[0185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0186] Step 1:

[0187] Using a smartphone or tablet, the user inputs information such as the type, quantity, weight, budget, and date of items in the distribution center on the screen, and the conditions set by the user are converted into a digital format.

[0188] input:

[0189] Type of item

[0190] quantity

[0191] Weight

[0192] budget

[0193] date

[0194] output:

[0195] Input information converted into digital format

[0196] Step 2:

[0197] The device sends the entered digital format information to the server, which transfers the information using API requests.

[0198] input:

[0199] Server API endpoint

[0200] Input information in digital format

[0201] output:

[0202] Input information sent to the server

[0203] Step 3:

[0204] The server begins data analysis based on the received input information. Specifically, it searches for similar past cases and reference data to collect the information necessary to analyze the generative AI model.

[0205] input:

[0206] Submitted input information

[0207] output:

[0208] Collected historical and reference data

[0209] Step 4:

[0210] The server uses a generative AI model to automatically generate optimal plans for product placement and delivery routes. The AI ​​algorithm calculates the optimal placement and route by taking into account multiple factors based on input information and collected data.

[0211] input:

[0212] Input information

[0213] Data collected

[0214] output:

[0215] Optimal product placement and delivery route planning

[0216] Step 5:

[0217] The server sends the generated deployment plan and estimate results (cost, schedule, etc.) to the terminal, allowing the user to view the optimal plan.

[0218] input:

[0219] Generated Plan

[0220] Estimate results

[0221] output:

[0222] Plans and quote results sent to your device

[0223] Step 6:

[0224] The user reviews the plan and estimate results received on the device, and if there are any questions or corrections regarding the plan or estimate, they provide feedback via the device.

[0225] input:

[0226] Received plan and quote results

[0227] User Feedback

[0228] output:

[0229] Feedback Information

[0230] Step 7:

[0231] The server reevaluates the plan based on user feedback and modifies it as necessary. It then uses the generative AI model again to create a revised plan and sends it to the device.

[0232] input:

[0233] Feedback Information

[0234] Data collected

[0235] output:

[0236] Revised Plan

[0237] In this way, optimal product placement and delivery routes are provided to users, making it possible to improve the operational efficiency of logistics centers.

[0238] 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.

[0239] The present invention relates to a system that proposes optimal plans for the installation and improvement of mobile phone base stations. This system provides plans that are more suitable for users by combining it with an emotion engine that recognizes user emotions.

[0240] First, the user uses a terminal to input the location, purpose, and conditions for installing the base station. Specifically, the user inputs the latitude and longitude of the installation location, the purpose (e.g., improving communication speed, expanding the coverage area), and the conditions (e.g., budget, environmental conditions, legal regulations).

[0241] The input information is converted into a digital format by the device and sent to the server. At this time, the emotion engine analyzes the user's emotional state at the time of input. For example, it analyzes information such as whether the user is tense or relaxed based on the input text, input speed, and input device usage.

[0242] The server analyzes the received information and, if any information is missing, requests it again from the terminal to complete the data.The server then references its internal database to search for and obtain geographical information, population density data, the status of existing communications infrastructure, and other information related to the input installation location.

[0243] Next, the server searches for similar past cases and reference data, and analyzes the necessary data. The analysis results are used as input data for the generative AI model. Based on the input information and past data, the generative AI model automatically generates an optimal base station installation and improvement plan. This model comprehensively takes into account multiple factors, including geographical characteristics, communication traffic data, budget, and legal regulations.

[0244] The generated plan includes the specific location of the installation site, the type and number of required equipment, estimated installation and operation costs, an implementation schedule, and notes on legal regulations and environmental conditions. The emotion engine takes the user's emotional state into account and adjusts the plan presentation accordingly. For example, if the user is nervous, the plan will be explained in more concise and clear language.

[0245] The server sends the plan and estimate results it has created to the device and provides them to the user. The user checks the plan received on the device and reviews its contents. If the user has any questions or requests for corrections regarding the plan or estimate, they can send feedback from their device to the server. At this time, the emotion engine analyzes the user's emotions when providing feedback and reevaluates the plan as necessary.

[0246] After analyzing the feedback, the server re-runs the generative AI model to generate a revised, optimal plan. The server then re-presents this revised plan to the user, while making adjustments to increase user satisfaction through the emotion engine.

[0247] As a concrete example, consider the installation of a base station to improve communication speeds in Shinjuku Ward. A user inputs, "I would like to install a base station in Shinjuku Ward to improve communication speeds, with a budget of 50 million yen or less and taking environmental conditions into consideration." If the user is nervous at this time, the emotion engine can present a plan in language that will reassure them.

[0248] In this way, even users who lack technical knowledge or experience can use the system of the present invention to efficiently and optimally install and improve base stations, and by taking user feelings into consideration, they can provide more user-friendly services.

[0249] The processing flow will be explained below.

[0250] Step 1:

[0251] The user uses a terminal to input information such as the base station installation location, purpose, and conditions, including the desired latitude and longitude, the purpose (such as improving communication speed or expanding the coverage area), budget limits, and legal restrictions.

[0252] Step 2:

[0253] The device converts the user's input into a digital format and then uses an emotion engine to analyze the user's emotional state (e.g., nervousness, relaxation) as they type, including keystroke speed and emotional analysis of the input.

[0254] Step 3:

[0255] The terminal transmits the information converted into a digital format and the analyzed emotional state information to a server.

[0256] Step 4:

[0257] The server receives the information sent from the device, verifies the data format, and, if necessary, requests the device to complete any missing data.

[0258] Step 5:

[0259] The server refers to the database to search and obtain geographical information, population density data, and information on existing communications infrastructure for the desired installation location.

[0260] Step 6:

[0261] The server searches for and analyzes similar past cases and reference data, thereby preparing input data for the generative AI model.

[0262] Step 7:

[0263] The server runs a generative AI model to automatically generate optimal installation and improvement plans, taking into account multiple factors such as geographical characteristics, communication traffic data, budget, and legal regulations.

[0264] Step 8:

[0265] The server generates a report of the plan, which includes the installation location, required equipment, estimated installation and operating costs, implementation schedule, and regulatory and environmental considerations.

[0266] Step 9:

[0267] The emotion engine adjusts the plan presentation based on the user's emotional state, for example, providing concise, positive explanations if the user is nervous, or providing detailed information if the user is relaxed.

[0268] Step 10:

[0269] The server sends the generated report and the adjusted plan to the device.

[0270] Step 11:

[0271] The user checks the received plan on the device, reviews its contents, and sends feedback if there are any questions or corrections they would like to make regarding the plan or estimate.

[0272] Step 12:

[0273] Along with the feedback, the emotion engine analyzes the user's emotions at the time of the feedback, allowing the server to take the most appropriate action.

[0274] Step 13:

[0275] The server re-runs the generative AI model to re-evaluate and revise the plan based on the feedback, generating a revised plan that the emotion engine adjusts again before providing to the user again.

[0276] In this way, this system goes through a series of processing steps to provide efficient and optimal base station installation and improvement plans, and by taking user emotions into consideration, it realizes a more user-friendly service.

[0277] Example 2

[0278] 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."

[0279] Conventional base station installation plan creation systems have difficulty presenting efficient plans because they do not take into account the user's emotional state. Furthermore, they are unable to effectively utilize similar past data, making it difficult to generate optimal plans. Furthermore, even if users provide feedback on the plan, appropriate revisions based on that feedback are not made.

[0280] 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.

[0281] In this invention, the server includes: a means for a user to input the base station installation location, purpose, and conditions; a means for converting the input information into a digital format and transmitting it to the server; an emotion engine including means for performing emotion analysis based on the transmitted information; a means for analyzing the received information and completing any missing information; a means for referencing an internal database to acquire relevant geographic information, population density data, and the status of existing communication infrastructure; a means for analyzing similar past cases and reference data and using them as input data for a generative AI model; a means for automatically generating an optimal base station installation and improvement plan using the generative AI model; and a emotion engine including means for adjusting the presentation content of the generated plan taking into account the user's emotional state; a means for providing the generated plan and estimate results to the user; and a means for receiving user feedback, analyzing the user's emotional state at the time of the feedback, and reevaluating and modifying the plan. This enables the generation and provision of an efficient and optimal base station installation and improvement plan while taking into account the user's emotions.

[0282] A "user" is an end user who inputs the installation location, purpose, conditions, etc. of a base station.

[0283] A "terminal" is an electronic device that allows a user to input information and convert that information into a digital format.

[0284] A "server" is a computer system that receives digitally formatted information sent from a terminal and performs processing such as analysis, search, and generation based on that information.

[0285] The "emotion engine" is a software module that analyzes the user's emotional state at the time of input and adjusts the service content based on the analysis results.

[0286] "Digital format" refers to a data format in which information entered by a user is converted into a format that can be processed electronically.

[0287] An "internal database" is a storage device for storing data such as geographical information, population density data, and the status of existing communication infrastructure.

[0288] The "generative AI model" is an artificial intelligence model that automatically generates optimal base station installation and improvement plans based on similar past cases and reference data.

[0289] The "plan" is an installation and improvement strategy that includes the specific installation location of the base station, the type and number of required equipment, estimated installation and operation costs, implementation schedule, and points to note regarding legal regulations and environmental conditions.

[0290] "Feedback" is response information that allows the user to check the plan and convey any questions or requests for corrections to the server.

[0291] "Analysis" is the process by which the server extracts and understands the necessary information based on the data it receives.

[0292] "Search" is the process by which the server finds the necessary information from its internal database or past data.

[0293] "Reassessment" is the process of receiving feedback, reviewing existing plans based on that feedback, and making adjustments as needed.

[0294] MODE FOR CARRYING OUT THE INVENTION

[0295] The present invention is a system that proposes optimal plans for installing and improving base stations. This system aims to provide plans that are more suitable for each user by combining it with an emotion engine that recognizes the user's emotions. A specific example of the system is described below.

[0296] First, the user uses a terminal to input the base station installation location, purpose, and conditions. Specific input items include the latitude and longitude of the installation location, purpose (e.g., improving communication speed, expanding coverage area), and conditions (e.g., budget, environmental conditions, legal regulations). This input information is converted into a digital format by the terminal and sent to the server.

[0297] The emotion engine kicks in at this point and analyzes the user's emotional state as they type, for example, by looking at the text they type, their typing speed, and how they use their input device to determine whether they are tense or relaxed.

[0298] The server then analyzes the received information and, if any information is missing, requests it again from the terminal to complete the data. At this point, the server references its internal database to search and obtain geographical information, population density data, the status of existing communications infrastructure, and other information related to the input installation location.

[0299] The system then searches for similar past cases and reference data, and uses the analysis results as input data for the generative AI model. This generative AI model references existing base station installation cases in Osaka City and Tokyo, for example, and automatically generates an optimal base station installation and improvement plan based on the input information and past data. The model comprehensively takes into account multiple factors, including geographical characteristics, communication traffic data, budget, and legal regulations.

[0300] The plan includes the specific location of the installation site, the type and quantity of equipment required, estimated installation and operation costs, an implementation schedule, and notes on legal regulations and environmental conditions. The emotion engine takes the user's emotional state into account and adjusts the plan presentation accordingly. For example, if the user is nervous, the plan will be explained in more concise and clear language.

[0301] The server sends the plan and estimate results to the terminal and provides them to the user. The user can then use the terminal to check and review the contents. If the user has any questions or requests for corrections regarding the plan or estimate, they can send feedback from their terminal to the server. At this time, the emotion engine analyzes the user's emotional state at the time of feedback and reevaluates the plan as necessary.

[0302] Finally, the server re-runs the generative AI model based on the analyzed feedback to generate a revised optimal plan, which it then provides to the user again while making adjustments to increase user satisfaction through the emotion engine.

[0303] Specific examples

[0304] As a concrete example, consider the installation of a base station to improve communication speeds in Shinjuku Ward. The user inputs, "I would like to install a base station to improve communication speeds in Shinjuku Ward, with a budget of 50 million yen or less and taking environmental conditions into consideration." At this time, the emotion engine recognizes the user's state of tension and presents a plan in language that gives a sense of security.

[0305] Prompt Sentence Examples

[0306] "We would like to install a base station in Shinjuku Ward to improve communication speeds. The budget is within 50 million yen, and we would like you to take into consideration the environmental conditions. Please provide us with the optimal plan based on data from similar cases in the past."

[0307] In this way, even users who lack technical knowledge or experience can use the system of the present invention to efficiently and optimally install and improve base stations, and by taking user feelings into consideration, they can provide more user-friendly services.

[0308] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0309] Step 1:

[0310] The user uses a terminal to input the location, purpose, and conditions for installing the base station. Specifically, the user enters, "I want to install a base station in Shinjuku Ward to improve communication speeds, with a budget of 50 million yen or less and taking environmental conditions into consideration." This information becomes the initial input data for the program.

[0311] Step 2:

[0312] The terminal converts the information entered by the user into digital format. The converted information includes the installation location (latitude and longitude), purpose (improvement of communication speed), and conditions (budget of 50 million yen or less, environmental conditions). The converted information is sent to the server.

[0313] Step 3:

[0314] The server receives the information sent from the device. This information includes the latitude and longitude of the installation location, purpose, and conditions. The emotion engine then analyzes the user's emotional state. Specifically, it analyzes the emotion of the input text and keyboard input speed to determine whether the user is tense or relaxed.

[0315] Step 4:

[0316] The server analyzes the received information, identifies any missing information, and sends another request to the device. For example, if it determines that "budget breakdown details are missing," it requests the device to "please tell me the budget breakdown." This request is displayed to the user.

[0317] Step 5:

[0318] The terminal presents the request from the server to the user. The user enters the missing information, which the terminal then converts back into digital format and sends to the server. The input data here might be details of a budget breakdown.

[0319] Step 6:

[0320] The server receives the information resent by the user and refers to its internal database to search and obtain relevant geographical information, population density data, and the status of existing communication infrastructure. For example, it collects geographical information and population density data for Shinjuku Ward. This information is used for analysis.

[0321] Step 7:

[0322] The server searches and analyzes similar cases and reference data from the past, such as base station installation cases to improve communication speeds in other cities, and uses this data as input data for the generative AI model.

[0323] Step 8:

[0324] The server runs the generative AI model to automatically generate an optimal base station installation and improvement plan. The generative AI model takes into account many data points (geographical characteristics, communication traffic, budget, legal regulations, etc.) and generates a plan that includes specific installation locations, required equipment, operating costs, implementation schedules, etc.

[0325] Step 9:

[0326] The generated plan is adjusted using an emotion engine according to the user's emotional state. For example, if the user is nervous, the plan explanation is adjusted to be more concise and clear. This adjusted plan becomes the final output data.

[0327] Step 10:

[0328] The server sends the adjusted plan and estimate results to the device, which then provides them to the user, who then confirms and reviews the plan. For example, if the user feels that the budget breakdown of the plan is unclear, that feedback is recorded.

[0329] Step 11:

[0330] The user sends feedback from their device to the server. The server receives this feedback and uses an emotion engine to analyze the user's emotional state at the time of the feedback. For example, if it is determined to be "dissatisfied," it considers countermeasures.

[0331] Step 12:

[0332] The server re-runs the generative AI model based on the feedback and generates a revised, optimal plan. The revised plan includes more detailed breakdowns and additional information to enhance user satisfaction.

[0333] Step 13:

[0334] The server sends the revised plan back to the terminal, which then provides it to the user. The user reviews the plan again and gives a final rating. This cycle is repeated until the user is satisfied.

[0335] In this way, by understanding the specific processing flow of the invention, it becomes clear how the system operates and how it proposes optimal base station installations to users.

[0336] (Application example 2)

[0337] 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."

[0338] The problem to be solved by this invention is to provide users with mobile phone base station installation and improvement plans that allow even users with little technical knowledge or experience to easily understand and implement efficient and optimal plans. Another object of this invention is to increase user satisfaction by taking into account the user's emotional state and making flexible suggestions accordingly.

[0339] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input the installation location, purpose, and conditions of a base station; means including a generation AI model that analyzes data based on the input information and automatically generates an optimal installation and improvement plan; means for analyzing the user's emotional state using an emotion engine and adjusting the generated plan based on the analysis results; and means for providing the generated plan and estimate results to the user. This makes it possible for even users who lack technical knowledge or experience to obtain an optimal base station installation and improvement plan that is easily adjusted, and allows for flexible responses according to the user's emotional state.

[0340] "User" refers to a person who inputs the location, purpose, and conditions of a base station through the system and checks and reviews the generated plan.

[0341] The "location of the base station" is information indicating the specific physical location where the base station is installed for mobile phone communications.

[0342] "Purpose" refers to the goal of installing a base station, and refers to the specific things that you want to achieve, such as improving communication speeds or expanding the coverage area.

[0343] "Conditions" refer to various constraints and requirements for installing a base station, including budget, environmental conditions, and legal regulations.

[0344] An "emotion engine" is a software module for analyzing a user's emotional state, and has the function of evaluating the user's emotions based on the input text and the status of the device being used.

[0345] A "generative AI model" is an artificial intelligence model that automatically generates optimal base station installation and improvement plans based on input information and past data.

[0346] A "Plan" is a specific implementation plan for the installation of a base station, including the specific location of the installation site, the type and number of facilities required, estimated installation and operation costs, and an implementation schedule.

[0347] "Feedback" is information indicating opinions and desired modifications made by the user to the generated plan.

[0348] A "server" is a remote computer system that receives, analyzes, and processes information sent by users.

[0349] "Tension" refers to the emotional state of anxiety or stress that a user experiences when using a system.

[0350] "Rest API" is a standard protocol for data communication between a server and a client, and is a communication method that uses HTTP.

[0351] The present invention relates to a system that allows users to propose optimal plans for the installation and improvement of mobile phone base stations. This system has an emotion-responsive function that makes proposals taking into account the user's emotional state.

[0352] Hardware and software used

[0353] Hardware: smartphone or tablet (for user input and display), server (for data processing and storage)

[0354] software:

[0355] Implementation in Python

[0356] EmotionEngine: A library for analyzing user emotions

[0357] AI model: An artificial intelligence model that generates optimal plans

[0358] REST API: Data communication protocol between server and client

[0359] Overall system processing flow

[0360] 1. User Input

[0361] Users use their smartphones or tablets to input the base station installation location, purpose, and conditions (e.g., budget, environmental conditions, legal regulations).The emotion engine then analyzes the user's input speed and all aspects of the device being used to obtain emotion data.

[0362] 2. Converting information into digital format and transmitting it

[0363] The input information is converted into a digital format and sent to a server, which then begins analyzing the data based on the received information and the user's emotional state.

[0364] 3. Data analysis and plan generation

[0365] The server references its internal database based on the input information to obtain geographical information, population density data, the state of existing communications infrastructure, and other information. It also searches for similar past cases and reference data, and inputs this information into the generative AI model. The generative AI model then comprehensively analyzes this information to generate an optimal base station installation and improvement plan.

[0366] 4. Adjusting the plan with the emotion engine

[0367] The server adjusts the generated plan based on the analysis results of the emotion engine. For example, if the user is nervous, the server may explain the plan in simple and clear terms.

[0368] 5. Providing the plan and receiving feedback

[0369] The generated plan and estimate results are provided to the user via a smartphone or tablet. The user can review the plan and send feedback to the server. This feedback is also analyzed by the emotion engine, and the plan is reevaluated and revised as necessary.

[0370] Specific examples

[0371] Consider installing a base station to improve communication speeds in Shinjuku Ward. A user inputs, "I would like to install a base station in Shinjuku Ward to improve communication speeds, with a budget of 50 million yen or less and taking environmental conditions into consideration." If the user is nervous, the emotion engine can present the plan in language that will reassure them.

[0372] Prompt Sentence Examples

[0373] "Please propose an optimization plan to improve the response speed of electronic payments within Shinjuku Ward. The budget is 5 million yen. If users are unsure, please explain it in simple terms to reassure them."

[0374] By feeding this prompt into a generative AI model, we can obtain a more user-friendly and emotionally sensitive plan.

[0375] This system allows even users with limited technical knowledge or experience to receive optimal base station installation and improvement plans, and also makes it possible to make suggestions that reflect the user's emotions.

[0376] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0377] Step 1:

[0378] The user uses a smartphone or tablet to input the base station installation location, purpose, and conditions. Specifically, the user launches the app and inputs the latitude and longitude of the installation location, purpose (improving communication speed, expanding coverage area, etc.), budget, environmental conditions, etc. The input data includes latitude and longitude information, text data of the purpose, detailed conditions, etc. The input data is temporarily stored on the device.

[0379] Step 2:

[0380] The terminal converts the acquired user input data into a digital format and sends it to the server. Specifically, the input data is loaded into a standard data format such as JSON format and sent to the server using the HTTP protocol. At this time, the input data includes the location, purpose, conditions, etc. entered by the user, and is sent to the server as output data.

[0381] Step 3:

[0382] The server receives the transmitted data and analyzes the user's emotional state using an emotion engine. Specifically, the emotional state (e.g., tension, anxiety, relaxation, etc.) is evaluated based on the user's input speed and text content. Input data includes input speed, input text, and the status of the device being used. As a result of the analysis, the emotion engine outputs an emotional state such as "tension," "anxiety," or "relaxation."

[0383] Step 4:

[0384] The server retrieves the necessary additional data (geographical information, population density data, the status of existing communications infrastructure, etc.) from its internal database. Specifically, it searches for and retrieves geographical information about the installation location specified by the user, population density data for the people living there, and the status of the existing communications infrastructure. The input data includes the latitude and longitude information entered by the user, and the output data includes the related geographical information, population density data, and communications infrastructure information, which are obtained as input data for the generative AI model.

[0385] Step 5:

[0386] The server searches for similar past cases and reference data, and runs the generative AI model to generate the optimal plan. Specifically, data from similar past cases is collected, and based on that, the generative AI model automatically generates the optimal base station installation and improvement plan. Input data includes geographic information, population data, communications infrastructure data, and past case data, and the output data is the optimal base station installation plan.

[0387] Step 6:

[0388] The server adjusts the content of the generated plan based on the analysis results of the emotion engine. Specifically, if the user is nervous, the plan is adjusted to explain things in simple and clear terms. The input data includes the content of the generated plan and the analysis results of the emotion engine, and the adjusted final plan is generated as output data.

[0389] Step 7:

[0390] The server provides the generated plan and estimate results to the user via a smartphone or tablet. Specifically, it sends the adjusted plan and estimate results to the user's device as an HTTP response. The input data includes the adjusted plan contents and estimate results, and the output data provides the plan information that is displayed to the user.

[0391] Step 8:

[0392] The user checks the plan provided and sends feedback if necessary. Specifically, the user inputs their opinions and requests for revisions to the plan through the app and sends them back to the server. The input data includes the user's feedback, and the output data is the feedback information sent to the server.

[0393] Step 9:

[0394] The server receives user feedback and reevaluates and modifies it based on analysis by the emotion engine. Specifically, it reruns the generative AI model to generate a new plan, which is then adjusted through the emotion engine. The input data includes the user's feedback and emotional state, and the output data is a reevaluated optimal plan.

[0395] By repeating this step, users can receive optimal base station installation and improvement plans that take their emotions into consideration.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] [Second embodiment]

[0400] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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."

[0412] The present invention relates to a system for proposing an optimal plan for installing and improving a mobile phone base station. This system is implemented in the following specific form.

[0413] First, the user uses a terminal to input the location, purpose, and conditions for installing the base station. Specifically, the user inputs the desired installation location (latitude and longitude), installation purpose (e.g., improving communication speed or expanding the coverage area), and installation conditions (e.g., budget limits, environmental conditions, legal regulations, etc.) on the terminal screen.

[0414] The entered data is converted into a digital format by the terminal and sent to the server, where the data format is verified and missing data is completed. For example, if the user forgets to enter information about the budget or a specific legal regulation, the server will send another request to the terminal to complete the information.

[0415] The server analyzes the received information and extracts the necessary data. Specifically, it investigates geographic information, population density data, and the state of the existing communications infrastructure based on the installation location. Next, the server references its internal database to search for similar past cases and reference data. This allows it to perform an analysis based on past successes and failures.

[0416] Once the analysis is complete, the server uses a generative AI model to automatically generate an optimal installation and improvement plan. This generative AI model calculates the most efficient plan by comprehensively considering a wide range of factors, including geographical characteristics, communication traffic data, budget, and legal regulations. For example, when generating a plan to improve communication speeds in Shinjuku Ward, the model proposes the optimal base station placement based on the area's peak communication traffic times and population distribution.

[0417] The generated plan includes detailed information such as the installation location, the type and number of required equipment, estimated installation and operating costs, implementation schedule, and notes on legal regulations and environmental conditions. The plan is then sent from the server to the terminal and provided to the user.

[0418] The user checks the plan received on their device and reviews its contents. If there are any questions or corrections to the plan or estimate, the user can send feedback from their device to the server. The server reevaluates the plan based on the received feedback, regenerates a revised plan as necessary, and provides it to the user.

[0419] As a concrete example, consider a base station installation plan aimed at improving communication speeds in urban areas. If a user inputs that they would like to install the base station in Shinjuku Ward, that their budget is within 50 million yen, and that they would like to consider environmental conditions, the server will use that information to propose the optimal layout and equipment. This proposal includes specific candidate locations for the base station, the necessary equipment, estimated installation and operating costs, and an implementation schedule, allowing the user to refer to it to proceed with a specific installation plan.

[0420] In this way, even if one lacks technical knowledge or experience, by using the system of the present invention, it is possible to efficiently and optimally install and improve base stations.

[0421] The processing flow will be explained below.

[0422] Step 1:

[0423] The user inputs information into the terminal, such as the location, purpose, and conditions of the base station installation. For example, detailed information such as the latitude and longitude of the installation location, purpose (improving communication speed or expanding the coverage area), and conditions (budget, legal regulations) is entered.

[0424] Step 2:

[0425] The terminal converts the user's input into a digital format, which includes cleaning and formatting the entered text and numeric data.

[0426] Step 3:

[0427] The device converts the information into a digital format and sends it to the server, using the necessary protocols to ensure that the data arrives at the server without being lost or tampered with.

[0428] Step 4:

[0429] The server receives the information sent from the device and verifies the data format. If any information is missing, the server sends another request to the device to complete the missing data.

[0430] Step 5:

[0431] The server references the database to search and obtain geographical information, population density data, and the status of existing communications infrastructure for the entered installation location.

[0432] Step 6:

[0433] The server searches for similar cases and reference data from the past, analyzes the necessary data, and prepares input data for the generative AI model based on the results of this analysis.

[0434] Step 7:

[0435] The server runs the AI ​​model to automatically generate optimal installation and improvement plans. The model takes into consideration geographical characteristics, communication traffic data, budget, legal regulations, etc. to calculate the optimal solution.

[0436] Step 8:

[0437] The server generates a detailed report of the generated plan, including specific installation locations, required equipment, estimated installation and operating costs, implementation schedules, and regulatory and environmental considerations.

[0438] Step 9:

[0439] The server generates a report and sends it to the terminal, including visual elements and brief explanations to make it easier for the user to understand.

[0440] Step 10:

[0441] The user checks the received plan on the device and reviews the contents. If there are any questions or corrections regarding the plan or estimate, the user can send feedback from the device to the server.

[0442] Step 11:

[0443] The server receives the feedback, re-evaluates and modifies the plan, and then generates a new optimal plan to serve to the user.

[0444] Example 1

[0445] 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."

[0446] With conventional base station installation planning systems, when generating optimal installation and improvement plans based on a variety of information, it took time to collect and analyze data, making it difficult to quickly provide highly accurate plans. Furthermore, they lacked the functionality to reevaluate and revise plans based on user feedback, making it impossible to provide appropriate plans even in situations where flexible responses were required.

[0447] 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.

[0448] In this invention, the server includes: a means for a user to input the installation location, installation purpose, and installation conditions of a communication base station; a means for converting the input information into a digital format and transmitting it; a means for analyzing the data based on the received information and extracting necessary data; a means for automatically generating an optimal installation and improvement plan using a generative AI model based on the analyzed data; and a means for providing the generated plan and estimate results to the user. This makes it possible to quickly and accurately collect and analyze a variety of information, provide highly accurate plans, and flexibly reevaluate and modify plans based on user feedback.

[0449] A "communications base station" is a wireless transmitting / receiving device installed to carry out mobile phone and wireless communications.

[0450] The "installation location" is information indicating the geographical location where the communication base station is physically installed.

[0451] "Installation purpose" is information indicating the reason or goal for installing a communication base station, such as improving communication speed or expanding the coverage area.

[0452] "Installation conditions" refers to information indicating constraints and conditions that must be considered when installing a communication base station, such as budget, environmental conditions, and legal regulations.

[0453] "Digital format" means information entered by a user that has been converted into a form that can be processed by a computer.

[0454] "Received information" refers to data regarding the installation location, installation purpose, and installation conditions that are input by the user and received by the server.

[0455] "Analyzing data" is the process of extracting and structuring the necessary data based on the received information.

[0456] "Extracting the necessary data" means extracting from the analyzed information the information necessary to generate optimal installation and improvement plans for communication base stations.

[0457] A "generative AI model" is an algorithm that uses artificial intelligence to generate optimal installation and improvement plans based on input data.

[0458] The "optimal installation and improvement plan" is a plan for the most efficient and effective placement and installation of communication base stations, calculated by comprehensively taking into account multiple factors.

[0459] "Plans and Estimates" refers to the installation plans and associated cost estimates created by the generative AI model.

[0460] "User" refers to a person who uses this system to create installation and improvement plans for communication base stations.

[0461] The "internal database" is a storage system that stores similar past cases and reference data.

[0462] "Feedback" refers to opinions and requests for corrections that a user provides to a generated plan.

[0463] "Plan reassessment and revision" is the process of reviewing the generated plan based on the feedback received and making changes as necessary.

[0464] This invention relates to a system that proposes optimal plans for installing and improving base stations in mobile phone communication networks. This system analyzes data based on the installation location, installation purpose, and installation conditions entered by the user, and generates optimal installation and improvement plans.

[0465] First, the user uses a terminal to input the desired installation location (e.g., the latitude and longitude of Shinjuku Ward), the purpose of installation (improving communication speed), and installation conditions (e.g., a budget of 50 million yen, consideration of environmental conditions and legal regulations). This information is converted into a digital format by the terminal and sent to the server. A secure protocol (e.g., HTTPS) is used for communication between the server and the terminal.

[0466] The server first verifies the received data and, if any information is missing, sends a completion request to the terminal, allowing the user to re-enter the missing information. The server then searches the system's internal database based on the information to obtain geographical information, population density data, the status of existing communications infrastructure, and other information. The internal database contains similar past cases and reference data, which are used for analysis.

[0467] The server then uses the analyzed data to apply a generative AI model to automatically generate an optimal installation and improvement plan. The generative AI model considers a wide range of factors, including geographical characteristics, communication traffic data, budget, and legal regulations, to propose an efficient and effective plan. The generated plan includes the installation location, the type and number of required equipment, estimated installation and operating costs, an implementation schedule, and notes on legal regulations and environmental conditions. This plan is then sent from the server to the terminal and provided to the user.

[0468] The user checks the plan received on their device and reviews its contents. If there are any questions or corrections to the plan, the user sends feedback from their device to the server. The server reevaluates the plan based on the received feedback, generates a revised plan as necessary, and provides it to the user again.

[0469] As a concrete example, if a base station is to be installed to improve communication speeds in Shinjuku Ward, the user inputs "Improve communication speeds in Shinjuku Ward, budget 50 million yen, considering environmental conditions" into the terminal. The terminal sends this information to the server, which then proposes the optimal base station placement based on geographical information and population data. This proposal includes candidate base station installation sites, required equipment, installation and operating costs, implementation schedule, etc.

[0470] An example prompt is, "Generate a base station installation plan to improve communication speeds in Shinjuku Ward. The budget should be within 50 million yen, and environmental conditions and legal regulations should be taken into consideration. Please provide a proposal that includes the installation location, required equipment, cost estimate, and implementation schedule."

[0471] This system enables efficient and appropriate installation and improvement of base stations, even for those with a lack of technical knowledge or experience.

[0472] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0473] Specific flow of system program processing

[0474] Step 1:

[0475] The user uses a terminal to input the desired installation location, purpose, and installation conditions. For example, they might input the latitude and longitude of Shinjuku Ward, the need to improve communication speed, a budget of 50 million yen, and environmental conditions to be taken into consideration. This input data is sent to the next step.

[0476] Input: desired installation location, installation purpose, installation conditions

[0477] Output: Input data (desired installation location, installation purpose, installation conditions)

[0478] Step 2:

[0479] The terminal converts the input data into a digital format. Specifically, it identifies the text information entered by the user as structured data and converts it into, for example, JSON format. This data conversion makes it easier for the server to process in the next step.

[0480] Input: Input data (desired installation location, installation purpose, installation conditions)

[0481] Output: Data converted to digital format

[0482] Step 3:

[0483] The device then transmits the data in digital format to the server, using a secure protocol (e.g., HTTPS) to protect the integrity and privacy of the data.

[0484] Input: Data converted into digital format

[0485] Output: Data sent to the server

[0486] Step 4:

[0487] The server validates the received data, checking for missing data or formatting issues, and generates a completion request if necessary. For example, if regulatory information is missing, a completion request is generated and sent to the device.

[0488] Input: Data sent to the server

[0489] Output: Validation results and completion request (if necessary)

[0490] Step 5:

[0491] The terminal receives the completion request and prompts the user for additional information. The user enters the missing information (e.g., regulatory details) into the terminal and resubmits the data.

[0492] Input: Completion request

[0493] Output: Imputed data

[0494] Step 6:

[0495] The server performs analysis based on the supplemented data. Specifically, it retrieves geographic information system (GIS) data and population density data and compares them with the input information. It also searches its internal database for similar cases and reference data from the past.

[0496] Input: Imputed data

[0497] Output: Analysis results (geographical information, population density data, past similar cases)

[0498] Step 7:

[0499] Based on the analysis results, the server uses a generative AI model to generate an optimal plan, taking into consideration geographical characteristics, communication traffic data, budget, legal regulations, etc. For example, it proposes the optimal placement of base stations by taking into account peak communication traffic times and population distribution in Shinjuku Ward.

[0500] Input: Analysis results (geographical information, population density data, past similar cases)

[0501] Output: The generated optimal plan

[0502] Step 8:

[0503] The server then sends the generated plan to the terminal, which includes the installation location, the type and number of required equipment, estimated installation and operation costs, an implementation schedule, and notes on legal regulations and environmental conditions.

[0504] Input: Generated optimal plan

[0505] Output: Plan sent to the device

[0506] Step 9:

[0507] The user checks the plan received on the device and reviews its contents. If there are any questions or corrections to the plan, the user can send feedback from the device to the server. Specifically, if the user is not satisfied with the cost estimate items, the user can include the reasons in the feedback.

[0508] Input: Plan sent to device

[0509] Output: User feedback

[0510] Step 10:

[0511] The server receives the feedback and re-evaluates the plan, reusing the generative AI model as needed to generate a new plan with adjustments and modifications, such as revising equipment and layout to stay within budget.

[0512] Input: User feedback

[0513] Output: revised plan

[0514] Step 11:

[0515] The server sends the revised plan to the terminal and provides it to the user, allowing the user to review the optimized plan again and make a final decision.

[0516] Input: revised plan

[0517] Output: The revised plan sent to the device

[0518] (Application example 1)

[0519] 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."

[0520] Determining efficient product placement and delivery routes is extremely important in the operation of a logistics center. However, previous methods required manual determination of optimal placement and routes while taking into account multiple factors such as the type and quantity of goods, budget, and delivery date, which required a significant amount of time and effort. Furthermore, the accuracy of optimization was insufficient, making it difficult to improve the efficiency of the entire logistics center. To solve these issues, a system that utilizes existing data to automatically and optimally propose product placement and delivery routes is needed.

[0521] 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.

[0522] In this invention, the server includes a means for a user to input placement locations, purposes, and conditions, a processing means including a generation AI model that analyzes data based on the input information and automatically generates an optimal placement and route plan, a means for providing the generated plan and estimate results to the user, and a means for proposing optimal item placement and delivery routes based on the placement data, thereby enabling efficient and automatic optimization of item placement and delivery routes in a logistics center.

[0523] "User" refers to a person who uses the system to optimize the placement of goods and delivery routes.

[0524] "Location" refers to the specific location within the logistics center where the item is to be placed.

[0525] "Objective" refers to the goal you want to achieve by optimizing the placement of goods and delivery routes, such as improving efficiency or reducing costs.

[0526] "Conditions" refer to constraints or requirements that must be taken into account when optimizing the placement of goods and delivery routes, such as budget, weight, and legal regulations.

[0527] "Data" refers to the information entered by the User and any additional information related thereto.

[0528] "Analysis" refers to the calculations and evaluations that are carried out to derive optimal layout and route plans based on input data.

[0529] "Optimal placement and route plan" refers to a plan that designs the placement of goods and delivery routes in the most efficient way.

[0530] "Generative AI model" refers to an artificial intelligence algorithm that automatically generates optimal placement and route plans based on input information.

[0531] "Processing means" refers to the function that analyzes the information entered by the user and automatically generates the optimal plan using a generative AI model.

[0532] "Estimation results" refers to cost and schedule predictions based on the generated optimal layout and route plan.

[0533] "Feedback" refers to the evaluation or opinion provided by a user regarding a generated plan.

[0534] "Plan reevaluation and revision" refers to the process of reviewing the generated plan based on feedback from users and revising it as necessary.

[0535] "System" refers to the entire set of hardware and software including the above means and models.

[0536] The present invention relates to a system for automatically optimizing the allocation of goods and delivery routes in a logistics center. Specific embodiments of the system are described below.

[0537] First, the user uses a device such as a smartphone or tablet to input the type, quantity, weight, delivery area, budget, etc. The input information is converted into a digital format and sent to the server.

[0538] The server analyzes the data based on the received information and extracts the necessary data. Specifically, based on the input product information, budget, and date and time, it searches for similar past cases and reference data and uses them to analyze the generated AI model.

[0539] Next, the server automatically generates optimal product placement and delivery routes using a generative AI model that comprehensively considers multiple factors (product type, quantity, weight, budget, delivery area, date, etc.) to calculate the most efficient placement plan and delivery route.

[0540] The generated plan includes information on the location, the type and number of required facilities, estimated installation and operation costs, implementation schedule, and notes on legal regulations and environmental conditions. This information is sent from the server to the terminal and provided to the user.

[0541] The user checks the plan received on their device and reviews its contents. If there are any questions or corrections to the plan or estimate, the user can send feedback from their device to the server. The server reevaluates the plan based on the received feedback, regenerates a revised plan as necessary, and provides it to the user.

[0542] The system's program uses the requests library (Python) to send API requests and executes data analysis using a generative AI model on the server. The main hardware used is devices such as smartphones and tablets. The generative AI model also runs on the server, calculating optimal placement and route plans.

[0543] As a specific example, we will explain the case where we propose the optimal product placement and delivery route in a logistics center under the following conditions.

[0544] Logistics Center ID: 12345A

[0545] Goods:

[0546] Material ID: A111, Quantity: 100, Weight: 2.5

[0547] Material ID: B222, Quantity: 200, Weight: 3.0

[0548] Budget: 500,000 yen

[0549] Date: 2023-10-01

[0550] Example prompt sentence:

[0551] Please propose the optimal product layout and delivery route for the distribution center under the following conditions:

[0552] Logistics Center ID: 12345A

[0553] Goods:

[0554] Material ID: A111, Quantity: 100, Weight: 2.5

[0555] Material ID: B222, Quantity: 200, Weight: 3.0

[0556] Budget: 500,000 yen

[0557] Date: 2023-10-01

[0558] This type of system makes it possible to propose efficient and optimal product placement and delivery routes even for those lacking technical knowledge or experience.

[0559] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0560] Step 1:

[0561] Using a smartphone or tablet, the user inputs information such as the type, quantity, weight, budget, and date of items in the distribution center on the screen, and the conditions set by the user are converted into a digital format.

[0562] input:

[0563] Type of item

[0564] quantity

[0565] Weight

[0566] budget

[0567] date

[0568] output:

[0569] Input information converted into digital format

[0570] Step 2:

[0571] The device sends the entered digital format information to the server, which transfers the information using API requests.

[0572] input:

[0573] Server API endpoint

[0574] Input information in digital format

[0575] output:

[0576] Input information sent to the server

[0577] Step 3:

[0578] The server begins data analysis based on the received input information. Specifically, it searches for similar past cases and reference data to collect the information necessary to analyze the generative AI model.

[0579] input:

[0580] Submitted input information

[0581] output:

[0582] Collected historical and reference data

[0583] Step 4:

[0584] The server uses a generative AI model to automatically generate optimal plans for product placement and delivery routes. The AI ​​algorithm calculates the optimal placement and route by taking into account multiple factors based on input information and collected data.

[0585] input:

[0586] Input information

[0587] Data collected

[0588] output:

[0589] Optimal product placement and delivery route planning

[0590] Step 5:

[0591] The server sends the generated deployment plan and estimate results (cost, schedule, etc.) to the terminal, allowing the user to view the optimal plan.

[0592] input:

[0593] Generated Plan

[0594] Estimate results

[0595] output:

[0596] Plans and quote results sent to your device

[0597] Step 6:

[0598] The user reviews the plan and estimate results received on the device, and if there are any questions or corrections regarding the plan or estimate, they provide feedback via the device.

[0599] input:

[0600] Received plan and quote results

[0601] User Feedback

[0602] output:

[0603] Feedback Information

[0604] Step 7:

[0605] The server reevaluates the plan based on user feedback and modifies it as necessary. It then uses the generative AI model again to create a revised plan and sends it to the device.

[0606] input:

[0607] Feedback Information

[0608] Data collected

[0609] output:

[0610] Revised Plan

[0611] In this way, optimal product placement and delivery routes are provided to users, making it possible to improve the operational efficiency of logistics centers.

[0612] 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.

[0613] The present invention relates to a system that proposes optimal plans for the installation and improvement of mobile phone base stations. This system provides plans that are more suitable for users by combining it with an emotion engine that recognizes user emotions.

[0614] First, the user uses a terminal to input the location, purpose, and conditions for installing the base station. Specifically, the user inputs the latitude and longitude of the installation location, the purpose (e.g., improving communication speed, expanding the coverage area), and the conditions (e.g., budget, environmental conditions, legal regulations).

[0615] The input information is converted into a digital format by the device and sent to the server. At this time, the emotion engine analyzes the user's emotional state at the time of input. For example, it analyzes information such as whether the user is tense or relaxed based on the input text, input speed, and input device usage.

[0616] The server analyzes the received information and, if any information is missing, requests it again from the terminal to complete the data.The server then references its internal database to search for and obtain geographical information, population density data, the status of existing communications infrastructure, and other information related to the input installation location.

[0617] Next, the server searches for similar past cases and reference data, and analyzes the necessary data. The analysis results are used as input data for the generative AI model. Based on the input information and past data, the generative AI model automatically generates an optimal base station installation and improvement plan. This model comprehensively takes into account multiple factors, including geographical characteristics, communication traffic data, budget, and legal regulations.

[0618] The generated plan includes the specific location of the installation site, the type and number of required equipment, estimated installation and operation costs, an implementation schedule, and notes on legal regulations and environmental conditions. The emotion engine takes the user's emotional state into account and adjusts the plan presentation accordingly. For example, if the user is nervous, the plan will be explained in more concise and clear language.

[0619] The server sends the plan and estimate results it has created to the device and provides them to the user. The user checks the plan received on the device and reviews its contents. If the user has any questions or requests for corrections regarding the plan or estimate, they can send feedback from their device to the server. At this time, the emotion engine analyzes the user's emotions when providing feedback and reevaluates the plan as necessary.

[0620] After analyzing the feedback, the server re-runs the generative AI model to generate a revised, optimal plan. The server then re-presents this revised plan to the user, while making adjustments to increase user satisfaction through the emotion engine.

[0621] As a concrete example, consider the installation of a base station to improve communication speeds in Shinjuku Ward. A user inputs, "I would like to install a base station in Shinjuku Ward to improve communication speeds, with a budget of 50 million yen or less and taking environmental conditions into consideration." If the user is nervous at this time, the emotion engine can present a plan in language that will reassure them.

[0622] In this way, even users who lack technical knowledge or experience can use the system of the present invention to efficiently and optimally install and improve base stations, and by taking user feelings into consideration, they can provide more user-friendly services.

[0623] The processing flow will be explained below.

[0624] Step 1:

[0625] The user uses a terminal to input information such as the base station installation location, purpose, and conditions, including the desired latitude and longitude, the purpose (such as improving communication speed or expanding the coverage area), budget limits, and legal restrictions.

[0626] Step 2:

[0627] The device converts the user's input into a digital format and then uses an emotion engine to analyze the user's emotional state (e.g., nervousness, relaxation) as they type, including keystroke speed and emotional analysis of the input.

[0628] Step 3:

[0629] The terminal transmits the information converted into a digital format and the analyzed emotional state information to a server.

[0630] Step 4:

[0631] The server receives the information sent from the device, verifies the data format, and, if necessary, requests the device to complete any missing data.

[0632] Step 5:

[0633] The server refers to the database to search and obtain geographical information, population density data, and information on existing communications infrastructure for the desired installation location.

[0634] Step 6:

[0635] The server searches for and analyzes similar past cases and reference data, thereby preparing input data for the generative AI model.

[0636] Step 7:

[0637] The server runs a generative AI model to automatically generate optimal installation and improvement plans, taking into account multiple factors such as geographical characteristics, communication traffic data, budget, and legal regulations.

[0638] Step 8:

[0639] The server generates a report of the plan, which includes the installation location, required equipment, estimated installation and operating costs, implementation schedule, and regulatory and environmental considerations.

[0640] Step 9:

[0641] The emotion engine adjusts the plan presentation based on the user's emotional state, for example, providing concise, positive explanations if the user is nervous, or providing detailed information if the user is relaxed.

[0642] Step 10:

[0643] The server sends the generated report and the adjusted plan to the device.

[0644] Step 11:

[0645] The user checks the received plan on the device, reviews its contents, and sends feedback if there are any questions or corrections they would like to make regarding the plan or estimate.

[0646] Step 12:

[0647] Along with the feedback, the emotion engine analyzes the user's emotions at the time of the feedback, allowing the server to take the most appropriate action.

[0648] Step 13:

[0649] The server re-runs the generative AI model to re-evaluate and revise the plan based on the feedback, generating a revised plan that the emotion engine adjusts again before providing to the user again.

[0650] In this way, this system goes through a series of processing steps to provide efficient and optimal base station installation and improvement plans, and by taking user emotions into consideration, it realizes a more user-friendly service.

[0651] Example 2

[0652] 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."

[0653] Conventional base station installation plan creation systems have difficulty presenting efficient plans because they do not take into account the user's emotional state. Furthermore, they are unable to effectively utilize similar past data, making it difficult to generate optimal plans. Furthermore, even if users provide feedback on the plan, appropriate revisions based on that feedback are not made.

[0654] 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.

[0655] In this invention, the server includes: a means for a user to input the base station installation location, purpose, and conditions; a means for converting the input information into a digital format and transmitting it to the server; an emotion engine including means for performing emotion analysis based on the transmitted information; a means for analyzing the received information and completing any missing information; a means for referencing an internal database to acquire relevant geographic information, population density data, and the status of existing communication infrastructure; a means for analyzing similar past cases and reference data and using them as input data for a generative AI model; a means for automatically generating an optimal base station installation and improvement plan using the generative AI model; and a emotion engine including means for adjusting the presentation content of the generated plan taking into account the user's emotional state; a means for providing the generated plan and estimate results to the user; and a means for receiving user feedback, analyzing the user's emotional state at the time of the feedback, and reevaluating and modifying the plan. This enables the generation and provision of an efficient and optimal base station installation and improvement plan while taking into account the user's emotions.

[0656] A "user" is an end user who inputs the installation location, purpose, conditions, etc. of a base station.

[0657] A "terminal" is an electronic device that allows a user to input information and convert that information into a digital format.

[0658] A "server" is a computer system that receives digitally formatted information sent from a terminal and performs processing such as analysis, search, and generation based on that information.

[0659] The "emotion engine" is a software module that analyzes the user's emotional state at the time of input and adjusts the service content based on the analysis results.

[0660] "Digital format" refers to a data format in which information entered by a user is converted into a format that can be processed electronically.

[0661] An "internal database" is a storage device for storing data such as geographical information, population density data, and the status of existing communication infrastructure.

[0662] The "generative AI model" is an artificial intelligence model that automatically generates optimal base station installation and improvement plans based on similar past cases and reference data.

[0663] The "plan" is an installation and improvement strategy that includes the specific installation location of the base station, the type and number of required equipment, estimated installation and operation costs, implementation schedule, and points to note regarding legal regulations and environmental conditions.

[0664] "Feedback" is response information that allows the user to check the plan and convey any questions or requests for corrections to the server.

[0665] "Analysis" is the process by which the server extracts and understands the necessary information based on the data it receives.

[0666] "Search" is the process by which the server finds the necessary information from its internal database or past data.

[0667] "Reassessment" is the process of receiving feedback, reviewing existing plans based on that feedback, and making adjustments as needed.

[0668] MODE FOR CARRYING OUT THE INVENTION

[0669] The present invention is a system that proposes optimal plans for installing and improving base stations. This system aims to provide plans that are more suitable for each user by combining it with an emotion engine that recognizes the user's emotions. A specific example of the system is described below.

[0670] First, the user uses a terminal to input the base station installation location, purpose, and conditions. Specific input items include the latitude and longitude of the installation location, purpose (e.g., improving communication speed, expanding coverage area), and conditions (e.g., budget, environmental conditions, legal regulations). This input information is converted into a digital format by the terminal and sent to the server.

[0671] The emotion engine kicks in at this point and analyzes the user's emotional state as they type, for example, by looking at the text they type, their typing speed, and how they use their input device to determine whether they are tense or relaxed.

[0672] The server then analyzes the received information and, if any information is missing, requests it again from the terminal to complete the data. At this point, the server references its internal database to search and obtain geographical information, population density data, the status of existing communications infrastructure, and other information related to the input installation location.

[0673] The system then searches for similar past cases and reference data, and uses the analysis results as input data for the generative AI model. This generative AI model references existing base station installation cases in Osaka City and Tokyo, for example, and automatically generates an optimal base station installation and improvement plan based on the input information and past data. The model comprehensively takes into account multiple factors, including geographical characteristics, communication traffic data, budget, and legal regulations.

[0674] The plan includes the specific location of the installation site, the type and quantity of equipment required, estimated installation and operation costs, an implementation schedule, and notes on legal regulations and environmental conditions. The emotion engine takes the user's emotional state into account and adjusts the plan presentation accordingly. For example, if the user is nervous, the plan will be explained in more concise and clear language.

[0675] The server sends the plan and estimate results to the terminal and provides them to the user. The user can then use the terminal to check and review the contents. If the user has any questions or requests for corrections regarding the plan or estimate, they can send feedback from their terminal to the server. At this time, the emotion engine analyzes the user's emotional state at the time of feedback and reevaluates the plan as necessary.

[0676] Finally, the server re-runs the generative AI model based on the analyzed feedback to generate a revised optimal plan, which it then provides to the user again while making adjustments to increase user satisfaction through the emotion engine.

[0677] Specific examples

[0678] As a concrete example, consider the installation of a base station to improve communication speeds in Shinjuku Ward. The user inputs, "I would like to install a base station to improve communication speeds in Shinjuku Ward, with a budget of 50 million yen or less and taking environmental conditions into consideration." At this time, the emotion engine recognizes the user's state of tension and presents a plan in language that gives a sense of security.

[0679] Prompt Sentence Examples

[0680] "We would like to install a base station in Shinjuku Ward to improve communication speeds. The budget is within 50 million yen, and we would like you to take into consideration the environmental conditions. Please provide us with the optimal plan based on data from similar cases in the past."

[0681] In this way, even users who lack technical knowledge or experience can use the system of the present invention to efficiently and optimally install and improve base stations, and by taking user feelings into consideration, they can provide more user-friendly services.

[0682] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0683] Step 1:

[0684] The user uses a terminal to input the location, purpose, and conditions for installing the base station. Specifically, the user enters, "I want to install a base station in Shinjuku Ward to improve communication speeds, with a budget of 50 million yen or less and taking environmental conditions into consideration." This information becomes the initial input data for the program.

[0685] Step 2:

[0686] The terminal converts the information entered by the user into digital format. The converted information includes the installation location (latitude and longitude), purpose (improvement of communication speed), and conditions (budget of 50 million yen or less, environmental conditions). The converted information is sent to the server.

[0687] Step 3:

[0688] The server receives the information sent from the device. This information includes the latitude and longitude of the installation location, purpose, and conditions. The emotion engine then analyzes the user's emotional state. Specifically, it analyzes the emotion of the input text and keyboard input speed to determine whether the user is tense or relaxed.

[0689] Step 4:

[0690] The server analyzes the received information, identifies any missing information, and sends another request to the device. For example, if it determines that "budget breakdown details are missing," it requests the device to "please tell me the budget breakdown." This request is displayed to the user.

[0691] Step 5:

[0692] The terminal presents the request from the server to the user. The user enters the missing information, which the terminal then converts back into digital format and sends to the server. The input data here might be details of a budget breakdown.

[0693] Step 6:

[0694] The server receives the information resent by the user and refers to its internal database to search and obtain relevant geographical information, population density data, and the status of existing communication infrastructure. For example, it collects geographical information and population density data for Shinjuku Ward. This information is used for analysis.

[0695] Step 7:

[0696] The server searches and analyzes similar cases and reference data from the past, such as base station installation cases to improve communication speeds in other cities, and uses this data as input data for the generative AI model.

[0697] Step 8:

[0698] The server runs the generative AI model to automatically generate an optimal base station installation and improvement plan. The generative AI model takes into account many data points (geographical characteristics, communication traffic, budget, legal regulations, etc.) and generates a plan that includes specific installation locations, required equipment, operating costs, implementation schedules, etc.

[0699] Step 9:

[0700] The generated plan is adjusted using an emotion engine according to the user's emotional state. For example, if the user is nervous, the plan explanation is adjusted to be more concise and clear. This adjusted plan becomes the final output data.

[0701] Step 10:

[0702] The server sends the adjusted plan and estimate results to the device, which then provides them to the user, who then confirms and reviews the plan. For example, if the user feels that the budget breakdown of the plan is unclear, that feedback is recorded.

[0703] Step 11:

[0704] The user sends feedback from their device to the server. The server receives this feedback and uses an emotion engine to analyze the user's emotional state at the time of the feedback. For example, if it is determined to be "dissatisfied," it considers countermeasures.

[0705] Step 12:

[0706] The server re-runs the generative AI model based on the feedback and generates a revised, optimal plan. The revised plan includes more detailed breakdowns and additional information to enhance user satisfaction.

[0707] Step 13:

[0708] The server sends the revised plan back to the terminal, which then provides it to the user. The user reviews the plan again and gives a final rating. This cycle is repeated until the user is satisfied.

[0709] In this way, by understanding the specific processing flow of the invention, it becomes clear how the system operates and how it proposes optimal base station installations to users.

[0710] (Application example 2)

[0711] 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."

[0712] The problem to be solved by this invention is to provide users with mobile phone base station installation and improvement plans that allow even users with little technical knowledge or experience to easily understand and implement efficient and optimal plans. Another object of this invention is to increase user satisfaction by taking into account the user's emotional state and making flexible suggestions accordingly.

[0713] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input the installation location, purpose, and conditions of a base station; means including a generation AI model that analyzes data based on the input information and automatically generates an optimal installation and improvement plan; means for analyzing the user's emotional state using an emotion engine and adjusting the generated plan based on the analysis results; and means for providing the generated plan and estimate results to the user. This makes it possible for even users who lack technical knowledge or experience to obtain an optimal base station installation and improvement plan that is easily adjusted, and allows for flexible responses according to the user's emotional state.

[0714] "User" refers to a person who inputs the location, purpose, and conditions of a base station through the system and checks and reviews the generated plan.

[0715] The "location of the base station" is information indicating the specific physical location where the base station is installed for mobile phone communications.

[0716] "Purpose" refers to the goal of installing a base station, and refers to the specific things that you want to achieve, such as improving communication speeds or expanding the coverage area.

[0717] "Conditions" refer to various constraints and requirements for installing a base station, including budget, environmental conditions, and legal regulations.

[0718] An "emotion engine" is a software module for analyzing a user's emotional state, and has the function of evaluating the user's emotions based on the input text and the status of the device being used.

[0719] A "generative AI model" is an artificial intelligence model that automatically generates optimal base station installation and improvement plans based on input information and past data.

[0720] A "Plan" is a specific implementation plan for the installation of a base station, including the specific location of the installation site, the type and number of facilities required, estimated installation and operation costs, and an implementation schedule.

[0721] "Feedback" is information indicating opinions and desired modifications made by the user to the generated plan.

[0722] A "server" is a remote computer system that receives, analyzes, and processes information sent by users.

[0723] "Tension" refers to the emotional state of anxiety or stress that a user experiences when using a system.

[0724] "Rest API" is a standard protocol for data communication between a server and a client, and is a communication method that uses HTTP.

[0725] The present invention relates to a system that allows users to propose optimal plans for the installation and improvement of mobile phone base stations. This system has an emotion-responsive function that makes proposals taking into account the user's emotional state.

[0726] Hardware and software used

[0727] Hardware: smartphone or tablet (for user input and display), server (for data processing and storage)

[0728] software:

[0729] Implementation in Python

[0730] EmotionEngine: A library for analyzing user emotions

[0731] AI model: An artificial intelligence model that generates optimal plans

[0732] REST API: Data communication protocol between server and client

[0733] Overall system processing flow

[0734] 1. User Input

[0735] Users use their smartphones or tablets to input the base station installation location, purpose, and conditions (e.g., budget, environmental conditions, legal regulations).The emotion engine then analyzes the user's input speed and all aspects of the device being used to obtain emotion data.

[0736] 2. Converting information into digital format and transmitting it

[0737] The input information is converted into a digital format and sent to a server, which then begins analyzing the data based on the received information and the user's emotional state.

[0738] 3. Data analysis and plan generation

[0739] The server references its internal database based on the input information to obtain geographical information, population density data, the state of existing communications infrastructure, and other information. It also searches for similar past cases and reference data, and inputs this information into the generative AI model. The generative AI model then comprehensively analyzes this information to generate an optimal base station installation and improvement plan.

[0740] 4. Adjusting the plan with the emotion engine

[0741] The server adjusts the generated plan based on the analysis results of the emotion engine. For example, if the user is nervous, the server may explain the plan in simple and clear terms.

[0742] 5. Providing the plan and receiving feedback

[0743] The generated plan and estimate results are provided to the user via a smartphone or tablet. The user can review the plan and send feedback to the server. This feedback is also analyzed by the emotion engine, and the plan is reevaluated and revised as necessary.

[0744] Specific examples

[0745] Consider installing a base station to improve communication speeds in Shinjuku Ward. A user inputs, "I would like to install a base station in Shinjuku Ward to improve communication speeds, with a budget of 50 million yen or less and taking environmental conditions into consideration." If the user is nervous, the emotion engine can present the plan in language that will reassure them.

[0746] Prompt Sentence Examples

[0747] "Please propose an optimization plan to improve the response speed of electronic payments within Shinjuku Ward. The budget is 5 million yen. If users are unsure, please explain it in simple terms to reassure them."

[0748] By feeding this prompt into a generative AI model, we can obtain a more user-friendly and emotionally sensitive plan.

[0749] This system allows even users with limited technical knowledge or experience to receive optimal base station installation and improvement plans, and also makes it possible to make suggestions that reflect the user's emotions.

[0750] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0751] Step 1:

[0752] The user uses a smartphone or tablet to input the base station installation location, purpose, and conditions. Specifically, the user launches the app and inputs the latitude and longitude of the installation location, purpose (improving communication speed, expanding coverage area, etc.), budget, environmental conditions, etc. The input data includes latitude and longitude information, text data of the purpose, detailed conditions, etc. The input data is temporarily stored on the device.

[0753] Step 2:

[0754] The terminal converts the acquired user input data into a digital format and sends it to the server. Specifically, the input data is loaded into a standard data format such as JSON format and sent to the server using the HTTP protocol. At this time, the input data includes the location, purpose, conditions, etc. entered by the user, and is sent to the server as output data.

[0755] Step 3:

[0756] The server receives the transmitted data and analyzes the user's emotional state using an emotion engine. Specifically, the emotional state (e.g., tension, anxiety, relaxation, etc.) is evaluated based on the user's input speed and text content. Input data includes input speed, input text, and the status of the device being used. As a result of the analysis, the emotion engine outputs an emotional state such as "tension," "anxiety," or "relaxation."

[0757] Step 4:

[0758] The server retrieves the necessary additional data (geographical information, population density data, the status of existing communications infrastructure, etc.) from its internal database. Specifically, it searches for and retrieves geographical information about the installation location specified by the user, population density data for the people living there, and the status of the existing communications infrastructure. The input data includes the latitude and longitude information entered by the user, and the output data includes the related geographical information, population density data, and communications infrastructure information, which are obtained as input data for the generative AI model.

[0759] Step 5:

[0760] The server searches for similar past cases and reference data, and runs the generative AI model to generate the optimal plan. Specifically, data from similar past cases is collected, and based on that, the generative AI model automatically generates the optimal base station installation and improvement plan. Input data includes geographic information, population data, communications infrastructure data, and past case data, and the output data is the optimal base station installation plan.

[0761] Step 6:

[0762] The server adjusts the content of the generated plan based on the analysis results of the emotion engine. Specifically, if the user is nervous, the plan is adjusted to explain things in simple and clear terms. The input data includes the content of the generated plan and the analysis results of the emotion engine, and the adjusted final plan is generated as output data.

[0763] Step 7:

[0764] The server provides the generated plan and estimate results to the user via a smartphone or tablet. Specifically, it sends the adjusted plan and estimate results to the user's device as an HTTP response. The input data includes the adjusted plan contents and estimate results, and the output data provides the plan information that is displayed to the user.

[0765] Step 8:

[0766] The user checks the plan provided and sends feedback if necessary. Specifically, the user inputs their opinions and requests for revisions to the plan through the app and sends them back to the server. The input data includes the user's feedback, and the output data is the feedback information sent to the server.

[0767] Step 9:

[0768] The server receives user feedback and reevaluates and modifies it based on analysis by the emotion engine. Specifically, it reruns the generative AI model to generate a new plan, which is then adjusted through the emotion engine. The input data includes the user's feedback and emotional state, and the output data is a reevaluated optimal plan.

[0769] By repeating this step, users can receive optimal base station installation and improvement plans that take their emotions into consideration.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] [Third embodiment]

[0774] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0775] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0776] 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).

[0777] 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.

[0778] 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.

[0779] 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).

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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."

[0786] The present invention relates to a system for proposing an optimal plan for installing and improving a mobile phone base station. This system is implemented in the following specific form.

[0787] First, the user uses a terminal to input the location, purpose, and conditions for installing the base station. Specifically, the user inputs the desired installation location (latitude and longitude), installation purpose (e.g., improving communication speed or expanding the coverage area), and installation conditions (e.g., budget limits, environmental conditions, legal regulations, etc.) on the terminal screen.

[0788] The entered data is converted into a digital format by the terminal and sent to the server, where the data format is verified and missing data is completed. For example, if the user forgets to enter information about the budget or a specific legal regulation, the server will send another request to the terminal to complete the information.

[0789] The server analyzes the received information and extracts the necessary data. Specifically, it investigates geographic information, population density data, and the state of the existing communications infrastructure based on the installation location. Next, the server references its internal database to search for similar past cases and reference data. This allows it to perform an analysis based on past successes and failures.

[0790] Once the analysis is complete, the server uses a generative AI model to automatically generate an optimal installation and improvement plan. This generative AI model calculates the most efficient plan by comprehensively considering a wide range of factors, including geographical characteristics, communication traffic data, budget, and legal regulations. For example, when generating a plan to improve communication speeds in Shinjuku Ward, the model proposes the optimal base station placement based on the area's peak communication traffic times and population distribution.

[0791] The generated plan includes detailed information such as the installation location, the type and number of required equipment, estimated installation and operating costs, implementation schedule, and notes on legal regulations and environmental conditions. The plan is then sent from the server to the terminal and provided to the user.

[0792] The user checks the plan received on their device and reviews its contents. If there are any questions or corrections to the plan or estimate, the user can send feedback from their device to the server. The server reevaluates the plan based on the received feedback, regenerates a revised plan as necessary, and provides it to the user.

[0793] As a concrete example, consider a base station installation plan aimed at improving communication speeds in urban areas. If a user inputs that they would like to install the base station in Shinjuku Ward, that their budget is within 50 million yen, and that they would like to consider environmental conditions, the server will use that information to propose the optimal layout and equipment. This proposal includes specific candidate locations for the base station, the necessary equipment, estimated installation and operating costs, and an implementation schedule, allowing the user to refer to it to proceed with a specific installation plan.

[0794] In this way, even if one lacks technical knowledge or experience, by using the system of the present invention, it is possible to efficiently and optimally install and improve base stations.

[0795] The processing flow will be explained below.

[0796] Step 1:

[0797] The user inputs information into the terminal, such as the location, purpose, and conditions of the base station installation. For example, detailed information such as the latitude and longitude of the installation location, purpose (improving communication speed or expanding the coverage area), and conditions (budget, legal regulations) is entered.

[0798] Step 2:

[0799] The terminal converts the user's input into a digital format, which includes cleaning and formatting the entered text and numeric data.

[0800] Step 3:

[0801] The device converts the information into a digital format and sends it to the server, using the necessary protocols to ensure that the data arrives at the server without being lost or tampered with.

[0802] Step 4:

[0803] The server receives the information sent from the device and verifies the data format. If any information is missing, the server sends another request to the device to complete the missing data.

[0804] Step 5:

[0805] The server references the database to search and obtain geographical information, population density data, and the status of existing communications infrastructure for the entered installation location.

[0806] Step 6:

[0807] The server searches for similar cases and reference data from the past, analyzes the necessary data, and prepares input data for the generative AI model based on the results of this analysis.

[0808] Step 7:

[0809] The server runs the AI ​​model to automatically generate optimal installation and improvement plans. The model takes into consideration geographical characteristics, communication traffic data, budget, legal regulations, etc. to calculate the optimal solution.

[0810] Step 8:

[0811] The server generates a detailed report of the generated plan, including specific installation locations, required equipment, estimated installation and operating costs, implementation schedules, and regulatory and environmental considerations.

[0812] Step 9:

[0813] The server generates a report and sends it to the terminal, including visual elements and brief explanations to make it easier for the user to understand.

[0814] Step 10:

[0815] The user checks the received plan on the device and reviews the contents. If there are any questions or corrections regarding the plan or estimate, the user can send feedback from the device to the server.

[0816] Step 11:

[0817] The server receives the feedback, re-evaluates and modifies the plan, and then generates a new optimal plan to serve to the user.

[0818] Example 1

[0819] 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."

[0820] With conventional base station installation planning systems, when generating optimal installation and improvement plans based on a variety of information, it took time to collect and analyze data, making it difficult to quickly provide highly accurate plans. Furthermore, they lacked the functionality to reevaluate and revise plans based on user feedback, making it impossible to provide appropriate plans even in situations where flexible responses were required.

[0821] 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.

[0822] In this invention, the server includes: a means for a user to input the installation location, installation purpose, and installation conditions of a communication base station; a means for converting the input information into a digital format and transmitting it; a means for analyzing the data based on the received information and extracting necessary data; a means for automatically generating an optimal installation and improvement plan using a generative AI model based on the analyzed data; and a means for providing the generated plan and estimate results to the user. This makes it possible to quickly and accurately collect and analyze a variety of information, provide highly accurate plans, and flexibly reevaluate and modify plans based on user feedback.

[0823] A "communications base station" is a wireless transmitting / receiving device installed to carry out mobile phone and wireless communications.

[0824] The "installation location" is information indicating the geographical location where the communication base station is physically installed.

[0825] "Installation purpose" is information indicating the reason or goal for installing a communication base station, such as improving communication speed or expanding the coverage area.

[0826] "Installation conditions" refers to information indicating constraints and conditions that must be considered when installing a communication base station, such as budget, environmental conditions, and legal regulations.

[0827] "Digital format" means information entered by a user that has been converted into a form that can be processed by a computer.

[0828] "Received information" refers to data regarding the installation location, installation purpose, and installation conditions that are input by the user and received by the server.

[0829] "Analyzing data" is the process of extracting and structuring the necessary data based on the received information.

[0830] "Extracting the necessary data" means extracting from the analyzed information the information necessary to generate optimal installation and improvement plans for communication base stations.

[0831] A "generative AI model" is an algorithm that uses artificial intelligence to generate optimal installation and improvement plans based on input data.

[0832] The "optimal installation and improvement plan" is a plan for the most efficient and effective placement and installation of communication base stations, calculated by comprehensively taking into account multiple factors.

[0833] "Plans and Estimates" refers to the installation plans and associated cost estimates created by the generative AI model.

[0834] "User" refers to a person who uses this system to create installation and improvement plans for communication base stations.

[0835] The "internal database" is a storage system that stores similar past cases and reference data.

[0836] "Feedback" refers to opinions and requests for corrections that a user provides to a generated plan.

[0837] "Plan reassessment and revision" is the process of reviewing the generated plan based on the feedback received and making changes as necessary.

[0838] This invention relates to a system that proposes optimal plans for installing and improving base stations in mobile phone communication networks. This system analyzes data based on the installation location, installation purpose, and installation conditions entered by the user, and generates optimal installation and improvement plans.

[0839] First, the user uses a terminal to input the desired installation location (e.g., the latitude and longitude of Shinjuku Ward), the purpose of installation (improving communication speed), and installation conditions (e.g., a budget of 50 million yen, consideration of environmental conditions and legal regulations). This information is converted into a digital format by the terminal and sent to the server. A secure protocol (e.g., HTTPS) is used for communication between the server and the terminal.

[0840] The server first verifies the received data and, if any information is missing, sends a completion request to the terminal, allowing the user to re-enter the missing information. The server then searches the system's internal database based on the information to obtain geographical information, population density data, the status of existing communications infrastructure, and other information. The internal database contains similar past cases and reference data, which are used for analysis.

[0841] The server then uses the analyzed data to apply a generative AI model to automatically generate an optimal installation and improvement plan. The generative AI model considers a wide range of factors, including geographical characteristics, communication traffic data, budget, and legal regulations, to propose an efficient and effective plan. The generated plan includes the installation location, the type and number of required equipment, estimated installation and operating costs, an implementation schedule, and notes on legal regulations and environmental conditions. This plan is then sent from the server to the terminal and provided to the user.

[0842] The user checks the plan received on their device and reviews its contents. If there are any questions or corrections to the plan, the user sends feedback from their device to the server. The server reevaluates the plan based on the received feedback, generates a revised plan as necessary, and provides it to the user again.

[0843] As a concrete example, if a base station is to be installed to improve communication speeds in Shinjuku Ward, the user inputs "Improve communication speeds in Shinjuku Ward, budget 50 million yen, considering environmental conditions" into the terminal. The terminal sends this information to the server, which then proposes the optimal base station placement based on geographical information and population data. This proposal includes candidate base station installation sites, required equipment, installation and operating costs, implementation schedule, etc.

[0844] An example prompt is, "Generate a base station installation plan to improve communication speeds in Shinjuku Ward. The budget should be within 50 million yen, and environmental conditions and legal regulations should be taken into consideration. Please provide a proposal that includes the installation location, required equipment, cost estimate, and implementation schedule."

[0845] This system enables efficient and appropriate installation and improvement of base stations, even for those with a lack of technical knowledge or experience.

[0846] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0847] Specific flow of system program processing

[0848] Step 1:

[0849] The user uses a terminal to input the desired installation location, purpose, and installation conditions. For example, they might input the latitude and longitude of Shinjuku Ward, the need to improve communication speed, a budget of 50 million yen, and environmental conditions to be taken into consideration. This input data is sent to the next step.

[0850] Input: desired installation location, installation purpose, installation conditions

[0851] Output: Input data (desired installation location, installation purpose, installation conditions)

[0852] Step 2:

[0853] The terminal converts the input data into a digital format. Specifically, it identifies the text information entered by the user as structured data and converts it into, for example, JSON format. This data conversion makes it easier for the server to process in the next step.

[0854] Input: Input data (desired installation location, installation purpose, installation conditions)

[0855] Output: Data converted to digital format

[0856] Step 3:

[0857] The device then transmits the data in digital format to the server, using a secure protocol (e.g., HTTPS) to protect the integrity and privacy of the data.

[0858] Input: Data converted into digital format

[0859] Output: Data sent to the server

[0860] Step 4:

[0861] The server validates the received data, checking for missing data or formatting issues, and generates a completion request if necessary. For example, if regulatory information is missing, a completion request is generated and sent to the device.

[0862] Input: Data sent to the server

[0863] Output: Validation results and completion request (if necessary)

[0864] Step 5:

[0865] The terminal receives the completion request and prompts the user for additional information. The user enters the missing information (e.g., regulatory details) into the terminal and resubmits the data.

[0866] Input: Completion request

[0867] Output: Imputed data

[0868] Step 6:

[0869] The server performs analysis based on the supplemented data. Specifically, it retrieves geographic information system (GIS) data and population density data and compares them with the input information. It also searches its internal database for similar cases and reference data from the past.

[0870] Input: Imputed data

[0871] Output: Analysis results (geographical information, population density data, past similar cases)

[0872] Step 7:

[0873] Based on the analysis results, the server uses a generative AI model to generate an optimal plan, taking into consideration geographical characteristics, communication traffic data, budget, legal regulations, etc. For example, it proposes the optimal placement of base stations by taking into account peak communication traffic times and population distribution in Shinjuku Ward.

[0874] Input: Analysis results (geographical information, population density data, past similar cases)

[0875] Output: The generated optimal plan

[0876] Step 8:

[0877] The server then sends the generated plan to the terminal, which includes the installation location, the type and number of required equipment, estimated installation and operation costs, an implementation schedule, and notes on legal regulations and environmental conditions.

[0878] Input: Generated optimal plan

[0879] Output: Plan sent to the device

[0880] Step 9:

[0881] The user checks the plan received on the device and reviews its contents. If there are any questions or corrections to the plan, the user can send feedback from the device to the server. Specifically, if the user is not satisfied with the cost estimate items, the user can include the reasons in the feedback.

[0882] Input: Plan sent to device

[0883] Output: User feedback

[0884] Step 10:

[0885] The server receives the feedback and re-evaluates the plan, reusing the generative AI model as needed to generate a new plan with adjustments and modifications, such as revising equipment and layout to stay within budget.

[0886] Input: User feedback

[0887] Output: revised plan

[0888] Step 11:

[0889] The server sends the revised plan to the terminal and provides it to the user, allowing the user to review the optimized plan again and make a final decision.

[0890] Input: revised plan

[0891] Output: The revised plan sent to the device

[0892] (Application example 1)

[0893] 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."

[0894] Determining efficient product placement and delivery routes is extremely important in the operation of a logistics center. However, previous methods required manual determination of optimal placement and routes while taking into account multiple factors such as the type and quantity of goods, budget, and delivery date, which required a significant amount of time and effort. Furthermore, the accuracy of optimization was insufficient, making it difficult to improve the efficiency of the entire logistics center. To solve these issues, a system that utilizes existing data to automatically and optimally propose product placement and delivery routes is needed.

[0895] 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.

[0896] In this invention, the server includes a means for a user to input placement locations, purposes, and conditions, a processing means including a generation AI model that analyzes data based on the input information and automatically generates an optimal placement and route plan, a means for providing the generated plan and estimate results to the user, and a means for proposing optimal item placement and delivery routes based on the placement data, thereby enabling efficient and automatic optimization of item placement and delivery routes in a logistics center.

[0897] "User" refers to a person who uses the system to optimize the placement of goods and delivery routes.

[0898] "Location" refers to the specific location within the logistics center where the item is to be placed.

[0899] "Objective" refers to the goal you want to achieve by optimizing the placement of goods and delivery routes, such as improving efficiency or reducing costs.

[0900] "Conditions" refer to constraints or requirements that must be taken into account when optimizing the placement of goods and delivery routes, such as budget, weight, and legal regulations.

[0901] "Data" refers to the information entered by the User and any additional information related thereto.

[0902] "Analysis" refers to the calculations and evaluations that are carried out to derive optimal layout and route plans based on input data.

[0903] "Optimal placement and route plan" refers to a plan that designs the placement of goods and delivery routes in the most efficient way.

[0904] "Generative AI model" refers to an artificial intelligence algorithm that automatically generates optimal placement and route plans based on input information.

[0905] "Processing means" refers to the function that analyzes the information entered by the user and automatically generates the optimal plan using a generative AI model.

[0906] "Estimation results" refers to cost and schedule predictions based on the generated optimal layout and route plan.

[0907] "Feedback" refers to the evaluation or opinion provided by a user regarding a generated plan.

[0908] "Plan reevaluation and revision" refers to the process of reviewing the generated plan based on feedback from users and revising it as necessary.

[0909] "System" refers to the entire set of hardware and software including the above means and models.

[0910] The present invention relates to a system for automatically optimizing the allocation of goods and delivery routes in a logistics center. Specific embodiments of the system are described below.

[0911] First, the user uses a device such as a smartphone or tablet to input the type, quantity, weight, delivery area, budget, etc. The input information is converted into a digital format and sent to the server.

[0912] The server analyzes the data based on the received information and extracts the necessary data. Specifically, based on the input product information, budget, and date and time, it searches for similar past cases and reference data and uses them to analyze the generated AI model.

[0913] Next, the server automatically generates optimal product placement and delivery routes using a generative AI model that comprehensively considers multiple factors (product type, quantity, weight, budget, delivery area, date, etc.) to calculate the most efficient placement plan and delivery route.

[0914] The generated plan includes information on the location, the type and number of required facilities, estimated installation and operation costs, implementation schedule, and notes on legal regulations and environmental conditions. This information is sent from the server to the terminal and provided to the user.

[0915] The user checks the plan received on their device and reviews its contents. If there are any questions or corrections to the plan or estimate, the user can send feedback from their device to the server. The server reevaluates the plan based on the received feedback, regenerates a revised plan as necessary, and provides it to the user.

[0916] The system's program uses the requests library (Python) to send API requests and executes data analysis using a generative AI model on the server. The main hardware used is devices such as smartphones and tablets. The generative AI model also runs on the server, calculating optimal placement and route plans.

[0917] As a specific example, we will explain the case where we propose the optimal product placement and delivery route in a logistics center under the following conditions.

[0918] Logistics Center ID: 12345A

[0919] Goods:

[0920] Material ID: A111, Quantity: 100, Weight: 2.5

[0921] Material ID: B222, Quantity: 200, Weight: 3.0

[0922] Budget: 500,000 yen

[0923] Date: 2023-10-01

[0924] Example prompt sentence:

[0925] Please propose the optimal product layout and delivery route for the distribution center under the following conditions:

[0926] Logistics Center ID: 12345A

[0927] Goods:

[0928] Material ID: A111, Quantity: 100, Weight: 2.5

[0929] Material ID: B222, Quantity: 200, Weight: 3.0

[0930] Budget: 500,000 yen

[0931] Date: 2023-10-01

[0932] This type of system makes it possible to propose efficient and optimal product placement and delivery routes even for those lacking technical knowledge or experience.

[0933] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0934] Step 1:

[0935] Using a smartphone or tablet, the user inputs information such as the type, quantity, weight, budget, and date of items in the distribution center on the screen, and the conditions set by the user are converted into a digital format.

[0936] input:

[0937] Type of item

[0938] quantity

[0939] Weight

[0940] budget

[0941] date

[0942] output:

[0943] Input information converted into digital format

[0944] Step 2:

[0945] The device sends the entered digital format information to the server, which transfers the information using API requests.

[0946] input:

[0947] Server API endpoint

[0948] Input information in digital format

[0949] output:

[0950] Input information sent to the server

[0951] Step 3:

[0952] The server begins data analysis based on the received input information. Specifically, it searches for similar past cases and reference data to collect the information necessary to analyze the generative AI model.

[0953] input:

[0954] Submitted input information

[0955] output:

[0956] Collected historical and reference data

[0957] Step 4:

[0958] The server uses a generative AI model to automatically generate optimal plans for product placement and delivery routes. The AI ​​algorithm calculates the optimal placement and route by taking into account multiple factors based on input information and collected data.

[0959] input:

[0960] Input information

[0961] Data collected

[0962] output:

[0963] Optimal product placement and delivery route planning

[0964] Step 5:

[0965] The server sends the generated deployment plan and estimate results (cost, schedule, etc.) to the terminal, allowing the user to view the optimal plan.

[0966] input:

[0967] Generated Plan

[0968] Estimate results

[0969] output:

[0970] Plans and quote results sent to your device

[0971] Step 6:

[0972] The user reviews the plan and estimate results received on the device, and if there are any questions or corrections regarding the plan or estimate, they provide feedback via the device.

[0973] input:

[0974] Received plan and quote results

[0975] User Feedback

[0976] output:

[0977] Feedback Information

[0978] Step 7:

[0979] The server reevaluates the plan based on user feedback and modifies it as necessary. It then uses the generative AI model again to create a revised plan and sends it to the device.

[0980] input:

[0981] Feedback Information

[0982] Data collected

[0983] output:

[0984] Revised Plan

[0985] In this way, optimal product placement and delivery routes are provided to users, making it possible to improve the operational efficiency of logistics centers.

[0986] 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.

[0987] The present invention relates to a system that proposes optimal plans for the installation and improvement of mobile phone base stations. This system provides plans that are more suitable for users by combining it with an emotion engine that recognizes user emotions.

[0988] First, the user uses a terminal to input the location, purpose, and conditions for installing the base station. Specifically, the user inputs the latitude and longitude of the installation location, the purpose (e.g., improving communication speed, expanding the coverage area), and the conditions (e.g., budget, environmental conditions, legal regulations).

[0989] The input information is converted into a digital format by the device and sent to the server. At this time, the emotion engine analyzes the user's emotional state at the time of input. For example, it analyzes information such as whether the user is tense or relaxed based on the input text, input speed, and input device usage.

[0990] The server analyzes the received information and, if any information is missing, requests it again from the terminal to complete the data.The server then references its internal database to search for and obtain geographical information, population density data, the status of existing communications infrastructure, and other information related to the input installation location.

[0991] Next, the server searches for similar past cases and reference data, and analyzes the necessary data. The analysis results are used as input data for the generative AI model. Based on the input information and past data, the generative AI model automatically generates an optimal base station installation and improvement plan. This model comprehensively takes into account multiple factors, including geographical characteristics, communication traffic data, budget, and legal regulations.

[0992] The generated plan includes the specific location of the installation site, the type and number of required equipment, estimated installation and operation costs, an implementation schedule, and notes on legal regulations and environmental conditions. The emotion engine takes the user's emotional state into account and adjusts the plan presentation accordingly. For example, if the user is nervous, the plan will be explained in more concise and clear language.

[0993] The server sends the plan and estimate results it has created to the device and provides them to the user. The user checks the plan received on the device and reviews its contents. If the user has any questions or requests for corrections regarding the plan or estimate, they can send feedback from their device to the server. At this time, the emotion engine analyzes the user's emotions when providing feedback and reevaluates the plan as necessary.

[0994] After analyzing the feedback, the server re-runs the generative AI model to generate a revised, optimal plan. The server then re-presents this revised plan to the user, while making adjustments to increase user satisfaction through the emotion engine.

[0995] As a concrete example, consider the installation of a base station to improve communication speeds in Shinjuku Ward. A user inputs, "I would like to install a base station in Shinjuku Ward to improve communication speeds, with a budget of 50 million yen or less and taking environmental conditions into consideration." If the user is nervous at this time, the emotion engine can present a plan in language that will reassure them.

[0996] In this way, even users who lack technical knowledge or experience can use the system of the present invention to efficiently and optimally install and improve base stations, and by taking user feelings into consideration, they can provide more user-friendly services.

[0997] The processing flow will be explained below.

[0998] Step 1:

[0999] The user uses a terminal to input information such as the base station installation location, purpose, and conditions, including the desired latitude and longitude, the purpose (such as improving communication speed or expanding the coverage area), budget limits, and legal restrictions.

[1000] Step 2:

[1001] The device converts the user's input into a digital format and then uses an emotion engine to analyze the user's emotional state (e.g., nervousness, relaxation) as they type, including keystroke speed and emotional analysis of the input.

[1002] Step 3:

[1003] The terminal transmits the information converted into a digital format and the analyzed emotional state information to a server.

[1004] Step 4:

[1005] The server receives the information sent from the device, verifies the data format, and, if necessary, requests the device to complete any missing data.

[1006] Step 5:

[1007] The server refers to the database to search and obtain geographical information, population density data, and information on existing communications infrastructure for the desired installation location.

[1008] Step 6:

[1009] The server searches for and analyzes similar past cases and reference data, thereby preparing input data for the generative AI model.

[1010] Step 7:

[1011] The server runs a generative AI model to automatically generate optimal installation and improvement plans, taking into account multiple factors such as geographical characteristics, communication traffic data, budget, and legal regulations.

[1012] Step 8:

[1013] The server generates a report of the plan, which includes the installation location, required equipment, estimated installation and operating costs, implementation schedule, and regulatory and environmental considerations.

[1014] Step 9:

[1015] The emotion engine adjusts the plan presentation based on the user's emotional state, for example, providing concise, positive explanations if the user is nervous, or providing detailed information if the user is relaxed.

[1016] Step 10:

[1017] The server sends the generated report and the adjusted plan to the device.

[1018] Step 11:

[1019] The user checks the received plan on the device, reviews its contents, and sends feedback if there are any questions or corrections they would like to make regarding the plan or estimate.

[1020] Step 12:

[1021] Along with the feedback, the emotion engine analyzes the user's emotions at the time of the feedback, allowing the server to take the most appropriate action.

[1022] Step 13:

[1023] The server re-runs the generative AI model to re-evaluate and revise the plan based on the feedback, generating a revised plan that the emotion engine adjusts again before providing to the user again.

[1024] In this way, this system goes through a series of processing steps to provide efficient and optimal base station installation and improvement plans, and by taking user emotions into consideration, it realizes a more user-friendly service.

[1025] Example 2

[1026] 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."

[1027] Conventional base station installation plan creation systems have difficulty presenting efficient plans because they do not take into account the user's emotional state. Furthermore, they are unable to effectively utilize similar past data, making it difficult to generate optimal plans. Furthermore, even if users provide feedback on the plan, appropriate revisions based on that feedback are not made.

[1028] 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.

[1029] In this invention, the server includes: a means for a user to input the base station installation location, purpose, and conditions; a means for converting the input information into a digital format and transmitting it to the server; an emotion engine including means for performing emotion analysis based on the transmitted information; a means for analyzing the received information and completing any missing information; a means for referencing an internal database to acquire relevant geographic information, population density data, and the status of existing communication infrastructure; a means for analyzing similar past cases and reference data and using them as input data for a generative AI model; a means for automatically generating an optimal base station installation and improvement plan using the generative AI model; and a emotion engine including means for adjusting the presentation content of the generated plan taking into account the user's emotional state; a means for providing the generated plan and estimate results to the user; and a means for receiving user feedback, analyzing the user's emotional state at the time of the feedback, and reevaluating and modifying the plan. This enables the generation and provision of an efficient and optimal base station installation and improvement plan while taking into account the user's emotions.

[1030] A "user" is an end user who inputs the installation location, purpose, conditions, etc. of a base station.

[1031] A "terminal" is an electronic device that allows a user to input information and convert that information into a digital format.

[1032] A "server" is a computer system that receives digitally formatted information sent from a terminal and performs processing such as analysis, search, and generation based on that information.

[1033] The "emotion engine" is a software module that analyzes the user's emotional state at the time of input and adjusts the service content based on the analysis results.

[1034] "Digital format" refers to a data format in which information entered by a user is converted into a format that can be processed electronically.

[1035] An "internal database" is a storage device for storing data such as geographical information, population density data, and the status of existing communication infrastructure.

[1036] The "generative AI model" is an artificial intelligence model that automatically generates optimal base station installation and improvement plans based on similar past cases and reference data.

[1037] The "plan" is an installation and improvement strategy that includes the specific installation location of the base station, the type and number of required equipment, estimated installation and operation costs, implementation schedule, and points to note regarding legal regulations and environmental conditions.

[1038] "Feedback" is response information that allows the user to check the plan and convey any questions or requests for corrections to the server.

[1039] "Analysis" is the process by which the server extracts and understands the necessary information based on the data it receives.

[1040] "Search" is the process by which the server finds the necessary information from its internal database or past data.

[1041] "Reassessment" is the process of receiving feedback, reviewing existing plans based on that feedback, and making adjustments as needed.

[1042] MODE FOR CARRYING OUT THE INVENTION

[1043] The present invention is a system that proposes optimal plans for installing and improving base stations. This system aims to provide plans that are more suitable for each user by combining it with an emotion engine that recognizes the user's emotions. A specific example of the system is described below.

[1044] First, the user uses a terminal to input the base station installation location, purpose, and conditions. Specific input items include the latitude and longitude of the installation location, purpose (e.g., improving communication speed, expanding coverage area), and conditions (e.g., budget, environmental conditions, legal regulations). This input information is converted into a digital format by the terminal and sent to the server.

[1045] The emotion engine kicks in at this point and analyzes the user's emotional state as they type, for example, by looking at the text they type, their typing speed, and how they use their input device to determine whether they are tense or relaxed.

[1046] The server then analyzes the received information and, if any information is missing, requests it again from the terminal to complete the data. At this point, the server references its internal database to search and obtain geographical information, population density data, the status of existing communications infrastructure, and other information related to the input installation location.

[1047] The system then searches for similar past cases and reference data, and uses the analysis results as input data for the generative AI model. This generative AI model references existing base station installation cases in Osaka City and Tokyo, for example, and automatically generates an optimal base station installation and improvement plan based on the input information and past data. The model comprehensively takes into account multiple factors, including geographical characteristics, communication traffic data, budget, and legal regulations.

[1048] The plan includes the specific location of the installation site, the type and quantity of equipment required, estimated installation and operation costs, an implementation schedule, and notes on legal regulations and environmental conditions. The emotion engine takes the user's emotional state into account and adjusts the plan presentation accordingly. For example, if the user is nervous, the plan will be explained in more concise and clear language.

[1049] The server sends the plan and estimate results to the terminal and provides them to the user. The user can then use the terminal to check and review the contents. If the user has any questions or requests for corrections regarding the plan or estimate, they can send feedback from their terminal to the server. At this time, the emotion engine analyzes the user's emotional state at the time of feedback and reevaluates the plan as necessary.

[1050] Finally, the server re-runs the generative AI model based on the analyzed feedback to generate a revised optimal plan, which it then provides to the user again while making adjustments to increase user satisfaction through the emotion engine.

[1051] Specific examples

[1052] As a concrete example, consider the installation of a base station to improve communication speeds in Shinjuku Ward. The user inputs, "I would like to install a base station to improve communication speeds in Shinjuku Ward, with a budget of 50 million yen or less and taking environmental conditions into consideration." At this time, the emotion engine recognizes the user's state of tension and presents a plan in language that gives a sense of security.

[1053] Prompt Sentence Examples

[1054] "We would like to install a base station in Shinjuku Ward to improve communication speeds. The budget is within 50 million yen, and we would like you to take into consideration the environmental conditions. Please provide us with the optimal plan based on data from similar cases in the past."

[1055] In this way, even users who lack technical knowledge or experience can use the system of the present invention to efficiently and optimally install and improve base stations, and by taking user feelings into consideration, they can provide more user-friendly services.

[1056] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1057] Step 1:

[1058] The user uses a terminal to input the location, purpose, and conditions for installing the base station. Specifically, the user enters, "I want to install a base station in Shinjuku Ward to improve communication speeds, with a budget of 50 million yen or less and taking environmental conditions into consideration." This information becomes the initial input data for the program.

[1059] Step 2:

[1060] The terminal converts the information entered by the user into digital format. The converted information includes the installation location (latitude and longitude), purpose (improvement of communication speed), and conditions (budget of 50 million yen or less, environmental conditions). The converted information is sent to the server.

[1061] Step 3:

[1062] The server receives the information sent from the device. This information includes the latitude and longitude of the installation location, purpose, and conditions. The emotion engine then analyzes the user's emotional state. Specifically, it analyzes the emotion of the input text and keyboard input speed to determine whether the user is tense or relaxed.

[1063] Step 4:

[1064] The server analyzes the received information, identifies any missing information, and sends another request to the device. For example, if it determines that "budget breakdown details are missing," it requests the device to "please tell me the budget breakdown." This request is displayed to the user.

[1065] Step 5:

[1066] The terminal presents the request from the server to the user. The user enters the missing information, which the terminal then converts back into digital format and sends to the server. The input data here might be details of a budget breakdown.

[1067] Step 6:

[1068] The server receives the information resent by the user and refers to its internal database to search and obtain relevant geographical information, population density data, and the status of existing communication infrastructure. For example, it collects geographical information and population density data for Shinjuku Ward. This information is used for analysis.

[1069] Step 7:

[1070] The server searches and analyzes similar cases and reference data from the past, such as base station installation cases to improve communication speeds in other cities, and uses this data as input data for the generative AI model.

[1071] Step 8:

[1072] The server runs the generative AI model to automatically generate an optimal base station installation and improvement plan. The generative AI model takes into account many data points (geographical characteristics, communication traffic, budget, legal regulations, etc.) and generates a plan that includes specific installation locations, required equipment, operating costs, implementation schedules, etc.

[1073] Step 9:

[1074] The generated plan is adjusted using an emotion engine according to the user's emotional state. For example, if the user is nervous, the plan explanation is adjusted to be more concise and clear. This adjusted plan becomes the final output data.

[1075] Step 10:

[1076] The server sends the adjusted plan and estimate results to the device, which then provides them to the user, who then confirms and reviews the plan. For example, if the user feels that the budget breakdown of the plan is unclear, that feedback is recorded.

[1077] Step 11:

[1078] The user sends feedback from their device to the server. The server receives this feedback and uses an emotion engine to analyze the user's emotional state at the time of the feedback. For example, if it is determined to be "dissatisfied," it considers countermeasures.

[1079] Step 12:

[1080] The server re-runs the generative AI model based on the feedback and generates a revised, optimal plan. The revised plan includes more detailed breakdowns and additional information to enhance user satisfaction.

[1081] Step 13:

[1082] The server sends the revised plan back to the terminal, which then provides it to the user. The user reviews the plan again and gives a final rating. This cycle is repeated until the user is satisfied.

[1083] In this way, by understanding the specific processing flow of the invention, it becomes clear how the system operates and how it proposes optimal base station installations to users.

[1084] (Application example 2)

[1085] 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."

[1086] The problem to be solved by this invention is to provide users with mobile phone base station installation and improvement plans that allow even users with little technical knowledge or experience to easily understand and implement efficient and optimal plans. Another object of this invention is to increase user satisfaction by taking into account the user's emotional state and making flexible suggestions accordingly.

[1087] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input the installation location, purpose, and conditions of a base station; means including a generation AI model that analyzes data based on the input information and automatically generates an optimal installation and improvement plan; means for analyzing the user's emotional state using an emotion engine and adjusting the generated plan based on the analysis results; and means for providing the generated plan and estimate results to the user. This makes it possible for even users who lack technical knowledge or experience to obtain an optimal base station installation and improvement plan that is easily adjusted, and allows for flexible responses according to the user's emotional state.

[1088] "User" refers to a person who inputs the location, purpose, and conditions of a base station through the system and checks and reviews the generated plan.

[1089] The "location of the base station" is information indicating the specific physical location where the base station is installed for mobile phone communications.

[1090] "Purpose" refers to the goal of installing a base station, and refers to the specific things that you want to achieve, such as improving communication speeds or expanding the coverage area.

[1091] "Conditions" refer to various constraints and requirements for installing a base station, including budget, environmental conditions, and legal regulations.

[1092] An "emotion engine" is a software module for analyzing a user's emotional state, and has the function of evaluating the user's emotions based on the input text and the status of the device being used.

[1093] A "generative AI model" is an artificial intelligence model that automatically generates optimal base station installation and improvement plans based on input information and past data.

[1094] A "Plan" is a specific implementation plan for the installation of a base station, including the specific location of the installation site, the type and number of facilities required, estimated installation and operation costs, and an implementation schedule.

[1095] "Feedback" is information indicating opinions and desired modifications made by the user to the generated plan.

[1096] A "server" is a remote computer system that receives, analyzes, and processes information sent by users.

[1097] "Tension" refers to the emotional state of anxiety or stress that a user experiences when using a system.

[1098] "Rest API" is a standard protocol for data communication between a server and a client, and is a communication method that uses HTTP.

[1099] The present invention relates to a system that allows users to propose optimal plans for the installation and improvement of mobile phone base stations. This system has an emotion-responsive function that makes proposals taking into account the user's emotional state.

[1100] Hardware and software used

[1101] Hardware: smartphone or tablet (for user input and display), server (for data processing and storage)

[1102] software:

[1103] Implementation in Python

[1104] EmotionEngine: A library for analyzing user emotions

[1105] AI model: An artificial intelligence model that generates optimal plans

[1106] REST API: Data communication protocol between server and client

[1107] Overall system processing flow

[1108] 1. User Input

[1109] Users use their smartphones or tablets to input the base station installation location, purpose, and conditions (e.g., budget, environmental conditions, legal regulations).The emotion engine then analyzes the user's input speed and all aspects of the device being used to obtain emotion data.

[1110] 2. Converting information into digital format and transmitting it

[1111] The input information is converted into a digital format and sent to a server, which then begins analyzing the data based on the received information and the user's emotional state.

[1112] 3. Data analysis and plan generation

[1113] The server references its internal database based on the input information to obtain geographical information, population density data, the state of existing communications infrastructure, and other information. It also searches for similar past cases and reference data, and inputs this information into the generative AI model. The generative AI model then comprehensively analyzes this information to generate an optimal base station installation and improvement plan.

[1114] 4. Adjusting the plan with the emotion engine

[1115] The server adjusts the generated plan based on the analysis results of the emotion engine. For example, if the user is nervous, the server may explain the plan in simple and clear terms.

[1116] 5. Providing the plan and receiving feedback

[1117] The generated plan and estimate results are provided to the user via a smartphone or tablet. The user can review the plan and send feedback to the server. This feedback is also analyzed by the emotion engine, and the plan is reevaluated and revised as necessary.

[1118] Specific examples

[1119] Consider installing a base station to improve communication speeds in Shinjuku Ward. A user inputs, "I would like to install a base station in Shinjuku Ward to improve communication speeds, with a budget of 50 million yen or less and taking environmental conditions into consideration." If the user is nervous, the emotion engine can present the plan in language that will reassure them.

[1120] Prompt Sentence Examples

[1121] "Please propose an optimization plan to improve the response speed of electronic payments within Shinjuku Ward. The budget is 5 million yen. If users are unsure, please explain it in simple terms to reassure them."

[1122] By feeding this prompt into a generative AI model, we can obtain a more user-friendly and emotionally sensitive plan.

[1123] This system allows even users with limited technical knowledge or experience to receive optimal base station installation and improvement plans, and also makes it possible to make suggestions that reflect the user's emotions.

[1124] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1125] Step 1:

[1126] The user uses a smartphone or tablet to input the base station installation location, purpose, and conditions. Specifically, the user launches the app and inputs the latitude and longitude of the installation location, purpose (improving communication speed, expanding coverage area, etc.), budget, environmental conditions, etc. The input data includes latitude and longitude information, text data of the purpose, detailed conditions, etc. The input data is temporarily stored on the device.

[1127] Step 2:

[1128] The terminal converts the acquired user input data into a digital format and sends it to the server. Specifically, the input data is loaded into a standard data format such as JSON format and sent to the server using the HTTP protocol. At this time, the input data includes the location, purpose, conditions, etc. entered by the user, and is sent to the server as output data.

[1129] Step 3:

[1130] The server receives the transmitted data and analyzes the user's emotional state using an emotion engine. Specifically, the emotional state (e.g., tension, anxiety, relaxation, etc.) is evaluated based on the user's input speed and text content. Input data includes input speed, input text, and the status of the device being used. As a result of the analysis, the emotion engine outputs an emotional state such as "tension," "anxiety," or "relaxation."

[1131] Step 4:

[1132] The server retrieves the necessary additional data (geographical information, population density data, the status of existing communications infrastructure, etc.) from its internal database. Specifically, it searches for and retrieves geographical information about the installation location specified by the user, population density data for the people living there, and the status of the existing communications infrastructure. The input data includes the latitude and longitude information entered by the user, and the output data includes the related geographical information, population density data, and communications infrastructure information, which are obtained as input data for the generative AI model.

[1133] Step 5:

[1134] The server searches for similar past cases and reference data, and runs the generative AI model to generate the optimal plan. Specifically, data from similar past cases is collected, and based on that, the generative AI model automatically generates the optimal base station installation and improvement plan. Input data includes geographic information, population data, communications infrastructure data, and past case data, and the output data is the optimal base station installation plan.

[1135] Step 6:

[1136] The server adjusts the content of the generated plan based on the analysis results of the emotion engine. Specifically, if the user is nervous, the plan is adjusted to explain things in simple and clear terms. The input data includes the content of the generated plan and the analysis results of the emotion engine, and the adjusted final plan is generated as output data.

[1137] Step 7:

[1138] The server provides the generated plan and estimate results to the user via a smartphone or tablet. Specifically, it sends the adjusted plan and estimate results to the user's device as an HTTP response. The input data includes the adjusted plan contents and estimate results, and the output data provides the plan information that is displayed to the user.

[1139] Step 8:

[1140] The user checks the plan provided and sends feedback if necessary. Specifically, the user inputs their opinions and requests for revisions to the plan through the app and sends them back to the server. The input data includes the user's feedback, and the output data is the feedback information sent to the server.

[1141] Step 9:

[1142] The server receives user feedback and reevaluates and modifies it based on analysis by the emotion engine. Specifically, it reruns the generative AI model to generate a new plan, which is then adjusted through the emotion engine. The input data includes the user's feedback and emotional state, and the output data is a reevaluated optimal plan.

[1143] By repeating this step, users can receive optimal base station installation and improvement plans that take their emotions into consideration.

[1144] 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.

[1145] 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.

[1146] 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.

[1147] [Fourth embodiment]

[1148] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1149] 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.

[1150] 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).

[1151] 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.

[1152] 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.

[1153] 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).

[1154] 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.

[1155] 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.

[1156] 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.

[1157] 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.

[1158] 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.

[1159] 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.

[1160] 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."

[1161] The present invention relates to a system for proposing an optimal plan for installing and improving a mobile phone base station. This system is implemented in the following specific form.

[1162] First, the user uses a terminal to input the location, purpose, and conditions for installing the base station. Specifically, the user inputs the desired installation location (latitude and longitude), installation purpose (e.g., improving communication speed or expanding the coverage area), and installation conditions (e.g., budget limits, environmental conditions, legal regulations, etc.) on the terminal screen.

[1163] The entered data is converted into a digital format by the terminal and sent to the server, where the data format is verified and missing data is completed. For example, if the user forgets to enter information about the budget or a specific legal regulation, the server will send another request to the terminal to complete the information.

[1164] The server analyzes the received information and extracts the necessary data. Specifically, it investigates geographic information, population density data, and the state of the existing communications infrastructure based on the installation location. Next, the server references its internal database to search for similar past cases and reference data. This allows it to perform an analysis based on past successes and failures.

[1165] Once the analysis is complete, the server uses a generative AI model to automatically generate an optimal installation and improvement plan. This generative AI model calculates the most efficient plan by comprehensively considering a wide range of factors, including geographical characteristics, communication traffic data, budget, and legal regulations. For example, when generating a plan to improve communication speeds in Shinjuku Ward, the model proposes the optimal base station placement based on the area's peak communication traffic times and population distribution.

[1166] The generated plan includes detailed information such as the installation location, the type and number of required equipment, estimated installation and operating costs, implementation schedule, and notes on legal regulations and environmental conditions. The plan is then sent from the server to the terminal and provided to the user.

[1167] The user checks the plan received on their device and reviews its contents. If there are any questions or corrections to the plan or estimate, the user can send feedback from their device to the server. The server reevaluates the plan based on the received feedback, regenerates a revised plan as necessary, and provides it to the user.

[1168] As a concrete example, consider a base station installation plan aimed at improving communication speeds in urban areas. If a user inputs that they would like to install the base station in Shinjuku Ward, that their budget is within 50 million yen, and that they would like to consider environmental conditions, the server will use that information to propose the optimal layout and equipment. This proposal includes specific candidate locations for the base station, the necessary equipment, estimated installation and operating costs, and an implementation schedule, allowing the user to refer to it to proceed with a specific installation plan.

[1169] In this way, even if one lacks technical knowledge or experience, by using the system of the present invention, it is possible to efficiently and optimally install and improve base stations.

[1170] The processing flow will be explained below.

[1171] Step 1:

[1172] The user inputs information into the terminal, such as the location, purpose, and conditions of the base station installation. For example, detailed information such as the latitude and longitude of the installation location, purpose (improving communication speed or expanding the coverage area), and conditions (budget, legal regulations) is entered.

[1173] Step 2:

[1174] The terminal converts the user's input into a digital format, which includes cleaning and formatting the entered text and numeric data.

[1175] Step 3:

[1176] The device converts the information into a digital format and sends it to the server, using the necessary protocols to ensure that the data arrives at the server without being lost or tampered with.

[1177] Step 4:

[1178] The server receives the information sent from the device and verifies the data format. If any information is missing, the server sends another request to the device to complete the missing data.

[1179] Step 5:

[1180] The server references the database to search and obtain geographical information, population density data, and the status of existing communications infrastructure for the entered installation location.

[1181] Step 6:

[1182] The server searches for similar cases and reference data from the past, analyzes the necessary data, and prepares input data for the generative AI model based on the results of this analysis.

[1183] Step 7:

[1184] The server runs the AI ​​model to automatically generate optimal installation and improvement plans. The model takes into consideration geographical characteristics, communication traffic data, budget, legal regulations, etc. to calculate the optimal solution.

[1185] Step 8:

[1186] The server generates a detailed report of the generated plan, including specific installation locations, required equipment, estimated installation and operating costs, implementation schedules, and regulatory and environmental considerations.

[1187] Step 9:

[1188] The server generates a report and sends it to the terminal, including visual elements and brief explanations to make it easier for the user to understand.

[1189] Step 10:

[1190] The user checks the received plan on the device and reviews the contents. If there are any questions or corrections regarding the plan or estimate, the user can send feedback from the device to the server.

[1191] Step 11:

[1192] The server receives the feedback, re-evaluates and modifies the plan, and then generates a new optimal plan to serve to the user.

[1193] Example 1

[1194] 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."

[1195] With conventional base station installation planning systems, when generating optimal installation and improvement plans based on a variety of information, it took time to collect and analyze data, making it difficult to quickly provide highly accurate plans. Furthermore, they lacked the functionality to reevaluate and revise plans based on user feedback, making it impossible to provide appropriate plans even in situations where flexible responses were required.

[1196] 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.

[1197] In this invention, the server includes: a means for a user to input the installation location, installation purpose, and installation conditions of a communication base station; a means for converting the input information into a digital format and transmitting it; a means for analyzing the data based on the received information and extracting necessary data; a means for automatically generating an optimal installation and improvement plan using a generative AI model based on the analyzed data; and a means for providing the generated plan and estimate results to the user. This makes it possible to quickly and accurately collect and analyze a variety of information, provide highly accurate plans, and flexibly reevaluate and modify plans based on user feedback.

[1198] A "communications base station" is a wireless transmitting / receiving device installed to carry out mobile phone and wireless communications.

[1199] The "installation location" is information indicating the geographical location where the communication base station is physically installed.

[1200] "Installation purpose" is information indicating the reason or goal for installing a communication base station, such as improving communication speed or expanding the coverage area.

[1201] "Installation conditions" refers to information indicating constraints and conditions that must be considered when installing a communication base station, such as budget, environmental conditions, and legal regulations.

[1202] "Digital format" means information entered by a user that has been converted into a form that can be processed by a computer.

[1203] "Received information" refers to data regarding the installation location, installation purpose, and installation conditions that are input by the user and received by the server.

[1204] "Analyzing data" is the process of extracting and structuring the necessary data based on the received information.

[1205] "Extracting the necessary data" means extracting from the analyzed information the information necessary to generate optimal installation and improvement plans for communication base stations.

[1206] A "generative AI model" is an algorithm that uses artificial intelligence to generate optimal installation and improvement plans based on input data.

[1207] The "optimal installation and improvement plan" is a plan for the most efficient and effective placement and installation of communication base stations, calculated by comprehensively taking into account multiple factors.

[1208] "Plans and Estimates" refers to the installation plans and associated cost estimates created by the generative AI model.

[1209] "User" refers to a person who uses this system to create installation and improvement plans for communication base stations.

[1210] The "internal database" is a storage system that stores similar past cases and reference data.

[1211] "Feedback" refers to opinions and requests for corrections that a user provides to a generated plan.

[1212] "Plan reassessment and revision" is the process of reviewing the generated plan based on the feedback received and making changes as necessary.

[1213] This invention relates to a system that proposes optimal plans for installing and improving base stations in mobile phone communication networks. This system analyzes data based on the installation location, installation purpose, and installation conditions entered by the user, and generates optimal installation and improvement plans.

[1214] First, the user uses a terminal to input the desired installation location (e.g., the latitude and longitude of Shinjuku Ward), the purpose of installation (improving communication speed), and installation conditions (e.g., a budget of 50 million yen, consideration of environmental conditions and legal regulations). This information is converted into a digital format by the terminal and sent to the server. A secure protocol (e.g., HTTPS) is used for communication between the server and the terminal.

[1215] The server first verifies the received data and, if any information is missing, sends a completion request to the terminal, allowing the user to re-enter the missing information. The server then searches the system's internal database based on the information to obtain geographical information, population density data, the status of existing communications infrastructure, and other information. The internal database contains similar past cases and reference data, which are used for analysis.

[1216] The server then uses the analyzed data to apply a generative AI model to automatically generate an optimal installation and improvement plan. The generative AI model considers a wide range of factors, including geographical characteristics, communication traffic data, budget, and legal regulations, to propose an efficient and effective plan. The generated plan includes the installation location, the type and number of required equipment, estimated installation and operating costs, an implementation schedule, and notes on legal regulations and environmental conditions. This plan is then sent from the server to the terminal and provided to the user.

[1217] The user checks the plan received on their device and reviews its contents. If there are any questions or corrections to the plan, the user sends feedback from their device to the server. The server reevaluates the plan based on the received feedback, generates a revised plan as necessary, and provides it to the user again.

[1218] As a concrete example, if a base station is to be installed to improve communication speeds in Shinjuku Ward, the user inputs "Improve communication speeds in Shinjuku Ward, budget 50 million yen, considering environmental conditions" into the terminal. The terminal sends this information to the server, which then proposes the optimal base station placement based on geographical information and population data. This proposal includes candidate base station installation sites, required equipment, installation and operating costs, implementation schedule, etc.

[1219] An example prompt is, "Generate a base station installation plan to improve communication speeds in Shinjuku Ward. The budget should be within 50 million yen, and environmental conditions and legal regulations should be taken into consideration. Please provide a proposal that includes the installation location, required equipment, cost estimate, and implementation schedule."

[1220] This system enables efficient and appropriate installation and improvement of base stations, even for those with a lack of technical knowledge or experience.

[1221] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1222] Specific flow of system program processing

[1223] Step 1:

[1224] The user uses a terminal to input the desired installation location, purpose, and installation conditions. For example, they might input the latitude and longitude of Shinjuku Ward, the need to improve communication speed, a budget of 50 million yen, and environmental conditions to be taken into consideration. This input data is sent to the next step.

[1225] Input: desired installation location, installation purpose, installation conditions

[1226] Output: Input data (desired installation location, installation purpose, installation conditions)

[1227] Step 2:

[1228] The terminal converts the input data into a digital format. Specifically, it identifies the text information entered by the user as structured data and converts it into, for example, JSON format. This data conversion makes it easier for the server to process in the next step.

[1229] Input: Input data (desired installation location, installation purpose, installation conditions)

[1230] Output: Data converted to digital format

[1231] Step 3:

[1232] The device then transmits the data in digital format to the server, using a secure protocol (e.g., HTTPS) to protect the integrity and privacy of the data.

[1233] Input: Data converted into digital format

[1234] Output: Data sent to the server

[1235] Step 4:

[1236] The server validates the received data, checking for missing data or formatting issues, and generates a completion request if necessary. For example, if regulatory information is missing, a completion request is generated and sent to the device.

[1237] Input: Data sent to the server

[1238] Output: Validation results and completion request (if necessary)

[1239] Step 5:

[1240] The terminal receives the completion request and prompts the user for additional information. The user enters the missing information (e.g., regulatory details) into the terminal and resubmits the data.

[1241] Input: Completion request

[1242] Output: Imputed data

[1243] Step 6:

[1244] The server performs analysis based on the supplemented data. Specifically, it retrieves geographic information system (GIS) data and population density data and compares them with the input information. It also searches its internal database for similar cases and reference data from the past.

[1245] Input: Imputed data

[1246] Output: Analysis results (geographical information, population density data, past similar cases)

[1247] Step 7:

[1248] Based on the analysis results, the server uses a generative AI model to generate an optimal plan, taking into consideration geographical characteristics, communication traffic data, budget, legal regulations, etc. For example, it proposes the optimal placement of base stations by taking into account peak communication traffic times and population distribution in Shinjuku Ward.

[1249] Input: Analysis results (geographical information, population density data, past similar cases)

[1250] Output: The generated optimal plan

[1251] Step 8:

[1252] The server then sends the generated plan to the terminal, which includes the installation location, the type and number of required equipment, estimated installation and operation costs, an implementation schedule, and notes on legal regulations and environmental conditions.

[1253] Input: Generated optimal plan

[1254] Output: Plan sent to the device

[1255] Step 9:

[1256] The user checks the plan received on the device and reviews its contents. If there are any questions or corrections to the plan, the user can send feedback from the device to the server. Specifically, if the user is not satisfied with the cost estimate items, the user can include the reasons in the feedback.

[1257] Input: Plan sent to device

[1258] Output: User feedback

[1259] Step 10:

[1260] The server receives the feedback and re-evaluates the plan, reusing the generative AI model as needed to generate a new plan with adjustments and modifications, such as revising equipment and layout to stay within budget.

[1261] Input: User feedback

[1262] Output: revised plan

[1263] Step 11:

[1264] The server sends the revised plan to the terminal and provides it to the user, allowing the user to review the optimized plan again and make a final decision.

[1265] Input: revised plan

[1266] Output: The revised plan sent to the device

[1267] (Application example 1)

[1268] 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."

[1269] Determining efficient product placement and delivery routes is extremely important in the operation of a logistics center. However, previous methods required manual determination of optimal placement and routes while taking into account multiple factors such as the type and quantity of goods, budget, and delivery date, which required a significant amount of time and effort. Furthermore, the accuracy of optimization was insufficient, making it difficult to improve the efficiency of the entire logistics center. To solve these issues, a system that utilizes existing data to automatically and optimally propose product placement and delivery routes is needed.

[1270] 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.

[1271] In this invention, the server includes a means for a user to input placement locations, purposes, and conditions, a processing means including a generation AI model that analyzes data based on the input information and automatically generates an optimal placement and route plan, a means for providing the generated plan and estimate results to the user, and a means for proposing optimal item placement and delivery routes based on the placement data, thereby enabling efficient and automatic optimization of item placement and delivery routes in a logistics center.

[1272] "User" refers to a person who uses the system to optimize the placement of goods and delivery routes.

[1273] "Location" refers to the specific location within the logistics center where the item is to be placed.

[1274] "Objective" refers to the goal you want to achieve by optimizing the placement of goods and delivery routes, such as improving efficiency or reducing costs.

[1275] "Conditions" refer to constraints or requirements that must be taken into account when optimizing the placement of goods and delivery routes, such as budget, weight, and legal regulations.

[1276] "Data" refers to the information entered by the User and any additional information related thereto.

[1277] "Analysis" refers to the calculations and evaluations that are carried out to derive optimal layout and route plans based on input data.

[1278] "Optimal placement and route plan" refers to a plan that designs the placement of goods and delivery routes in the most efficient way.

[1279] "Generative AI model" refers to an artificial intelligence algorithm that automatically generates optimal placement and route plans based on input information.

[1280] "Processing means" refers to the function that analyzes the information entered by the user and automatically generates the optimal plan using a generative AI model.

[1281] "Estimation results" refers to cost and schedule predictions based on the generated optimal layout and route plan.

[1282] "Feedback" refers to the evaluation or opinion provided by a user regarding a generated plan.

[1283] "Plan reevaluation and revision" refers to the process of reviewing the generated plan based on feedback from users and revising it as necessary.

[1284] "System" refers to the entire set of hardware and software including the above means and models.

[1285] The present invention relates to a system for automatically optimizing the allocation of goods and delivery routes in a logistics center. Specific embodiments of the system are described below.

[1286] First, the user uses a device such as a smartphone or tablet to input the type, quantity, weight, delivery area, budget, etc. The input information is converted into a digital format and sent to the server.

[1287] The server analyzes the data based on the received information and extracts the necessary data. Specifically, based on the input product information, budget, and date and time, it searches for similar past cases and reference data and uses them to analyze the generated AI model.

[1288] Next, the server automatically generates optimal product placement and delivery routes using a generative AI model that comprehensively considers multiple factors (product type, quantity, weight, budget, delivery area, date, etc.) to calculate the most efficient placement plan and delivery route.

[1289] The generated plan includes information on the location, the type and number of required facilities, estimated installation and operation costs, implementation schedule, and notes on legal regulations and environmental conditions. This information is sent from the server to the terminal and provided to the user.

[1290] The user checks the plan received on their device and reviews its contents. If there are any questions or corrections to the plan or estimate, the user can send feedback from their device to the server. The server reevaluates the plan based on the received feedback, regenerates a revised plan as necessary, and provides it to the user.

[1291] The system's program uses the requests library (Python) to send API requests and executes data analysis using a generative AI model on the server. The main hardware used is devices such as smartphones and tablets. The generative AI model also runs on the server, calculating optimal placement and route plans.

[1292] As a specific example, we will explain the case where we propose the optimal product placement and delivery route in a logistics center under the following conditions.

[1293] Logistics Center ID: 12345A

[1294] Goods:

[1295] Material ID: A111, Quantity: 100, Weight: 2.5

[1296] Material ID: B222, Quantity: 200, Weight: 3.0

[1297] Budget: 500,000 yen

[1298] Date: 2023-10-01

[1299] Example prompt sentence:

[1300] Please propose the optimal product layout and delivery route for the distribution center under the following conditions:

[1301] Logistics Center ID: 12345A

[1302] Goods:

[1303] Material ID: A111, Quantity: 100, Weight: 2.5

[1304] Material ID: B222, Quantity: 200, Weight: 3.0

[1305] Budget: 500,000 yen

[1306] Date: 2023-10-01

[1307] This type of system makes it possible to propose efficient and optimal product placement and delivery routes even for those lacking technical knowledge or experience.

[1308] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1309] Step 1:

[1310] Using a smartphone or tablet, the user inputs information such as the type, quantity, weight, budget, and date of items in the distribution center on the screen, and the conditions set by the user are converted into a digital format.

[1311] input:

[1312] Type of item

[1313] quantity

[1314] Weight

[1315] budget

[1316] date

[1317] output:

[1318] Input information converted into digital format

[1319] Step 2:

[1320] The device sends the entered digital format information to the server, which transfers the information using API requests.

[1321] input:

[1322] Server API endpoint

[1323] Input information in digital format

[1324] output:

[1325] Input information sent to the server

[1326] Step 3:

[1327] The server begins data analysis based on the received input information. Specifically, it searches for similar past cases and reference data to collect the information necessary to analyze the generative AI model.

[1328] input:

[1329] Submitted input information

[1330] output:

[1331] Collected historical and reference data

[1332] Step 4:

[1333] The server uses a generative AI model to automatically generate optimal plans for product placement and delivery routes. The AI ​​algorithm calculates the optimal placement and route by taking into account multiple factors based on input information and collected data.

[1334] input:

[1335] Input information

[1336] Data collected

[1337] output:

[1338] Optimal product placement and delivery route planning

[1339] Step 5:

[1340] The server sends the generated deployment plan and estimate results (cost, schedule, etc.) to the terminal, allowing the user to view the optimal plan.

[1341] input:

[1342] Generated Plan

[1343] Estimate results

[1344] output:

[1345] Plans and quote results sent to your device

[1346] Step 6:

[1347] The user reviews the plan and estimate results received on the device, and if there are any questions or corrections regarding the plan or estimate, they provide feedback via the device.

[1348] input:

[1349] Received plan and quote results

[1350] User Feedback

[1351] output:

[1352] Feedback Information

[1353] Step 7:

[1354] The server reevaluates the plan based on user feedback and modifies it as necessary. It then uses the generative AI model again to create a revised plan and sends it to the device.

[1355] input:

[1356] Feedback Information

[1357] Data collected

[1358] output:

[1359] Revised Plan

[1360] In this way, optimal product placement and delivery routes are provided to users, making it possible to improve the operational efficiency of logistics centers.

[1361] 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.

[1362] The present invention relates to a system that proposes optimal plans for the installation and improvement of mobile phone base stations. This system provides plans that are more suitable for users by combining it with an emotion engine that recognizes user emotions.

[1363] First, the user uses a terminal to input the location, purpose, and conditions for installing the base station. Specifically, the user inputs the latitude and longitude of the installation location, the purpose (e.g., improving communication speed, expanding the coverage area), and the conditions (e.g., budget, environmental conditions, legal regulations).

[1364] The input information is converted into a digital format by the device and sent to the server. At this time, the emotion engine analyzes the user's emotional state at the time of input. For example, it analyzes information such as whether the user is tense or relaxed based on the input text, input speed, and input device usage.

[1365] The server analyzes the received information and, if any information is missing, requests it again from the terminal to complete the data.The server then references its internal database to search for and obtain geographical information, population density data, the status of existing communications infrastructure, and other information related to the input installation location.

[1366] Next, the server searches for similar past cases and reference data, and analyzes the necessary data. The analysis results are used as input data for the generative AI model. Based on the input information and past data, the generative AI model automatically generates an optimal base station installation and improvement plan. This model comprehensively takes into account multiple factors, including geographical characteristics, communication traffic data, budget, and legal regulations.

[1367] The generated plan includes the specific location of the installation site, the type and number of required equipment, estimated installation and operation costs, an implementation schedule, and notes on legal regulations and environmental conditions. The emotion engine takes the user's emotional state into account and adjusts the plan presentation accordingly. For example, if the user is nervous, the plan will be explained in more concise and clear language.

[1368] The server sends the plan and estimate results it has created to the device and provides them to the user. The user checks the plan received on the device and reviews its contents. If the user has any questions or requests for corrections regarding the plan or estimate, they can send feedback from their device to the server. At this time, the emotion engine analyzes the user's emotions when providing feedback and reevaluates the plan as necessary.

[1369] After analyzing the feedback, the server re-runs the generative AI model to generate a revised, optimal plan. The server then re-presents this revised plan to the user, while making adjustments to increase user satisfaction through the emotion engine.

[1370] As a concrete example, consider the installation of a base station to improve communication speeds in Shinjuku Ward. A user inputs, "I would like to install a base station in Shinjuku Ward to improve communication speeds, with a budget of 50 million yen or less and taking environmental conditions into consideration." If the user is nervous at this time, the emotion engine can present a plan in language that will reassure them.

[1371] In this way, even users who lack technical knowledge or experience can use the system of the present invention to efficiently and optimally install and improve base stations, and by taking user feelings into consideration, they can provide more user-friendly services.

[1372] The processing flow will be explained below.

[1373] Step 1:

[1374] The user uses a terminal to input information such as the base station installation location, purpose, and conditions, including the desired latitude and longitude, the purpose (such as improving communication speed or expanding the coverage area), budget limits, and legal restrictions.

[1375] Step 2:

[1376] The device converts the user's input into a digital format and then uses an emotion engine to analyze the user's emotional state (e.g., nervousness, relaxation) as they type, including keystroke speed and emotional analysis of the input.

[1377] Step 3:

[1378] The terminal transmits the information converted into a digital format and the analyzed emotional state information to a server.

[1379] Step 4:

[1380] The server receives the information sent from the device, verifies the data format, and, if necessary, requests the device to complete any missing data.

[1381] Step 5:

[1382] The server refers to the database to search and obtain geographical information, population density data, and information on existing communications infrastructure for the desired installation location.

[1383] Step 6:

[1384] The server searches for and analyzes similar past cases and reference data, thereby preparing input data for the generative AI model.

[1385] Step 7:

[1386] The server runs a generative AI model to automatically generate optimal installation and improvement plans, taking into account multiple factors such as geographical characteristics, communication traffic data, budget, and legal regulations.

[1387] Step 8:

[1388] The server generates a report of the plan, which includes the installation location, required equipment, estimated installation and operating costs, implementation schedule, and regulatory and environmental considerations.

[1389] Step 9:

[1390] The emotion engine adjusts the plan presentation based on the user's emotional state, for example, providing concise, positive explanations if the user is nervous, or providing detailed information if the user is relaxed.

[1391] Step 10:

[1392] The server sends the generated report and the adjusted plan to the device.

[1393] Step 11:

[1394] The user checks the received plan on the device, reviews its contents, and sends feedback if there are any questions or corrections they would like to make regarding the plan or estimate.

[1395] Step 12:

[1396] Along with the feedback, the emotion engine analyzes the user's emotions at the time of the feedback, allowing the server to take the most appropriate action.

[1397] Step 13:

[1398] The server re-runs the generative AI model to re-evaluate and revise the plan based on the feedback, generating a revised plan that the emotion engine adjusts again before providing to the user again.

[1399] In this way, this system goes through a series of processing steps to provide efficient and optimal base station installation and improvement plans, and by taking user emotions into consideration, it realizes a more user-friendly service.

[1400] Example 2

[1401] 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."

[1402] Conventional base station installation plan creation systems have difficulty presenting efficient plans because they do not take into account the user's emotional state. Furthermore, they are unable to effectively utilize similar past data, making it difficult to generate optimal plans. Furthermore, even if users provide feedback on the plan, appropriate revisions based on that feedback are not made.

[1403] 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.

[1404] In this invention, the server includes: a means for a user to input the base station installation location, purpose, and conditions; a means for converting the input information into a digital format and transmitting it to the server; an emotion engine including means for performing emotion analysis based on the transmitted information; a means for analyzing the received information and completing any missing information; a means for referencing an internal database to acquire relevant geographic information, population density data, and the status of existing communication infrastructure; a means for analyzing similar past cases and reference data and using them as input data for a generative AI model; a means for automatically generating an optimal base station installation and improvement plan using the generative AI model; and a emotion engine including means for adjusting the presentation content of the generated plan taking into account the user's emotional state; a means for providing the generated plan and estimate results to the user; and a means for receiving user feedback, analyzing the user's emotional state at the time of the feedback, and reevaluating and modifying the plan. This enables the generation and provision of an efficient and optimal base station installation and improvement plan while taking into account the user's emotions.

[1405] A "user" is an end user who inputs the installation location, purpose, conditions, etc. of a base station.

[1406] A "terminal" is an electronic device that allows a user to input information and convert that information into a digital format.

[1407] A "server" is a computer system that receives digitally formatted information sent from a terminal and performs processing such as analysis, search, and generation based on that information.

[1408] The "emotion engine" is a software module that analyzes the user's emotional state at the time of input and adjusts the service content based on the analysis results.

[1409] "Digital format" refers to a data format in which information entered by a user is converted into a format that can be processed electronically.

[1410] An "internal database" is a storage device for storing data such as geographical information, population density data, and the status of existing communication infrastructure.

[1411] The "generative AI model" is an artificial intelligence model that automatically generates optimal base station installation and improvement plans based on similar past cases and reference data.

[1412] The "plan" is an installation and improvement strategy that includes the specific installation location of the base station, the type and number of required equipment, estimated installation and operation costs, implementation schedule, and points to note regarding legal regulations and environmental conditions.

[1413] "Feedback" is response information that allows the user to check the plan and convey any questions or requests for corrections to the server.

[1414] "Analysis" is the process by which the server extracts and understands the necessary information based on the data it receives.

[1415] "Search" is the process by which the server finds the necessary information from its internal database or past data.

[1416] "Reassessment" is the process of receiving feedback, reviewing existing plans based on that feedback, and making adjustments as needed.

[1417] MODE FOR CARRYING OUT THE INVENTION

[1418] The present invention is a system that proposes optimal plans for installing and improving base stations. This system aims to provide plans that are more suitable for each user by combining it with an emotion engine that recognizes the user's emotions. A specific example of the system is described below.

[1419] First, the user uses a terminal to input the base station installation location, purpose, and conditions. Specific input items include the latitude and longitude of the installation location, purpose (e.g., improving communication speed, expanding coverage area), and conditions (e.g., budget, environmental conditions, legal regulations). This input information is converted into a digital format by the terminal and sent to the server.

[1420] The emotion engine kicks in at this point and analyzes the user's emotional state as they type, for example, by looking at the text they type, their typing speed, and how they use their input device to determine whether they are tense or relaxed.

[1421] The server then analyzes the received information and, if any information is missing, requests it again from the terminal to complete the data. At this point, the server references its internal database to search and obtain geographical information, population density data, the status of existing communications infrastructure, and other information related to the input installation location.

[1422] The system then searches for similar past cases and reference data, and uses the analysis results as input data for the generative AI model. This generative AI model references existing base station installation cases in Osaka City and Tokyo, for example, and automatically generates an optimal base station installation and improvement plan based on the input information and past data. The model comprehensively takes into account multiple factors, including geographical characteristics, communication traffic data, budget, and legal regulations.

[1423] The plan includes the specific location of the installation site, the type and quantity of equipment required, estimated installation and operation costs, an implementation schedule, and notes on legal regulations and environmental conditions. The emotion engine takes the user's emotional state into account and adjusts the plan presentation accordingly. For example, if the user is nervous, the plan will be explained in more concise and clear language.

[1424] The server sends the plan and estimate results to the terminal and provides them to the user. The user can then use the terminal to check and review the contents. If the user has any questions or requests for corrections regarding the plan or estimate, they can send feedback from their terminal to the server. At this time, the emotion engine analyzes the user's emotional state at the time of feedback and reevaluates the plan as necessary.

[1425] Finally, the server re-runs the generative AI model based on the analyzed feedback to generate a revised optimal plan, which it then provides to the user again while making adjustments to increase user satisfaction through the emotion engine.

[1426] Specific examples

[1427] As a concrete example, consider the installation of a base station to improve communication speeds in Shinjuku Ward. The user inputs, "I would like to install a base station to improve communication speeds in Shinjuku Ward, with a budget of 50 million yen or less and taking environmental conditions into consideration." At this time, the emotion engine recognizes the user's state of tension and presents a plan in language that gives a sense of security.

[1428] Prompt Sentence Examples

[1429] "We would like to install a base station in Shinjuku Ward to improve communication speeds. The budget is within 50 million yen, and we would like you to take into consideration the environmental conditions. Please provide us with the optimal plan based on data from similar cases in the past."

[1430] In this way, even users who lack technical knowledge or experience can use the system of the present invention to efficiently and optimally install and improve base stations, and by taking user feelings into consideration, they can provide more user-friendly services.

[1431] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1432] Step 1:

[1433] The user uses a terminal to input the location, purpose, and conditions for installing the base station. Specifically, the user enters, "I want to install a base station in Shinjuku Ward to improve communication speeds, with a budget of 50 million yen or less and taking environmental conditions into consideration." This information becomes the initial input data for the program.

[1434] Step 2:

[1435] The terminal converts the information entered by the user into digital format. The converted information includes the installation location (latitude and longitude), purpose (improvement of communication speed), and conditions (budget of 50 million yen or less, environmental conditions). The converted information is sent to the server.

[1436] Step 3:

[1437] The server receives the information sent from the device. This information includes the latitude and longitude of the installation location, purpose, and conditions. The emotion engine then analyzes the user's emotional state. Specifically, it analyzes the emotion of the input text and keyboard input speed to determine whether the user is tense or relaxed.

[1438] Step 4:

[1439] The server analyzes the received information, identifies any missing information, and sends another request to the device. For example, if it determines that "budget breakdown details are missing," it requests the device to "please tell me the budget breakdown." This request is displayed to the user.

[1440] Step 5:

[1441] The terminal presents the request from the server to the user. The user enters the missing information, which the terminal then converts back into digital format and sends to the server. The input data here might be details of a budget breakdown.

[1442] Step 6:

[1443] The server receives the information resent by the user and refers to its internal database to search and obtain relevant geographical information, population density data, and the status of existing communication infrastructure. For example, it collects geographical information and population density data for Shinjuku Ward. This information is used for analysis.

[1444] Step 7:

[1445] The server searches and analyzes similar cases and reference data from the past, such as base station installation cases to improve communication speeds in other cities, and uses this data as input data for the generative AI model.

[1446] Step 8:

[1447] The server runs the generative AI model to automatically generate an optimal base station installation and improvement plan. The generative AI model takes into account many data points (geographical characteristics, communication traffic, budget, legal regulations, etc.) and generates a plan that includes specific installation locations, required equipment, operating costs, implementation schedules, etc.

[1448] Step 9:

[1449] The generated plan is adjusted using an emotion engine according to the user's emotional state. For example, if the user is nervous, the plan explanation is adjusted to be more concise and clear. This adjusted plan becomes the final output data.

[1450] Step 10:

[1451] The server sends the adjusted plan and estimate results to the device, which then provides them to the user, who then confirms and reviews the plan. For example, if the user feels that the budget breakdown of the plan is unclear, that feedback is recorded.

[1452] Step 11:

[1453] The user sends feedback from their device to the server. The server receives this feedback and uses an emotion engine to analyze the user's emotional state at the time of the feedback. For example, if it is determined to be "dissatisfied," it considers countermeasures.

[1454] Step 12:

[1455] The server re-runs the generative AI model based on the feedback and generates a revised, optimal plan. The revised plan includes more detailed breakdowns and additional information to enhance user satisfaction.

[1456] Step 13:

[1457] The server sends the revised plan back to the terminal, which then provides it to the user. The user reviews the plan again and gives a final rating. This cycle is repeated until the user is satisfied.

[1458] In this way, by understanding the specific processing flow of the invention, it becomes clear how the system operates and how it proposes optimal base station installations to users.

[1459] (Application example 2)

[1460] 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."

[1461] The problem to be solved by this invention is to provide users with mobile phone base station installation and improvement plans that allow even users with little technical knowledge or experience to easily understand and implement efficient and optimal plans. Another object of this invention is to increase user satisfaction by taking into account the user's emotional state and making flexible suggestions accordingly.

[1462] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input the installation location, purpose, and conditions of a base station; means including a generation AI model that analyzes data based on the input information and automatically generates an optimal installation and improvement plan; means for analyzing the user's emotional state using an emotion engine and adjusting the generated plan based on the analysis results; and means for providing the generated plan and estimate results to the user. This makes it possible for even users who lack technical knowledge or experience to obtain an optimal base station installation and improvement plan that is easily adjusted, and allows for flexible responses according to the user's emotional state.

[1463] "User" refers to a person who inputs the location, purpose, and conditions of a base station through the system and checks and reviews the generated plan.

[1464] The "location of the base station" is information indicating the specific physical location where the base station is installed for mobile phone communications.

[1465] "Purpose" refers to the goal of installing a base station, and refers to the specific things that you want to achieve, such as improving communication speeds or expanding the coverage area.

[1466] "Conditions" refer to various constraints and requirements for installing a base station, including budget, environmental conditions, and legal regulations.

[1467] An "emotion engine" is a software module for analyzing a user's emotional state, and has the function of evaluating the user's emotions based on the input text and the status of the device being used.

[1468] A "generative AI model" is an artificial intelligence model that automatically generates optimal base station installation and improvement plans based on input information and past data.

[1469] A "Plan" is a specific implementation plan for the installation of a base station, including the specific location of the installation site, the type and number of facilities required, estimated installation and operation costs, and an implementation schedule.

[1470] "Feedback" is information indicating opinions and desired modifications made by the user to the generated plan.

[1471] A "server" is a remote computer system that receives, analyzes, and processes information sent by users.

[1472] "Tension" refers to the emotional state of anxiety or stress that a user experiences when using a system.

[1473] "Rest API" is a standard protocol for data communication between a server and a client, and is a communication method that uses HTTP.

[1474] The present invention relates to a system that allows users to propose optimal plans for the installation and improvement of mobile phone base stations. This system has an emotion-responsive function that makes proposals taking into account the user's emotional state.

[1475] Hardware and software used

[1476] Hardware: smartphone or tablet (for user input and display), server (for data processing and storage)

[1477] software:

[1478] Implementation in Python

[1479] EmotionEngine: A library for analyzing user emotions

[1480] AI model: An artificial intelligence model that generates optimal plans

[1481] REST API: Data communication protocol between server and client

[1482] Overall system processing flow

[1483] 1. User Input

[1484] Users use their smartphones or tablets to input the base station installation location, purpose, and conditions (e.g., budget, environmental conditions, legal regulations).The emotion engine then analyzes the user's input speed and all aspects of the device being used to obtain emotion data.

[1485] 2. Converting information into digital format and transmitting it

[1486] The input information is converted into a digital format and sent to a server, which then begins analyzing the data based on the received information and the user's emotional state.

[1487] 3. Data analysis and plan generation

[1488] The server references its internal database based on the input information to obtain geographical information, population density data, the state of existing communications infrastructure, and other information. It also searches for similar past cases and reference data, and inputs this information into the generative AI model. The generative AI model then comprehensively analyzes this information to generate an optimal base station installation and improvement plan.

[1489] 4. Adjusting the plan with the emotion engine

[1490] The server adjusts the generated plan based on the analysis results of the emotion engine. For example, if the user is nervous, the server may explain the plan in simple and clear terms.

[1491] 5. Providing the plan and receiving feedback

[1492] The generated plan and estimate results are provided to the user via a smartphone or tablet. The user can review the plan and send feedback to the server. This feedback is also analyzed by the emotion engine, and the plan is reevaluated and revised as necessary.

[1493] Specific examples

[1494] Consider installing a base station to improve communication speeds in Shinjuku Ward. A user inputs, "I would like to install a base station in Shinjuku Ward to improve communication speeds, with a budget of 50 million yen or less and taking environmental conditions into consideration." If the user is nervous, the emotion engine can present the plan in language that will reassure them.

[1495] Prompt Sentence Examples

[1496] "Please propose an optimization plan to improve the response speed of electronic payments within Shinjuku Ward. The budget is 5 million yen. If users are unsure, please explain it in simple terms to reassure them."

[1497] By feeding this prompt into a generative AI model, we can obtain a more user-friendly and emotionally sensitive plan.

[1498] This system allows even users with limited technical knowledge or experience to receive optimal base station installation and improvement plans, and also makes it possible to make suggestions that reflect the user's emotions.

[1499] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1500] Step 1:

[1501] The user uses a smartphone or tablet to input the base station installation location, purpose, and conditions. Specifically, the user launches the app and inputs the latitude and longitude of the installation location, purpose (improving communication speed, expanding coverage area, etc.), budget, environmental conditions, etc. The input data includes latitude and longitude information, text data of the purpose, detailed conditions, etc. The input data is temporarily stored on the device.

[1502] Step 2:

[1503] The terminal converts the acquired user input data into a digital format and sends it to the server. Specifically, the input data is loaded into a standard data format such as JSON format and sent to the server using the HTTP protocol. At this time, the input data includes the location, purpose, conditions, etc. entered by the user, and is sent to the server as output data.

[1504] Step 3:

[1505] The server receives the transmitted data and analyzes the user's emotional state using an emotion engine. Specifically, the emotional state (e.g., tension, anxiety, relaxation, etc.) is evaluated based on the user's input speed and text content. Input data includes input speed, input text, and the status of the device being used. As a result of the analysis, the emotion engine outputs an emotional state such as "tension," "anxiety," or "relaxation."

[1506] Step 4:

[1507] The server retrieves the necessary additional data (geographical information, population density data, the status of existing communications infrastructure, etc.) from its internal database. Specifically, it searches for and retrieves geographical information about the installation location specified by the user, population density data for the people living there, and the status of the existing communications infrastructure. The input data includes the latitude and longitude information entered by the user, and the output data includes the related geographical information, population density data, and communications infrastructure information, which are obtained as input data for the generative AI model.

[1508] Step 5:

[1509] The server searches for similar past cases and reference data, and runs the generative AI model to generate the optimal plan. Specifically, data from similar past cases is collected, and based on that, the generative AI model automatically generates the optimal base station installation and improvement plan. Input data includes geographic information, population data, communications infrastructure data, and past case data, and the output data is the optimal base station installation plan.

[1510] Step 6:

[1511] The server adjusts the content of the generated plan based on the analysis results of the emotion engine. Specifically, if the user is nervous, the plan is adjusted to explain things in simple and clear terms. The input data includes the content of the generated plan and the analysis results of the emotion engine, and the adjusted final plan is generated as output data.

[1512] Step 7:

[1513] The server provides the generated plan and estimate results to the user via a smartphone or tablet. Specifically, it sends the adjusted plan and estimate results to the user's device as an HTTP response. The input data includes the adjusted plan contents and estimate results, and the output data provides the plan information that is displayed to the user.

[1514] Step 8:

[1515] The user checks the plan provided and sends feedback if necessary. Specifically, the user inputs their opinions and requests for revisions to the plan through the app and sends them back to the server. The input data includes the user's feedback, and the output data is the feedback information sent to the server.

[1516] Step 9:

[1517] The server receives user feedback and reevaluates and modifies it based on analysis by the emotion engine. Specifically, it reruns the generative AI model to generate a new plan, which is then adjusted through the emotion engine. The input data includes the user's feedback and emotional state, and the output data is a reevaluated optimal plan.

[1518] By repeating this step, users can receive optimal base station installation and improvement plans that take their emotions into consideration.

[1519] 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.

[1520] 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.

[1521] 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.

[1522] 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.

[1523] 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.

[1524] 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.

[1525] 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).

[1526] 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.

[1527] 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."

[1528] 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.

[1529] 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).

[1530] 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.

[1531] 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.

[1532] 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.

[1533] 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.

[1534] 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.

[1535] 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.

[1536] 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.

[1537] 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.

[1538] 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.

[1539] 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.

[1540] The following is further disclosed regarding the above embodiment.

[1541] (Claim 1)

[1542] A means for users to input the location, purpose, and conditions of the base station;

[1543] A processing means including a generative AI model that analyzes data based on input information and automatically generates optimal installation and improvement plans;

[1544] A means for providing the generated plan and estimate results to users;

[1545] A system including:

[1546] (Claim 2)

[1547] The system of claim 1, further comprising means for searching for similar past cases and reference data and using the searched data to analyze the generated AI model.

[1548] (Claim 3)

[1549] 10. The system of claim 1, further comprising means for receiving feedback on the generated plan and for reevaluating and modifying the plan.

[1550] "Example 1"

[1551] (Claim 1)

[1552] A means for a user to input the installation location, installation purpose, and installation conditions of a communication base station;

[1553] means for converting and transmitting the input information into a digital format;

[1554] A means for analyzing data based on the received information and extracting necessary data;

[1555] A means to automatically generate optimal installation and improvement plans using a generative AI model based on the analyzed data,

[1556] A means for providing the generated plan and estimate results to users;

[1557] A system including:

[1558] (Claim 2)

[1559] The system of claim 1 further comprising means for searching an internal database for similar past cases and reference data and using the data to analyze the generated AI model.

[1560] (Claim 3)

[1561] 10. The system of claim 1, further comprising means for receiving feedback from a user on the generated plan and for reevaluating and modifying the plan.

[1562] "Application Example 1"

[1563] (Claim 1)

[1564] A means for users to input the location, purpose, and conditions of placement;

[1565] A processing means including a generating AI model that analyzes data based on input information and automatically generates an optimal placement and route plan;

[1566] A means for providing the generated plan and estimate results to users;

[1567] A means to propose optimal product placement and delivery routes based on placement data, and

[1568] A system including:

[1569] (Claim 2)

[1570] The system of claim 1, further comprising means for searching for similar past cases and reference data and using the searched data to analyze the generated AI model.

[1571] (Claim 3)

[1572] 10. The system of claim 1, further comprising means for receiving feedback on the generated plan and for reevaluating and modifying the plan.

[1573] "Example 2: Combining Emotion Engines"

[1574] (Claim 1)

[1575] A means for users to input the location, purpose, and conditions of the base station;

[1576] means for converting the input information into a digital format and transmitting the digital format to a server;

[1577] an emotion engine including means for performing emotion analysis based on the transmitted information;

[1578] A means of analyzing the received information and completing any missing information;

[1579] means for referencing an internal database to obtain relevant geographic information, population density data, and the status of existing communications infrastructure;

[1580] A method for analyzing similar cases and reference data from the past and using them as input data for the generative AI model;

[1581] A means to automatically generate optimal base station installation and improvement plans using generative AI models,

[1582] an emotion engine including means for adjusting the presentation of the generated plan in consideration of the emotional state;

[1583] A means for providing the generated plan and estimate results to users;

[1584] A system that includes a means for receiving user feedback, analyzing the user's emotional state at the time of the feedback, and reevaluating and revising the plan.

[1585] (Claim 2)

[1586] The system of claim 1, further comprising means for searching for similar past cases and reference data and using the searched data to analyze the generated AI model.

[1587] (Claim 3)

[1588] 10. The system of claim 1, further comprising means for receiving feedback on the generated plan and for reevaluating and modifying the plan.

[1589] "Application example 2 when combining emotion engines"

[1590] (Claim 1)

[1591] A means for users to input the location, purpose, and conditions of the base station;

[1592] A processing means including a generative AI model that analyzes data based on input information and automatically generates optimal installation and improvement plans;

[1593] means for analyzing the emotional state of the user by an emotion engine and adjusting the generated plan based on the analysis result;

[1594] A means for providing the generated plan and estimate results to users;

[1595] A system including:

[1596] (Claim 2)

[1597] The system of claim 1, further comprising means for searching for similar past cases and reference data and using the searched data to analyze the generated AI model.

[1598] (Claim 3)

[1599] 10. The system of claim 1, further comprising means for receiving feedback on the generated plan, re-evaluating and modifying the plan, and re-presenting the adjusted plan through the emotion engine. [Explanation of symbols]

[1600] 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 users to input the location, purpose, and conditions of the base station; A processing means including a generative AI model that analyzes data based on input information and automatically generates optimal installation and improvement plans; A means for providing the generated plan and estimate results to users; A system including:

2. The system according to claim 1, further comprising means for searching for similar past cases and reference data and using the searched data to analyze the generated AI model.

3. 10. The system of claim 1, further comprising means for receiving feedback on the generated plan and for reevaluating and modifying the plan.

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