System

Abstract

An object of a system according to an embodiment is to propose an optimal plan related to installation and improvement of a base station without expert knowledge.SOLUTION: A system includes an input unit, an analysis unit, a proposal unit, and an estimation unit. The input unit inputs an installation location, a purpose, and a condition of a base station from a user. The analysis unit analyzes the installation location, the purpose, and the condition input by the input unit. The proposal unit proposes an optimum plan on the basis of a result analyzed by the analysis unit. The estimation unit creates an estimate based on the plan proposed by the proposal unit.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] With conventional technology, proposing optimal plans for installing and improving base stations required specialized knowledge and experience, and if this knowledge and experience was lacking, it was difficult to make appropriate proposals.

[0005] The system according to the embodiment aims to propose an optimal plan for installing or improving a base station without requiring specialized knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a proposal unit, and an estimation unit. The input unit inputs the installation location, purpose, and conditions of the base station from a user. The analysis unit analyzes the installation location, purpose, and conditions input by the input unit. The proposal unit proposes an optimal plan based on the results of the analysis by the analysis unit. The estimation unit creates an estimate based on the plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal plan for installing or improving a base station without requiring specialized knowledge. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The base station installation plan proposal system according to an embodiment of the present invention is a system that utilizes generation AI to propose optimal plans for the installation and improvement of base stations. As a result, the base station installation plan proposal system can provide optimal plans and estimates simply by the user inputting the base station installation location, purpose, and conditions.

[0029] A base station installation plan proposal system according to an embodiment includes an input unit, an analysis unit, a proposal unit, and an estimation unit. The input unit receives input of the installation location, purpose, and conditions of the base station from a user. For example, the user may input, "I want to install a base station on the roof of a high-rise building in an urban area." The user may also input, "I want to expand the communication coverage area in a mountainous area." The user may also input, "My budget should be within XX yen." The analysis unit analyzes the installation location, purpose, and conditions input by the input unit. For example, the analysis unit analyzes the radio wave conditions at the input installation location. The analysis unit can also analyze the optimal installation method based on the input purpose. The analysis unit can also analyze costs based on the input conditions. The proposal unit proposes an optimal plan based on the results of the analysis by the analysis unit. For example, the proposal unit proposes an optimal plan for installing a base station on the roof of a high-rise building in an urban area. The proposal unit can also propose an optimal plan for expanding the communication coverage area in a mountainous area. The proposal unit can also propose an optimal plan within the budget. The estimation unit creates an estimate based on the plan proposed by the proposal unit. For example, the estimation unit estimates the cost of equipment required to install a base station. The estimation unit can also estimate the cost of installation work. The estimation unit can also estimate operating costs. As a result, the base station installation plan proposing system according to the embodiment can provide an optimal base station installation plan and estimate based on user input.

[0030] The input unit allows the generation AI to refer to past installation cases and success stories for the conditions entered by the user and propose optimal conditions. For example, based on the conditions entered by the user, the input unit allows the generation AI to search a database of past installation cases and present successful cases under similar conditions. For example, when installing a base station in a high-rise building in an urban area, the generation AI refers to past successful cases under similar conditions and proposes the optimal installation location and equipment selection. The input unit also allows the generation AI to analyze past successful cases and propose the installation method most suitable for the conditions entered by the user. For example, when expanding communication coverage in mountainous areas, the generation AI proposes the optimal base station placement and radio wave adjustment method based on past successful cases under similar conditions. The input unit also allows the generation AI to refer to past installation cases for the conditions entered by the user and propose conditions with a high probability of success. For example, to optimally install a base station within a budget, the generation AI proposes the optimal equipment selection and installation method based on past successful cases. This allows the generation AI to propose optimal conditions based on past successful cases.

[0031] The input unit allows the generation AI to perform simulations in real time based on the input conditions and visually present the optimal installation location and conditions. For example, the input unit allows the generation AI to perform simulations in real time based on the conditions input by the user and visually present the optimal installation location and conditions in a 3D model. For example, when installing a base station in a high-rise building in an urban area, the generation AI simulates the building structure and surrounding radio wave conditions to visually present the optimal installation location. The input unit also allows the generation AI to perform simulations in real time based on the input conditions and visually present the optimal base station placement and radio wave adjustment methods. For example, when expanding the communication coverage area in a mountainous area, the generation AI simulates the topography and existing communication infrastructure to visually present the optimal placement. The input unit also allows the generation AI to perform simulations in real time based on the conditions input by the user and visually present the optimal installation location and conditions. For example, to optimally install a base station within a budget, the generation AI visually presents the optimal equipment selection and installation method based on the simulation results. This allows real-time simulations to visually present the optimal installation location and conditions.

[0032] The suggestion unit allows the generation AI to generate different scenarios based on the input conditions and present multiple options to the user. For example, the suggestion unit generates multiple scenarios based on the conditions input by the user and presents the optimal installation location and conditions for each scenario. For example, when installing a base station in a high-rise building in an urban area, the suggestion unit proposes different installation locations and equipment selections. The suggestion unit also generates different scenarios based on the input conditions and presents multiple options to the user. For example, when expanding the communication coverage area in a mountainous area, the suggestion unit proposes different base station placements and radio wave adjustment methods. The suggestion unit also generates different scenarios based on the conditions input by the user and presents the optimal installation location and conditions for each scenario. For example, the suggestion unit proposes different equipment selections and installation methods to optimally install a base station within a budget. This allows different scenarios to be generated and presents multiple options to the user.

[0033] The input unit can accommodate voice input or gesture input for conditions entered by the user, enabling more intuitive operation. The input unit, for example, allows the user to input the base station installation location, purpose, and conditions to the generation AI using voice input. For example, the user might input "I want to install a base station on the roof of a high-rise building in an urban area" using voice input. The input unit also uses gesture input to allow the user to intuitively input the base station installation location and conditions. For example, input is made by pointing at the installation location on a map. The input unit also combines voice input and gesture input to allow the user to more intuitively input the base station installation location and conditions. For example, the user might input conditions using voice and specify the installation location using gestures. This allows for support of voice input and gesture input, enabling intuitive operation.

[0034] The proposal unit can perform predictive analysis based on past data and trends on the plan proposed by the generation AI to evaluate future optimality. The proposal unit, for example, performs predictive analysis based on past data on the plan proposed by the generation AI to evaluate future optimality. For example, it analyzes past communication demand data to predict future demand. The proposal unit also performs predictive analysis based on trend data on the proposed plan to evaluate future optimality. For example, it evaluates taking into account evolution of communication technology and market trends. The proposal unit also builds a system that performs predictive analysis based on past data and trends on the plan proposed by the generation AI to evaluate future optimality. For example, it evaluates based on past success stories and failure stories. This makes it possible to evaluate future optimality based on past data and trends.

[0035] The proposal unit allows the generation AI to automatically perform risk assessments on proposed plans and propose risk mitigation measures. The proposal unit, for example, automatically performs risk assessments on plans proposed by the generation AI and proposes risk mitigation measures. For example, it evaluates technical and operational risks associated with base station installation and proposes mitigation measures. The proposal unit also builds a system where the generation AI automatically performs risk assessments on proposed plans and proposes risk mitigation measures. For example, it evaluates the risk of natural disasters such as earthquakes and typhoons and proposes countermeasures. The proposal unit also automatically performs risk assessments on plans proposed by the generation AI and proposes risk mitigation measures. For example, it evaluates the risk of going over budget and proposes cost-cutting measures. This makes it possible to automatically perform risk assessments and propose risk mitigation measures.

[0036] The proposal unit can reevaluate the proposed plan under different conditions or scenarios to generate multiple alternative plans. For example, the proposal unit reevaluates the plan proposed by the generation AI under different conditions to generate multiple alternative plans. For example, the budget or installation location conditions are changed and the plan is reevaluated. The proposal unit also builds a system that reevaluates the proposed plan under different scenarios to generate multiple alternative plans. For example, the plan is reevaluated taking into account fluctuations in communication demand and technological advances. The proposal unit also reevaluates the plan proposed by the generation AI under different conditions or scenarios to generate multiple alternative plans. For example, the proposal unit proposes a plan using different communication technologies or equipment. This makes it possible to reevaluate under different conditions or scenarios and generate multiple alternative plans.

[0037] The proposal unit can improve the plan proposed by the generation AI based on feedback and evaluations from other users, thereby providing a more optimal plan. The proposal unit, for example, collects feedback from other users on the plan proposed by the generation AI and improves the plan based on the results. For example, it adjusts the installation location and equipment selection by reflecting user opinions. The proposal unit also builds a system that improves the proposed plan based on evaluations from other users. For example, it adjusts the priority of the plan based on user evaluation scores. The proposal unit also improves the plan proposed by the generation AI based on feedback and evaluations from other users, thereby providing a more optimal plan. For example, it adjusts the installation method and operating costs by reflecting user opinions. In this way, the plan can be improved based on feedback and evaluations from other users, thereby providing a more optimal plan.

[0038] When the generation AI creates an estimate, the estimation unit can perform predictive analysis based on past cost data and market trends to take future cost fluctuations into account. For example, when the generation AI creates an estimate, the estimation unit performs predictive analysis based on past cost data to take future cost fluctuations into account. For example, it analyzes data on past equipment costs and installation work costs to predict future costs. The estimation unit also performs predictive analysis based on market trends for each item included in the estimate to take future cost fluctuations into account. For example, it predicts price fluctuations of communications equipment and fluctuations in labor costs. The estimation unit also builds a system in which the generation AI performs predictive analysis based on past cost data and market trends to take future cost fluctuations into account when creating an estimate. For example, it predicts future costs based on past data and reflects this in the estimate. This makes it possible to take future cost fluctuations into account based on past cost data and market trends.

[0039] The estimation unit allows the generation AI to provide detailed explanations and rationales for each item included in the estimate, making it easier for the user to understand. For example, the estimation unit allows the generation AI to provide detailed explanations and rationales for each item included in the estimate. For example, the estimation unit provides a detailed breakdown of equipment costs and installation work costs, making it easier for the user to understand. The estimation unit also builds a system in which the generation AI provides detailed explanations and rationales for each item included in the estimate. For example, the estimation unit retrieves the rationale for each item's cost from a database and presents it to the user. The estimation unit also allows the generation AI to provide detailed explanations and rationales for each item included in the estimate, making it easier for the user to understand. For example, the estimation unit provides a detailed explanation of the installation work procedures and the reasons for selecting the necessary equipment. In this way, the estimation unit provides detailed explanations and rationales for each item included in the estimate, making it easier for the user to understand.

[0040] The estimation unit can adapt the estimate to different currencies or regional prices and provide estimates from an international perspective. For example, the generation AI in the estimation unit adapts the estimate to different currencies and provides estimates from an international perspective. For example, estimates are presented in multiple currencies such as dollars, euros, and yen. The estimation unit also builds a system that adapts the estimate to different regional prices and provides estimates from an international perspective. For example, estimates are presented taking into account local prices and labor costs. The estimation unit also adapts the estimate to different currencies or regional prices and provides estimates from an international perspective. For example, estimates are presented taking into account the cost of communication infrastructure in each region. This makes it possible to adapt to different currencies or regional prices and provide estimates from an international perspective.

[0041] The estimation unit can simultaneously generate estimates under different scenarios and conditions when the generation AI creates an estimate, and present multiple options to the user. For example, when the generation AI creates an estimate, the estimation unit can simultaneously generate estimates under different scenarios and conditions, and present multiple options to the user. For example, it can present estimates under different budgets and installation location conditions. The estimation unit can also build a system that simultaneously generates estimates under different scenarios and conditions, and presents multiple options to the user. For example, it can present estimates using different communication technologies and equipment. The estimation unit can also simultaneously generate estimates under different scenarios and conditions when the generation AI creates an estimate, and present multiple options to the user. For example, it can present estimates that take into account different installation methods and operating costs. This makes it possible to simultaneously generate estimates under different scenarios and conditions, and present multiple options to the user.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The input section allows the generation AI to refer to past installation examples and success stories for the conditions entered by the user and propose optimal conditions. For example, based on the conditions entered by the user, the generation AI searches a database for past installation examples and presents examples that were successful under similar conditions. When installing a base station in a high-rise building in an urban area, the AI ​​can refer to past successful examples under similar conditions and propose the optimal installation location and equipment selection. In addition, when expanding the communication coverage area in mountainous areas, the AI ​​can propose the optimal base station placement and radio wave adjustment method based on past successful examples under similar conditions. Furthermore, in order to optimally install a base station within a budget, the AI ​​can also propose the optimal equipment selection and installation method based on past successful examples. This allows the AI ​​to propose optimal conditions based on past successful examples.

[0044] The input section allows the generation AI to perform real-time simulations based on the conditions entered by the user, and visually present the optimal installation location and conditions. For example, based on the conditions entered by the user, the generation AI performs real-time simulations and visually presents the optimal installation location and conditions in a 3D model. When installing a base station in a high-rise building in an urban area, the system simulates the building structure and surrounding radio wave conditions to visually show the optimal installation location. When expanding the communication coverage area in mountainous areas, the system can simulate the terrain and existing communication infrastructure to visually show the optimal placement. Furthermore, to optimally install base stations within budget, the system can visually show the optimal equipment selection and installation method based on the simulation results. This allows real-time simulations to be performed and the optimal installation location and conditions to be visually shown.

[0045] The suggestion unit allows the generation AI to generate different scenarios based on the input conditions and present multiple options to the user. For example, the generation AI generates multiple scenarios based on the conditions input by the user and presents the optimal installation location and conditions for each scenario. When installing a base station in a high-rise building in an urban area, it can propose different installation locations and equipment selections. Also, when expanding the communication coverage area in mountainous areas, it can propose different base station placements and radio wave adjustment methods. Furthermore, it can propose different equipment selections and installation methods to optimally install base stations within a budget. This allows different scenarios to be generated and multiple options to be presented to the user.

[0046] The input unit can accommodate voice and gesture input for user input conditions, enabling more intuitive operation. For example, a user can use voice input to input the base station installation location, purpose, and conditions to the generation AI. For example, a user can input by voice, "I want to install a base station on the roof of a high-rise building in an urban area." Gesture input can also be used to allow users to intuitively input the base station installation location and conditions. For example, input can be made by pointing at the installation location on a map. Furthermore, voice input and gesture input can be combined to allow users to more intuitively input the base station installation location and conditions. For example, the user can input conditions by voice and specify the installation location by gesture. This supports voice input and gesture input, enabling intuitive operation.

[0047] The proposal unit can perform predictive analysis based on past data and trends on the plan proposed by the generation AI to evaluate its future optimality. For example, it can perform predictive analysis based on past data on the plan proposed by the generation AI to evaluate its future optimality. It can analyze past communication demand data to predict future demand. It can also perform predictive analysis based on trend data on the proposed plan to evaluate its future optimality. For example, it can evaluate plans taking into account evolution in communication technology and market trends. Furthermore, it can build a system that performs predictive analysis based on past data and trends on the plan proposed by the generation AI to evaluate its future optimality. For example, it can evaluate plans based on past successes and failures. This makes it possible to evaluate future optimality based on past data and trends.

[0048] The proposal unit allows the generation AI to automatically perform risk assessments on proposed plans and propose risk mitigation measures. For example, the generation AI can automatically perform risk assessments on plans proposed by the generation AI and propose risk mitigation measures. It can evaluate the technical and operational risks associated with base station installation and propose mitigation measures. It can also build a system in which the generation AI automatically performs risk assessments on proposed plans and proposes risk mitigation measures. For example, it can evaluate the risk of natural disasters such as earthquakes and typhoons and propose countermeasures. It can also automatically perform risk assessments on plans proposed by the generation AI and propose risk mitigation measures. For example, it can evaluate the risk of going over budget and propose cost-cutting measures. This makes it possible to automatically perform risk assessments and propose risk mitigation measures.

[0049] When the generation AI creates an estimate, the estimation unit can perform predictive analysis based on past cost data and market trends to take future cost fluctuations into account. For example, when the generation AI creates an estimate, it can perform predictive analysis based on past cost data to take future cost fluctuations into account. It can analyze data on past equipment costs and installation work costs to predict future costs. The generation AI can also perform predictive analysis based on market trends for each item included in the estimate to take future cost fluctuations into account. For example, it can predict price fluctuations of communications equipment and fluctuations in labor costs. Furthermore, when the generation AI creates an estimate, it can perform predictive analysis based on past cost data and market trends to build a system that takes future cost fluctuations into account. For example, it can predict future costs based on past data and reflect them in the estimate. This makes it possible to take future cost fluctuations into account based on past cost data and market trends.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The input unit receives input from the user about the location, purpose, and conditions for installing the base station. For example, the user might input, "I want to install a base station on the roof of a high-rise building in an urban area." The user can also input, "I want to expand the communication coverage area in mountainous areas." The user can also input, "My budget should be within XX yen." Step 2: The analysis unit analyzes the installation location, purpose, and conditions input by the input unit. For example, the analysis unit analyzes the radio wave conditions at the input installation location. The analysis unit can also analyze the optimal installation method based on the input purpose. The analysis unit can also analyze costs based on the input conditions. Step 3: The proposal unit proposes an optimal plan based on the results of the analysis by the analysis unit. For example, the proposal unit proposes an optimal plan for installing base stations on the rooftops of high-rise buildings in urban areas. The proposal unit can also propose an optimal plan for expanding communication coverage areas in mountainous areas. The proposal unit can also propose an optimal plan within a budget. Step 4: The estimation unit creates an estimate based on the plan proposed by the proposal unit. For example, the estimation unit estimates the cost of equipment required to install the base station. The estimation unit may also estimate the cost of installation work. The estimation unit may also estimate operating costs.

[0052] (Example 2) The base station installation plan proposal system according to an embodiment of the present invention is a system that utilizes generation AI to propose optimal plans for the installation and improvement of base stations. As a result, the base station installation plan proposal system can provide optimal plans and estimates simply by the user inputting the base station installation location, purpose, and conditions.

[0053] A base station installation plan proposal system according to an embodiment includes an input unit, an analysis unit, a proposal unit, and an estimation unit. The input unit receives input of the installation location, purpose, and conditions of the base station from a user. For example, the user may input, "I want to install a base station on the roof of a high-rise building in an urban area." The user may also input, "I want to expand the communication coverage area in a mountainous area." The user may also input, "My budget should be within XX yen." The analysis unit analyzes the installation location, purpose, and conditions input by the input unit. For example, the analysis unit analyzes the radio wave conditions at the input installation location. The analysis unit can also analyze the optimal installation method based on the input purpose. The analysis unit can also analyze costs based on the input conditions. The proposal unit proposes an optimal plan based on the results of the analysis by the analysis unit. For example, the proposal unit proposes an optimal plan for installing a base station on the roof of a high-rise building in an urban area. The proposal unit can also propose an optimal plan for expanding the communication coverage area in a mountainous area. The proposal unit can also propose an optimal plan within the budget. The estimation unit creates an estimate based on the plan proposed by the proposal unit. For example, the estimation unit estimates the cost of equipment required to install a base station. The estimation unit can also estimate the cost of installation work. The estimation unit can also estimate operating costs. As a result, the base station installation plan proposing system according to the embodiment can provide an optimal base station installation plan and estimate based on user input.

[0054] The input unit allows the generation AI to refer to past installation cases and success stories for the conditions entered by the user and propose optimal conditions. For example, based on the conditions entered by the user, the input unit allows the generation AI to search a database of past installation cases and present successful cases under similar conditions. For example, when installing a base station in a high-rise building in an urban area, the generation AI refers to past successful cases under similar conditions and proposes the optimal installation location and equipment selection. The input unit also allows the generation AI to analyze past successful cases and propose the installation method most suitable for the conditions entered by the user. For example, when expanding communication coverage in mountainous areas, the generation AI proposes the optimal base station placement and radio wave adjustment method based on past successful cases under similar conditions. The input unit also allows the generation AI to refer to past installation cases for the conditions entered by the user and propose conditions with a high probability of success. For example, to optimally install a base station within a budget, the generation AI proposes the optimal equipment selection and installation method based on past successful cases. This allows the generation AI to propose optimal conditions based on past successful cases.

[0055] The input unit allows the generation AI to perform simulations in real time based on the input conditions and visually present the optimal installation location and conditions. For example, the input unit allows the generation AI to perform simulations in real time based on the conditions input by the user and visually present the optimal installation location and conditions in a 3D model. For example, when installing a base station in a high-rise building in an urban area, the generation AI simulates the building structure and surrounding radio wave conditions to visually present the optimal installation location. The input unit also allows the generation AI to perform simulations in real time based on the input conditions and visually present the optimal base station placement and radio wave adjustment methods. For example, when expanding the communication coverage area in a mountainous area, the generation AI simulates the topography and existing communication infrastructure to visually present the optimal placement. The input unit also allows the generation AI to perform simulations in real time based on the conditions input by the user and visually present the optimal installation location and conditions. For example, to optimally install a base station within a budget, the generation AI visually presents the optimal equipment selection and installation method based on the simulation results. This allows real-time simulations to visually present the optimal installation location and conditions.

[0056] The input unit can use the emotion estimation function to analyze the emotion of the user when entering input and automatically adjust the interface to reduce stress. The input unit, for example, analyzes the facial expression and voice of the user when entering input and measures the stress level using the emotion estimation function. For example, if the user is nervous, the color or design of the interface is changed to relax the user. The input unit also uses the emotion estimation function to analyze the emotion of the user when entering input in real time and automatically adjust the interface to reduce stress. For example, if the user is feeling anxious, the input process is simplified to reduce stress. The input unit also builds a system that analyzes the emotion of the user when entering input and automatically adjusts the interface to reduce stress. For example, if the user is feeling anxious, an encouraging message is displayed on the interface to reassure the user. In this way, the user's emotion can be analyzed and the interface can be automatically adjusted to reduce stress.

[0057] The suggestion unit allows the generation AI to generate different scenarios based on the input conditions and present multiple options to the user. For example, the suggestion unit generates multiple scenarios based on the conditions input by the user and presents the optimal installation location and conditions for each scenario. For example, when installing a base station in a high-rise building in an urban area, the suggestion unit proposes different installation locations and equipment selections. The suggestion unit also generates different scenarios based on the input conditions and presents multiple options to the user. For example, when expanding the communication coverage area in a mountainous area, the suggestion unit proposes different base station placements and radio wave adjustment methods. The suggestion unit also generates different scenarios based on the conditions input by the user and presents the optimal installation location and conditions for each scenario. For example, the suggestion unit proposes different equipment selections and installation methods to optimally install a base station within a budget. This allows different scenarios to be generated and presents multiple options to the user.

[0058] The input unit can accommodate voice input or gesture input for conditions entered by the user, enabling more intuitive operation. The input unit, for example, allows the user to input the base station installation location, purpose, and conditions to the generation AI using voice input. For example, the user might input "I want to install a base station on the roof of a high-rise building in an urban area" using voice input. The input unit also uses gesture input to allow the user to intuitively input the base station installation location and conditions. For example, input is made by pointing at the installation location on a map. The input unit also combines voice input and gesture input to allow the user to more intuitively input the base station installation location and conditions. For example, the user might input conditions using voice and specify the installation location using gestures. This allows for support of voice input and gesture input, enabling intuitive operation.

[0059] The input unit can use the emotion estimation function to provide real-time feedback on the emotions a user feels when entering text and make suggestions to elicit positive emotions. The input unit, for example, analyzes the emotions a user feels when entering text in real time and makes suggestions to elicit positive emotions. For example, if the user feels anxious, it displays an encouraging message. The input unit also uses the emotion estimation function to provide real-time feedback on the emotions a user feels when entering text and makes suggestions to elicit positive emotions. For example, if the user feels anxious, it simplifies the input process to reassure the user. The input unit also builds a system that monitors the emotions a user feels when entering text in real time and makes suggestions to elicit positive emotions. For example, if the user feels nervous, it changes the interface design to relax the user. In this way, the input unit provides real-time feedback on the user's emotions and makes suggestions to elicit positive emotions.

[0060] The proposal unit can perform predictive analysis based on past data and trends on the plan proposed by the generation AI to evaluate future optimality. The proposal unit, for example, performs predictive analysis based on past data on the plan proposed by the generation AI to evaluate future optimality. For example, it analyzes past communication demand data to predict future demand. The proposal unit also performs predictive analysis based on trend data on the proposed plan to evaluate future optimality. For example, it evaluates taking into account evolution of communication technology and market trends. The proposal unit also builds a system that performs predictive analysis based on past data and trends on the plan proposed by the generation AI to evaluate future optimality. For example, it evaluates based on past success stories and failure stories. This makes it possible to evaluate future optimality based on past data and trends.

[0061] The proposal unit allows the generation AI to automatically perform risk assessments on proposed plans and propose risk mitigation measures. The proposal unit, for example, automatically performs risk assessments on plans proposed by the generation AI and proposes risk mitigation measures. For example, it evaluates technical and operational risks associated with base station installation and proposes mitigation measures. The proposal unit also builds a system where the generation AI automatically performs risk assessments on proposed plans and proposes risk mitigation measures. For example, it evaluates the risk of natural disasters such as earthquakes and typhoons and proposes countermeasures. The proposal unit also automatically performs risk assessments on plans proposed by the generation AI and proposes risk mitigation measures. For example, it evaluates the risk of going over budget and proposes cost-cutting measures. This makes it possible to automatically perform risk assessments and propose risk mitigation measures.

[0062] The suggestion unit can use the emotion estimation function to analyze the emotion the user has toward the proposed plan and preferentially present plans that are emotionally easy to accept. The suggestion unit, for example, uses the emotion estimation function to analyze the emotion the user has toward the proposed plan in real time and preferentially present plans that are emotionally easy to accept. For example, plans that give the user a sense of security are prioritized. The suggestion unit also builds a system that analyzes the emotion the user has toward the proposed plan and preferentially presents plans that are emotionally easy to accept. For example, plans that give the user positive emotions are prioritized. The suggestion unit also uses the emotion estimation function to analyze the emotion the user has toward the proposed plan and preferentially present plans that are emotionally easy to accept. For example, plans that do not make the user feel anxious are prioritized. In this way, the user's emotions can be analyzed and plans that are emotionally easy to accept can be preferentially presented.

[0063] The proposal unit can reevaluate the proposed plan under different conditions or scenarios to generate multiple alternative plans. For example, the proposal unit reevaluates the plan proposed by the generation AI under different conditions to generate multiple alternative plans. For example, the budget or installation location conditions are changed and the plan is reevaluated. The proposal unit also builds a system that reevaluates the proposed plan under different scenarios to generate multiple alternative plans. For example, the plan is reevaluated taking into account fluctuations in communication demand and technological advances. The proposal unit also reevaluates the plan proposed by the generation AI under different conditions or scenarios to generate multiple alternative plans. For example, the proposal unit proposes a plan using different communication technologies or equipment. This makes it possible to reevaluate under different conditions or scenarios and generate multiple alternative plans.

[0064] The proposal unit can improve the plan proposed by the generation AI based on feedback and evaluations from other users, thereby providing a more optimal plan. The proposal unit, for example, collects feedback from other users on the plan proposed by the generation AI and improves the plan based on the results. For example, it adjusts the installation location and equipment selection by reflecting user opinions. The proposal unit also builds a system that improves the proposed plan based on evaluations from other users. For example, it adjusts the priority of the plan based on user evaluation scores. The proposal unit also improves the plan proposed by the generation AI based on feedback and evaluations from other users, thereby providing a more optimal plan. For example, it adjusts the installation method and operating costs by reflecting user opinions. In this way, the plan can be improved based on feedback and evaluations from other users, thereby providing a more optimal plan.

[0065] The suggestion unit can use the emotion estimation function to monitor the emotion the user feels toward the proposed plan in real time, and propose a plan that elicits a positive emotional response. The suggestion unit, for example, uses the emotion estimation function to monitor the emotion the user feels toward the proposed plan in real time, and proposes a plan that elicits a positive emotional response. For example, it prioritizes a plan that makes the user feel relieved. The suggestion unit also builds a system that monitors the emotion the user feels toward the proposed plan in real time, and proposes a plan that elicits a positive emotional response. For example, it prioritizes a plan that makes the user feel happy. The suggestion unit also uses the emotion estimation function to monitor the emotion the user feels toward the proposed plan in real time, and proposes a plan that elicits a positive emotional response. For example, it prioritizes a plan that does not make the user feel anxious. In this way, it is possible to monitor the user's emotion in real time, and propose a plan that elicits a positive emotional response.

[0066] When the generation AI creates an estimate, the estimation unit can perform predictive analysis based on past cost data and market trends to take future cost fluctuations into account. For example, when the generation AI creates an estimate, the estimation unit performs predictive analysis based on past cost data to take future cost fluctuations into account. For example, it analyzes data on past equipment costs and installation work costs to predict future costs. The estimation unit also performs predictive analysis based on market trends for each item included in the estimate to take future cost fluctuations into account. For example, it predicts price fluctuations of communications equipment and fluctuations in labor costs. The estimation unit also builds a system in which the generation AI performs predictive analysis based on past cost data and market trends to take future cost fluctuations into account when creating an estimate. For example, it predicts future costs based on past data and reflects this in the estimate. This makes it possible to take future cost fluctuations into account based on past cost data and market trends.

[0067] The estimation unit allows the generation AI to provide detailed explanations and rationales for each item included in the estimate, making it easier for the user to understand. For example, the estimation unit allows the generation AI to provide detailed explanations and rationales for each item included in the estimate. For example, the estimation unit provides a detailed breakdown of equipment costs and installation work costs, making it easier for the user to understand. The estimation unit also builds a system in which the generation AI provides detailed explanations and rationales for each item included in the estimate. For example, the estimation unit retrieves the rationale for each item's cost from a database and presents it to the user. The estimation unit also allows the generation AI to provide detailed explanations and rationales for each item included in the estimate, making it easier for the user to understand. For example, the estimation unit provides a detailed explanation of the installation work procedures and the reasons for selecting the necessary equipment. In this way, the estimation unit provides detailed explanations and rationales for each item included in the estimate, making it easier for the user to understand.

[0068] The estimation unit can use the emotion estimation function to analyze the emotion the user has toward the estimate and present an estimate that is emotionally easy to understand. The estimation unit, for example, uses the emotion estimation function to analyze the emotion the user has toward the estimate in real time and present an estimate that is emotionally easy to understand. For example, it prioritizes estimates that give the user a sense of security. The estimation unit also builds a system that analyzes the emotion the user has toward the estimate and presents estimates that are emotionally easy to understand. For example, it prioritizes estimates that give the user positive emotions. The estimation unit also uses the emotion estimation function to analyze the emotion the user has toward the estimate and presents estimates that are emotionally easy to understand. For example, it prioritizes estimates that do not make the user feel anxious. In this way, it is possible to analyze the user's emotions and present estimates that are emotionally easy to understand.

[0069] The estimation unit can adapt the estimate to different currencies or regional prices and provide estimates from an international perspective. For example, the generation AI in the estimation unit adapts the estimate to different currencies and provides estimates from an international perspective. For example, estimates are presented in multiple currencies such as dollars, euros, and yen. The estimation unit also builds a system that adapts the estimate to different regional prices and provides estimates from an international perspective. For example, estimates are presented taking into account local prices and labor costs. The estimation unit also adapts the estimate to different currencies or regional prices and provides estimates from an international perspective. For example, estimates are presented taking into account the cost of communication infrastructure in each region. This makes it possible to adapt to different currencies or regional prices and provide estimates from an international perspective.

[0070] The estimation unit can simultaneously generate estimates under different scenarios and conditions when the generation AI creates an estimate, and present multiple options to the user. For example, when the generation AI creates an estimate, the estimation unit can simultaneously generate estimates under different scenarios and conditions, and present multiple options to the user. For example, it can present estimates under different budgets and installation location conditions. The estimation unit can also build a system that simultaneously generates estimates under different scenarios and conditions, and presents multiple options to the user. For example, it can present estimates using different communication technologies and equipment. The estimation unit can also simultaneously generate estimates under different scenarios and conditions when the generation AI creates an estimate, and present multiple options to the user. For example, it can present estimates that take into account different installation methods and operating costs. This makes it possible to simultaneously generate estimates under different scenarios and conditions, and present multiple options to the user.

[0071] The estimation unit can use the emotion estimation function to monitor the emotion the user feels toward the estimate in real time, and propose estimates that elicit a positive emotional response. The estimation unit, for example, can use the emotion estimation function to monitor the emotion the user feels toward the estimate in real time, and propose estimates that elicit a positive emotional response. For example, it prioritizes estimates that make the user feel relieved. The estimation unit also builds a system that monitors the emotion the user feels toward the estimate in real time, and proposes estimates that elicit a positive emotional response. For example, it prioritizes estimates that make the user feel happy. The estimation unit also uses the emotion estimation function to monitor the emotion the user feels toward the estimate in real time, and proposes estimates that elicit a positive emotional response. For example, it prioritizes estimates that do not make the user feel anxious. In this way, it is possible to monitor the user's emotion in real time, and propose estimates that elicit a positive emotional response.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The input section allows the generation AI to refer to past installation examples and success stories for the conditions entered by the user and propose optimal conditions. For example, based on the conditions entered by the user, the generation AI searches a database for past installation examples and presents examples that were successful under similar conditions. When installing a base station in a high-rise building in an urban area, the AI ​​can refer to past successful examples under similar conditions and propose the optimal installation location and equipment selection. In addition, when expanding the communication coverage area in mountainous areas, the AI ​​can propose the optimal base station placement and radio wave adjustment method based on past successful examples under similar conditions. Furthermore, in order to optimally install a base station within a budget, the AI ​​can also propose the optimal equipment selection and installation method based on past successful examples. This allows the AI ​​to propose optimal conditions based on past successful examples.

[0074] The input section allows the generation AI to perform real-time simulations based on the conditions entered by the user, and visually present the optimal installation location and conditions. For example, based on the conditions entered by the user, the generation AI performs real-time simulations and visually presents the optimal installation location and conditions in a 3D model. When installing a base station in a high-rise building in an urban area, the system simulates the building structure and surrounding radio wave conditions to visually show the optimal installation location. When expanding the communication coverage area in mountainous areas, the system can simulate the terrain and existing communication infrastructure to visually show the optimal placement. Furthermore, to optimally install base stations within budget, the system can visually show the optimal equipment selection and installation method based on the simulation results. This allows real-time simulations to be performed and the optimal installation location and conditions to be visually shown.

[0075] The input unit can use an emotion estimation function to analyze the emotions of the user when inputting and automatically adjust the interface to reduce stress. For example, the emotion estimation function can be used to analyze the user's facial expressions and voice when inputting and measure the stress level. If the user is nervous, the color or design of the interface can be changed to relax them. Also, if the user is feeling impatient, the input process can be simplified to reduce stress. Furthermore, if the user is feeling anxious, an encouraging message can be displayed on the interface to reassure them. In this way, the user's emotions can be analyzed and the interface can be automatically adjusted to reduce stress.

[0076] The suggestion unit allows the generation AI to generate different scenarios based on the input conditions and present multiple options to the user. For example, the generation AI generates multiple scenarios based on the conditions input by the user and presents the optimal installation location and conditions for each scenario. When installing a base station in a high-rise building in an urban area, it can propose different installation locations and equipment selections. Also, when expanding the communication coverage area in mountainous areas, it can propose different base station placements and radio wave adjustment methods. Furthermore, it can propose different equipment selections and installation methods to optimally install base stations within a budget. This allows different scenarios to be generated and multiple options to be presented to the user.

[0077] The input unit can accommodate voice and gesture input for user input conditions, enabling more intuitive operation. For example, a user can use voice input to input the base station installation location, purpose, and conditions to the generation AI. For example, a user can input by voice, "I want to install a base station on the roof of a high-rise building in an urban area." Gesture input can also be used to allow users to intuitively input the base station installation location and conditions. For example, input can be made by pointing at the installation location on a map. Furthermore, voice input and gesture input can be combined to allow users to more intuitively input the base station installation location and conditions. For example, the user can input conditions by voice and specify the installation location by gesture. This supports voice input and gesture input, enabling intuitive operation.

[0078] The input unit can use the emotion estimation function to provide real-time feedback on the emotions the user feels when entering text and make suggestions to elicit positive emotions. For example, the input unit can analyze the emotions the user feels when entering text in real time and make suggestions to elicit positive emotions. If the user feels anxious, an encouraging message can be displayed. If the user feels impatient, the input process can be simplified to reassure the user. Furthermore, if the user feels nervous, the interface design can be changed to help the user relax. This allows the input unit to provide real-time feedback on the user's emotions and make suggestions to elicit positive emotions.

[0079] The proposal unit can perform predictive analysis based on past data and trends on the plan proposed by the generation AI to evaluate its future optimality. For example, it can perform predictive analysis based on past data on the plan proposed by the generation AI to evaluate its future optimality. It can analyze past communication demand data to predict future demand. It can also perform predictive analysis based on trend data on the proposed plan to evaluate its future optimality. For example, it can evaluate plans taking into account evolution in communication technology and market trends. Furthermore, it can build a system that performs predictive analysis based on past data and trends on the plan proposed by the generation AI to evaluate its future optimality. For example, it can evaluate plans based on past successes and failures. This makes it possible to evaluate future optimality based on past data and trends.

[0080] The proposal unit allows the generation AI to automatically perform risk assessments on proposed plans and propose risk mitigation measures. For example, the generation AI can automatically perform risk assessments on plans proposed by the generation AI and propose risk mitigation measures. It can evaluate the technical and operational risks associated with base station installation and propose mitigation measures. It can also build a system in which the generation AI automatically performs risk assessments on proposed plans and proposes risk mitigation measures. For example, it can evaluate the risk of natural disasters such as earthquakes and typhoons and propose countermeasures. It can also automatically perform risk assessments on plans proposed by the generation AI and propose risk mitigation measures. For example, it can evaluate the risk of going over budget and propose cost-cutting measures. This makes it possible to automatically perform risk assessments and propose risk mitigation measures.

[0081] The suggestion unit can use the emotion estimation function to analyze the emotion the user has toward the proposed plan and prioritize presenting plans that are emotionally easy to accept. For example, the emotion estimation function can be used to analyze the emotion the user has toward the proposed plan in real time and prioritize presenting plans that are emotionally easy to accept. Plans that give the user a sense of security can be prioritized. Also, a system can be constructed that analyzes the emotion the user has toward the proposed plan and prioritize presenting plans that are emotionally easy to accept. For example, plans that give the user positive emotions can be prioritized. Furthermore, the emotion estimation function can be used to analyze the emotion the user has toward the proposed plan and prioritize presenting plans that are emotionally easy to accept. For example, plans that do not make the user feel anxious can be prioritized. In this way, the user's emotions can be analyzed and plans that are emotionally easy to accept can be prioritized.

[0082] When the generation AI creates an estimate, the estimation unit can perform predictive analysis based on past cost data and market trends to take future cost fluctuations into account. For example, when the generation AI creates an estimate, it can perform predictive analysis based on past cost data to take future cost fluctuations into account. It can analyze data on past equipment costs and installation work costs to predict future costs. The generation AI can also perform predictive analysis based on market trends for each item included in the estimate to take future cost fluctuations into account. For example, it can predict price fluctuations of communications equipment and fluctuations in labor costs. Furthermore, when the generation AI creates an estimate, it can perform predictive analysis based on past cost data and market trends to build a system that takes future cost fluctuations into account. For example, it can predict future costs based on past data and reflect them in the estimate. This makes it possible to take future cost fluctuations into account based on past cost data and market trends.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The input unit receives input from the user about the location, purpose, and conditions for installing the base station. For example, the user might input, "I want to install a base station on the roof of a high-rise building in an urban area." The user can also input, "I want to expand the communication coverage area in mountainous areas." The user can also input, "My budget should be within XX yen." Step 2: The analysis unit analyzes the installation location, purpose, and conditions input by the input unit. For example, the analysis unit analyzes the radio wave conditions at the input installation location. The analysis unit can also analyze the optimal installation method based on the input purpose. The analysis unit can also analyze costs based on the input conditions. Step 3: The proposal unit proposes an optimal plan based on the results of the analysis by the analysis unit. For example, the proposal unit proposes an optimal plan for installing base stations on the rooftops of high-rise buildings in urban areas. The proposal unit can also propose an optimal plan for expanding communication coverage areas in mountainous areas. The proposal unit can also propose an optimal plan within a budget. Step 4: The estimation unit creates an estimate based on the plan proposed by the proposal unit. For example, the estimation unit estimates the cost of equipment required to install the base station. The estimation unit may also estimate the cost of installation work. The estimation unit may also estimate operating costs.

[0085] 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.

[0086] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0091] 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.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0106] 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0121] 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] 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.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0126] 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.

[0127] 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.

[0128] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] 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.

[0135] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0136] 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.

[0137] 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).

[0138] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0146] The hardware resource that executes the specific process 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 process may be a single processor.

[0147] 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.

[0148] 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.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] 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.

[0151] 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. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system equipped with a generative AI, an input unit for inputting the installation location, purpose, and conditions of the base station from a user; an analysis unit that analyzes the installation location, the purpose, and the conditions input by the input unit; a proposal unit that proposes an optimal plan based on the results of the analysis by the analysis unit; an estimation unit that creates an estimate based on the plan proposed by the proposal unit; A system characterized by:

2. The input unit The AI ​​generation system will refer to past installation cases and success stories to propose optimal conditions for the conditions entered by the user.

2. The system of claim 1.

3. The input unit Based on the input conditions, the generation AI performs a simulation in real time and visually presents the optimal installation location and conditions.

2. The system of claim 1.

4. The input unit Analyze the user's emotions as they type and automatically adjust the interface to reduce stress.

2. The system of claim 1.

5. The proposal unit Based on the input conditions, the generation AI generates different scenarios and presents the user with a plurality of options.

2. The system of claim 1.

6. The input unit The conditions input by the user can be input by voice or gesture, enabling more intuitive operation.

2. The system of claim 1.

Images