system

Abstract

The system according to this embodiment aims to propose an optimal antenna placement in a complex urban environment and improve communication quality. [Solution] The system according to the embodiment comprises a simulation unit, a proposal unit, a comparison unit, and a visualization unit. The simulation unit simulates the propagation of radio waves. The proposal unit proposes the optimal antenna placement based on the simulation results obtained by the simulation unit. The comparison unit compares the coverage rate with that of other companies. The visualization unit visualizes the difficulty level of the building owner's response.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to efficiently determine an optimal antenna arrangement in a complex urban environment, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal antenna arrangement in a complex urban environment and improve communication quality.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a simulation unit, a proposal unit, a comparison unit, and a visualization unit. The simulation unit simulates the propagation of radio waves. The proposal unit proposes the optimal antenna placement based on the simulation results obtained by the simulation unit. The comparison unit compares coverage rates with those of other companies. The visualization unit visualizes the difficulty level for building owners to address the issue. [Effects of the Invention]

[0007] The system according to this embodiment can propose an optimal antenna placement in a complex urban environment and improve communication quality. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An assistant according to an embodiment of the present invention is a system for performing mobile phone radio wave simulations and assisting in the optimal placement of 5G antennas. This system simulates and proposes the optimal antenna placement in real time to efficiently deliver radio waves in complex urban environments such as between buildings, underground, and in areas with a high density of antennas. This enables telecommunications carriers to efficiently build 5G networks and provide users with a high-quality communication experience. It also compares coverage rates with other companies, extracts areas that should be prioritized, and visualizes and provides the difficulty of implementation for building owners. For example, the system simulates radio wave propagation in complex urban environments such as between buildings, underground, and in areas with a high density of antennas. In this process, AI is used to achieve highly accurate radio wave simulations. For example, it performs simulations that take into account the influence of buildings and underground structures, and proposes the optimal antenna placement. Next, based on the simulation results, it proposes the optimal antenna placement in real time. For example, it presents multiple candidate antenna placements and evaluates the radio wave coverage rate and the presence or absence of interference for each placement. This allows telecommunications carriers to instantly determine the optimal placement. Furthermore, it compares coverage rates with other companies and extracts areas that should be prioritized. For example, by identifying areas with low coverage by competitors and prioritizing antenna placement in those areas, a company can enhance its competitiveness. It can also visualize the difficulty of dealing with building owners and provide information to facilitate negotiations for antenna installation. This allows telecommunications carriers to efficiently build 5G networks and provide users with a high-quality communication experience. For instance, stable communication becomes possible even between buildings in urban areas or underground, improving user satisfaction. Furthermore, optimal antenna placement improves cost efficiency, enhancing the operational efficiency of telecommunications carriers. This enables the assistant to perform mobile phone signal simulations and assist in determining the optimal placement of 5G antennas.

[0029] The assistant according to this embodiment comprises a simulation unit, a proposal unit, a comparison unit, and a visualization unit. The simulation unit simulates the propagation of radio waves. The simulation unit can, for example, use AI to achieve high-precision radio wave simulations. The simulation unit can perform simulations that take into account the influence of buildings and underground structures. For example, the simulation unit simulates the reflection and absorption of radio waves considering the influence of buildings. The simulation unit can also simulate the propagation of radio waves considering the underground structure. The simulation unit can use AI to perform simulations that take into account the influence of buildings and underground structures. The proposal unit proposes the optimal antenna placement based on the simulation results obtained by the simulation unit. For example, the proposal unit presents multiple candidate antenna placements and evaluates the radio wave coverage rate and the presence or absence of interference for each placement. The proposal unit can use AI to present multiple candidate antenna placements and evaluate the radio wave coverage rate and the presence or absence of interference for each placement. The comparison unit compares the coverage rate with that of other companies. For example, the comparison unit identifies areas where other companies have low coverage rates and prioritizes placing antennas in those areas. The comparison unit uses AI to identify areas with low coverage by other companies and can prioritize antenna placement in those areas. The visualization unit visualizes the difficulty of dealing with building owners. For example, the visualization unit visualizes the difficulty of dealing with building owners and provides information to facilitate negotiations for antenna installation. The visualization unit can use AI to visualize the difficulty of dealing with building owners and provide information to facilitate negotiations for antenna installation. As a result, the assistant according to the embodiment can efficiently build a 5G network by simulating radio wave propagation, proposing the optimal antenna placement, comparing coverage with other companies, and visualizing the difficulty of dealing with building owners.

[0030] The simulation unit simulates radio wave propagation. For example, the simulation unit uses AI to achieve highly accurate radio wave simulations. Specifically, the simulation unit can perform simulations that take into account the influence of buildings and underground structures. For instance, the simulation unit simulates radio wave reflection and absorption considering the influence of buildings. Detailed data such as building materials, shapes, and placement are input, and the AI ​​analyzes these elements to predict the radio wave propagation path. The simulation unit can also simulate radio wave propagation considering underground structures. It analyzes radio wave attenuation and reflection in complex underground environments such as subways and underground parking lots in detail to derive the optimal antenna placement. The simulation unit uses AI to perform simulations that consider the influence of buildings and underground structures. The AI ​​uses machine learning algorithms to learn from past data and improve the accuracy of the simulation. For example, based on past radio wave propagation data, it predicts the behavior of radio waves under specific environmental conditions, improving the accuracy of the simulation results. Furthermore, the simulation unit can perform real-time simulations, enabling rapid responses to on-site conditions. As a result, the simulation unit can simulate radio wave propagation with high accuracy and efficiency, supporting optimal antenna placement.

[0031] The proposal unit proposes the optimal antenna placement based on the simulation results obtained by the simulation unit. For example, the proposal unit presents multiple antenna placement options and evaluates the radio wave coverage and interference for each placement. Specifically, the proposal unit can use AI to present multiple antenna placement options and evaluate the radio wave coverage and interference for each placement. The AI ​​analyzes the simulation results and executes an algorithm to derive the optimal antenna placement. For example, the AI ​​calculates a placement that maximizes radio wave coverage while minimizing interference. Furthermore, the proposal unit can propose the optimal placement considering the user's requirements and constraints. For example, it can provide flexible proposals tailored to user needs, such as prioritizing coverage in a specific area or requesting a cost-effective placement. Based on the simulation results, the proposal unit generates multiple placement scenarios and evaluates each scenario. The evaluation includes radio wave coverage, interference, installation costs, and installation difficulty. This allows the proposal unit to propose the optimal antenna placement to the user and support efficient network construction.

[0032] The comparison unit compares coverage rates with those of other companies. For example, the comparison unit identifies areas where other companies have low coverage and prioritizes antenna placement in those areas. Specifically, the comparison unit can use AI to identify areas where other companies have low coverage and prioritize antenna placement in those areas. The AI ​​identifies areas with low coverage by collecting and analyzing coverage rate data from other companies. For example, it plots the coverage rate data of other companies on a map to visually display areas with low coverage. Furthermore, the comparison unit can propose the optimal antenna placement for the identified areas. This allows the comparison unit to secure a competitive advantage over other companies and support the construction of an efficient network. The comparison unit regularly updates the coverage rate data of other companies, enabling comparisons based on the latest information. This allows it to always grasp the latest situation and respond quickly. Furthermore, the comparison unit can perform more accurate comparisons by considering not only the coverage rate data of other companies but also user feedback and usage data. This allows the comparison unit to accurately compare coverage rates with other companies and propose the optimal antenna placement.

[0033] The visualization unit visualizes the difficulty level of dealing with building owners. For example, the visualization unit visualizes the difficulty level of dealing with building owners and provides information to facilitate negotiations for antenna installation. Specifically, the visualization unit can use AI to visualize the difficulty level of dealing with building owners and provide information to facilitate negotiations for antenna installation. The AI ​​analyzes the building owner's past interaction history and current situation to evaluate the difficulty level of dealing with them. For example, it scores the difficulty level of dealing with building owners based on data on building owners who were cooperative with antenna installation in the past, or conversely, those who were reluctant to install it. Furthermore, the visualization unit displays the difficulty level of dealing with building owners on a map, making it easy to understand visually. This makes it easier for negotiators to decide which building owners to prioritize approaching. In addition to the difficulty level of dealing with building owners, the visualization unit can also visualize the advantages and disadvantages of antenna installation. For example, it can visually display the improvement in radio wave coverage rate due to antenna installation, installation costs, and installation difficulty, providing information to facilitate negotiations. This allows the visualization unit to efficiently negotiate with building owners and provide support for achieving optimal antenna placement.

[0034] The simulation unit can perform radio wave simulations that take into account the effects of buildings and underground structures. For example, the simulation unit can simulate radio wave reflection and absorption considering the effects of buildings. The simulation unit can also simulate radio wave propagation considering underground structures. The simulation unit can use AI to perform simulations that take into account the effects of buildings and underground structures. This makes it possible to perform more realistic radio wave simulations by taking into account the effects of buildings and underground structures.

[0035] The proposal function can present multiple antenna placement options and evaluate the radio wave coverage and interference for each placement. For example, the proposal function can present multiple antenna placement options and evaluate the radio wave coverage and interference for each placement. The proposal function can use AI to present multiple antenna placement options and evaluate the radio wave coverage and interference for each placement. This allows the system to propose the optimal antenna placement by presenting multiple options and evaluating radio wave coverage and interference.

[0036] The comparison unit can identify areas where competitors have low coverage and prioritize the placement of antennas in those areas. For example, the comparison unit can identify areas where competitors have low coverage and prioritize the placement of antennas in those areas. The comparison unit can use AI to identify areas where competitors have low coverage and prioritize the placement of antennas in those areas. This allows for increased competitiveness by identifying areas where competitors have low coverage and prioritizing antenna placement.

[0037] The visualization unit can visualize the difficulty level of dealing with building owners and provide information to facilitate negotiations for antenna installation. For example, the visualization unit can visualize the difficulty level of dealing with building owners and provide information to facilitate negotiations for antenna installation. The visualization unit can use AI to visualize the difficulty level of dealing with building owners and provide information to facilitate negotiations for antenna installation. This allows for smoother negotiations for antenna installation by visualizing the difficulty level of dealing with building owners.

[0038] The simulation unit can predict radio wave propagation by taking into account changes in weather and time of day during the simulation. For example, the simulation unit performs simulations considering radio wave attenuation during rainy weather. The simulation unit can also perform simulations considering radio wave reflection and interference at night. The simulation unit can also predict radio wave propagation throughout the year by considering seasonal weather patterns. This makes it possible to predict radio wave propagation more realistically by taking into account changes in weather and time of day. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI.

[0039] The simulation unit can reflect the materials and heights of surrounding buildings in detail during the simulation. For example, the simulation unit simulates the reflection and absorption of radio waves considering the building materials (concrete, glass, metal, etc.). The simulation unit can also simulate the shielding effect of radio waves considering the height of buildings. The simulation unit can also simulate the propagation path of radio waves in detail considering the arrangement and density of buildings. This makes it possible to perform more accurate radio wave simulations by reflecting the materials and heights of surrounding buildings in detail. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI.

[0040] The simulation unit can predict radio wave propagation by considering traffic volume and people's movement patterns during simulations. For example, during periods of heavy traffic, the simulation unit considers radio wave shielding by vehicles during simulations. The simulation unit can also simulate radio wave propagation during congestion by considering people's movement patterns. The simulation unit can also predict radio wave propagation by considering the gathering of people during specific events. This makes it possible to predict radio wave propagation in a more realistic way by considering traffic volume and people's movement patterns. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI.

[0041] The simulation unit can improve accuracy by referring to past simulation data during the simulation. For example, the simulation unit can improve the accuracy of the current simulation based on past simulation data. The simulation unit can also compare past simulation results with actual measurement data and adjust the simulation model. The simulation unit can also analyze past simulation data, extract specific patterns, and reflect them in the simulation. In this way, the accuracy of the current simulation can be improved by referring to past simulation data. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without using AI.

[0042] The proposal department can propose the optimal placement of antennas, taking into account the installation costs. For example, the proposal department can propose a cost-effective placement to minimize antenna installation costs. The proposal department can also propose the optimal placement by considering the balance between installation costs and coverage. The proposal department can also propose a placement that considers installation costs while also allowing for future expansion. This allows for the proposal of a cost-effective placement by considering antenna installation costs. Some or all of the above processing in the proposal department may be performed using AI, for example, or without using AI.

[0043] The proposal unit can determine the placement of antennas while considering the frequency of antenna maintenance. For example, the proposal unit may prioritize the placement of antennas that require less maintenance. The proposal unit may also place antennas in locations where maintenance is easy. The proposal unit may also propose a placement that maximizes coverage while considering the frequency of maintenance. In this way, by considering the frequency of antenna maintenance, it is possible to propose a placement that is easy to maintain. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0044] The proposal unit can propose antenna placements that take into account the safety of the installation location. For example, the proposal unit may place the antenna in a highly safe location. The proposal unit can also evaluate safety by considering the surrounding environment of the installation location. The proposal unit can also propose placements that maximize coverage while ensuring safety. In this way, by considering the safety of the antenna installation location, it is possible to propose highly safe placements. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0045] The proposal team can evaluate the impact of antenna installation on the landscape and decide on the placement at the time of proposal. For example, the proposal team may place the antenna in a location with minimal impact on the landscape. The proposal team can also select an installation location that does not impair the landscape. The proposal team can also propose a placement that maximizes coverage while minimizing the impact on the landscape. In this way, by evaluating the impact of antenna installation on the landscape, it is possible to propose a placement that does not impair the landscape. Some or all of the above processing in the proposal team may be performed using AI, for example, or without using AI.

[0046] The comparison unit can evaluate coverage rates by considering the antenna installation plans of other companies during the comparison process. For example, the comparison unit evaluates coverage rates based on the antenna installation plans of other companies. The comparison unit can also compare the antenna installation plans of other companies with its own plan and propose the optimal coverage rate. The comparison unit can also evaluate coverage rates that enhance competitiveness while considering the antenna installation plans of other companies. This allows for the evaluation of more competitive coverage rates by considering the antenna installation plans of other companies. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI.

[0047] The comparison unit can evaluate coverage rates while considering the service quality of other companies during the comparison process. For example, the comparison unit evaluates coverage rates based on the service quality of other companies. The comparison unit can also compare the service quality of other companies with its own quality and propose an optimal coverage rate. The comparison unit can also evaluate a coverage rate that enhances competitiveness while considering the service quality of other companies. This allows for the evaluation of a more competitive coverage rate by considering the service quality of other companies. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI.

[0048] The comparison unit can evaluate coverage rates by considering the pricing plans of other companies during the comparison process. For example, the comparison unit evaluates coverage rates based on the pricing plans of other companies. The comparison unit can also compare the pricing plans of other companies with its own plan and propose the optimal coverage rate. The comparison unit can also evaluate coverage rates that enhance competitiveness while considering the pricing plans of other companies. This allows for the evaluation of more competitive coverage rates by considering the pricing plans of other companies. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI.

[0049] The comparison unit can evaluate coverage rates while considering the customer satisfaction of other companies during the comparison process. For example, the comparison unit evaluates coverage rates based on the customer satisfaction of other companies. The comparison unit can also compare the customer satisfaction of other companies with that of the company itself and propose the optimal coverage rate. The comparison unit can also evaluate a coverage rate that enhances competitiveness while considering the customer satisfaction of other companies. This allows for the evaluation of a more competitive coverage rate by considering the customer satisfaction of other companies. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI.

[0050] The visualization unit can evaluate the difficulty level by referring to the building owner's past response history during visualization. For example, if the building owner has been cooperative with antenna installation in the past, the visualization unit will evaluate the difficulty level as low. If the building owner has opposed antenna installation in the past, the visualization unit may evaluate the difficulty level as high. The visualization unit can also analyze the building owner's past response history and predict the success rate of negotiations. This allows for an accurate evaluation of the difficulty level of negotiations by referring to the building owner's past response history. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI.

[0051] The visualization unit can evaluate the difficulty level by considering the building owner's attribute information during visualization. For example, the visualization unit evaluates the difficulty level of negotiations by considering the building owner's age and occupation. The visualization unit can also evaluate the difficulty level by combining the building owner's past interaction history with attribute information. The visualization unit can also propose the optimal negotiation method based on the building owner's attribute information. This allows for an accurate evaluation of the difficulty level of negotiations by considering the building owner's attribute information. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI.

[0052] The visualization unit can evaluate the difficulty level by considering the building owner's geographical location information during visualization. For example, the visualization unit may rate the difficulty level higher if the building owner is located in an urban area. The visualization unit may also rate the difficulty level lower if the building owner is located in a suburban area. The visualization unit can also evaluate the difficulty level of negotiations by considering the building owner's location and surrounding environment. This allows for an accurate evaluation of the difficulty level of negotiations by considering the building owner's geographical location information. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI.

[0053] The visualization unit can evaluate the difficulty level by considering the building owner's financial situation during visualization. For example, if the building owner's financial situation is good, the visualization unit will evaluate the difficulty level as low. If the building owner's financial situation is unstable, the visualization unit may evaluate the difficulty level as high. The visualization unit can also evaluate the difficulty level by combining the building owner's financial situation with past interaction history. This allows for an accurate evaluation of the difficulty level of negotiations by considering the building owner's financial situation. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The simulation unit can take into account the surrounding natural environment (e.g., trees and bodies of water) when simulating radio wave propagation. For example, the simulation unit can simulate the attenuation of radio waves due to tree leaves and branches. The simulation unit can also simulate radio wave propagation considering reflection and absorption by bodies of water. The simulation unit can also predict radio wave propagation throughout the year by considering seasonal changes in the natural environment. This makes it possible to perform more realistic radio wave simulations by taking the surrounding natural environment into account.

[0056] The comparison unit can consider the performance and technical specifications of other companies' antennas when evaluating coverage rates in consideration of other companies' antenna installation plans. For example, the comparison unit can evaluate coverage rates based on the output and sensitivity of other companies' antennas. The comparison unit can also compare the technical specifications of other companies' antennas with those of the company's own company and propose the optimal coverage rate. The comparison unit can also evaluate coverage rates that enhance competitiveness while considering the performance of other companies' antennas. This allows for the evaluation of more competitive coverage rates by considering the performance and technical specifications of other companies' antennas.

[0057] The visualization unit can evaluate the difficulty of dealing with a building owner by referring to the building owner's past interaction history. For example, if the building owner has been cooperative with antenna installation in the past, the visualization unit will rate the difficulty lower. If the building owner has opposed antenna installation in the past, the visualization unit can rate the difficulty higher. The visualization unit can also analyze the building owner's past interaction history and predict the success rate of negotiations. This allows for an accurate assessment of the difficulty of negotiations by referring to the building owner's past interaction history.

[0058] The proposal department can propose the optimal placement of antennas, taking into account the installation costs. For example, the proposal department can propose a cost-effective placement to minimize installation costs. The proposal department can also propose an optimal placement that balances installation costs with coverage. The proposal department can also propose a placement that considers installation costs while also allowing for future expansion. In this way, a cost-effective placement can be proposed by taking antenna installation costs into consideration.

[0059] The visualization unit can evaluate the difficulty level by considering the building owner's attribute information during visualization. For example, the visualization unit evaluates the difficulty level of negotiations by considering the building owner's age and occupation. The visualization unit can also evaluate the difficulty level by combining the building owner's past interaction history with attribute information. Based on the building owner's attribute information, the visualization unit can also suggest the optimal negotiation method. In this way, the difficulty level of negotiations can be accurately evaluated by considering the building owner's attribute information.

[0060] The simulation unit can predict radio wave propagation by taking into account changes in weather and time of day during simulations. For example, the simulation unit will perform simulations considering radio wave attenuation during rainy weather. The simulation unit can also perform simulations considering radio wave reflection and interference at night. The simulation unit can also predict radio wave propagation throughout the year by considering seasonal weather patterns. This makes it possible to make more realistic predictions of radio wave propagation by taking into account changes in weather and time of day.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The simulation unit simulates radio wave propagation. The simulation unit uses AI to achieve high-precision radio wave simulations, taking into account the effects of buildings and underground structures. For example, it simulates radio wave reflection and absorption considering the effects of buildings, and simulates radio wave propagation considering underground structures. Step 2: The proposal unit proposes the optimal antenna placement based on the simulation results obtained by the simulation unit. The proposal unit presents multiple candidate antenna placements and evaluates the radio wave coverage and interference status for each placement. Using AI, it is possible to present multiple candidate antenna placements and evaluate the radio wave coverage and interference status for each placement. Step 3: The comparison unit compares coverage rates with other companies. The comparison unit identifies areas where other companies have low coverage and prioritizes antenna placement in those areas. Using AI, it is possible to identify areas where other companies have low coverage and prioritize antenna placement in those areas. Step 4: The visualization unit visualizes the difficulty level of dealing with the building owner. The visualization unit visualizes the difficulty level of dealing with the building owner and provides information to facilitate negotiations for antenna installation. By using AI, it is possible to visualize the difficulty level of dealing with the building owner and provide information to facilitate negotiations for antenna installation.

[0063] (Example of form 2) An assistant according to an embodiment of the present invention is a system for performing mobile phone radio wave simulations and assisting in the optimal placement of 5G antennas. This system simulates and proposes the optimal antenna placement in real time to efficiently deliver radio waves in complex urban environments such as between buildings, underground, and in areas with a high density of antennas. This enables telecommunications carriers to efficiently build 5G networks and provide users with a high-quality communication experience. It also compares coverage rates with other companies, extracts areas that should be prioritized, and visualizes and provides the difficulty of implementation for building owners. For example, the system simulates radio wave propagation in complex urban environments such as between buildings, underground, and in areas with a high density of antennas. In this process, AI is used to achieve highly accurate radio wave simulations. For example, it performs simulations that take into account the influence of buildings and underground structures, and proposes the optimal antenna placement. Next, based on the simulation results, it proposes the optimal antenna placement in real time. For example, it presents multiple candidate antenna placements and evaluates the radio wave coverage rate and the presence or absence of interference for each placement. This allows telecommunications carriers to instantly determine the optimal placement. Furthermore, it compares coverage rates with other companies and extracts areas that should be prioritized. For example, by identifying areas with low coverage by competitors and prioritizing antenna placement in those areas, a company can enhance its competitiveness. It can also visualize the difficulty of dealing with building owners and provide information to facilitate negotiations for antenna installation. This allows telecommunications carriers to efficiently build 5G networks and provide users with a high-quality communication experience. For instance, stable communication becomes possible even between buildings in urban areas or underground, improving user satisfaction. Furthermore, optimal antenna placement improves cost efficiency, enhancing the operational efficiency of telecommunications carriers. This enables the assistant to perform mobile phone signal simulations and assist in determining the optimal placement of 5G antennas.

[0064] The assistant according to this embodiment comprises a simulation unit, a proposal unit, a comparison unit, and a visualization unit. The simulation unit simulates the propagation of radio waves. The simulation unit can, for example, use AI to achieve high-precision radio wave simulations. The simulation unit can perform simulations that take into account the influence of buildings and underground structures. For example, the simulation unit simulates the reflection and absorption of radio waves considering the influence of buildings. The simulation unit can also simulate the propagation of radio waves considering the underground structure. The simulation unit can use AI to perform simulations that take into account the influence of buildings and underground structures. The proposal unit proposes the optimal antenna placement based on the simulation results obtained by the simulation unit. For example, the proposal unit presents multiple candidate antenna placements and evaluates the radio wave coverage rate and the presence or absence of interference for each placement. The proposal unit can use AI to present multiple candidate antenna placements and evaluate the radio wave coverage rate and the presence or absence of interference for each placement. The comparison unit compares the coverage rate with that of other companies. For example, the comparison unit identifies areas where other companies have low coverage rates and prioritizes placing antennas in those areas. The comparison unit uses AI to identify areas with low coverage by other companies and can prioritize antenna placement in those areas. The visualization unit visualizes the difficulty of dealing with building owners. For example, the visualization unit visualizes the difficulty of dealing with building owners and provides information to facilitate negotiations for antenna installation. The visualization unit can use AI to visualize the difficulty of dealing with building owners and provide information to facilitate negotiations for antenna installation. As a result, the assistant according to the embodiment can efficiently build a 5G network by simulating radio wave propagation, proposing the optimal antenna placement, comparing coverage with other companies, and visualizing the difficulty of dealing with building owners.

[0065] The simulation unit simulates radio wave propagation. For example, the simulation unit uses AI to achieve highly accurate radio wave simulations. Specifically, the simulation unit can perform simulations that take into account the influence of buildings and underground structures. For instance, the simulation unit simulates radio wave reflection and absorption considering the influence of buildings. Detailed data such as building materials, shapes, and placement are input, and the AI ​​analyzes these elements to predict the radio wave propagation path. The simulation unit can also simulate radio wave propagation considering underground structures. It analyzes radio wave attenuation and reflection in complex underground environments such as subways and underground parking lots in detail to derive the optimal antenna placement. The simulation unit uses AI to perform simulations that consider the influence of buildings and underground structures. The AI ​​uses machine learning algorithms to learn from past data and improve the accuracy of the simulation. For example, based on past radio wave propagation data, it predicts the behavior of radio waves under specific environmental conditions, improving the accuracy of the simulation results. Furthermore, the simulation unit can perform real-time simulations, enabling rapid responses to on-site conditions. As a result, the simulation unit can simulate radio wave propagation with high accuracy and efficiency, supporting optimal antenna placement.

[0066] The proposal unit proposes the optimal antenna placement based on the simulation results obtained by the simulation unit. For example, the proposal unit presents multiple antenna placement options and evaluates the radio wave coverage and interference for each placement. Specifically, the proposal unit can use AI to present multiple antenna placement options and evaluate the radio wave coverage and interference for each placement. The AI ​​analyzes the simulation results and executes an algorithm to derive the optimal antenna placement. For example, the AI ​​calculates a placement that maximizes radio wave coverage while minimizing interference. Furthermore, the proposal unit can propose the optimal placement considering the user's requirements and constraints. For example, it can provide flexible proposals tailored to user needs, such as prioritizing coverage in a specific area or requesting a cost-effective placement. Based on the simulation results, the proposal unit generates multiple placement scenarios and evaluates each scenario. The evaluation includes radio wave coverage, interference, installation costs, and installation difficulty. This allows the proposal unit to propose the optimal antenna placement to the user and support efficient network construction.

[0067] The comparison unit compares coverage rates with those of other companies. For example, the comparison unit identifies areas where other companies have low coverage and prioritizes antenna placement in those areas. Specifically, the comparison unit can use AI to identify areas where other companies have low coverage and prioritize antenna placement in those areas. The AI ​​identifies areas with low coverage by collecting and analyzing coverage rate data from other companies. For example, it plots the coverage rate data of other companies on a map to visually display areas with low coverage. Furthermore, the comparison unit can propose the optimal antenna placement for the identified areas. This allows the comparison unit to secure a competitive advantage over other companies and support the construction of an efficient network. The comparison unit regularly updates the coverage rate data of other companies, enabling comparisons based on the latest information. This allows it to always grasp the latest situation and respond quickly. Furthermore, the comparison unit can perform more accurate comparisons by considering not only the coverage rate data of other companies but also user feedback and usage data. This allows the comparison unit to accurately compare coverage rates with other companies and propose the optimal antenna placement.

[0068] The visualization unit visualizes the difficulty level of dealing with building owners. For example, the visualization unit visualizes the difficulty level of dealing with building owners and provides information to facilitate negotiations for antenna installation. Specifically, the visualization unit can use AI to visualize the difficulty level of dealing with building owners and provide information to facilitate negotiations for antenna installation. The AI ​​analyzes the building owner's past interaction history and current situation to evaluate the difficulty level of dealing with them. For example, it scores the difficulty level of dealing with building owners based on data on building owners who were cooperative with antenna installation in the past, or conversely, those who were reluctant to install it. Furthermore, the visualization unit displays the difficulty level of dealing with building owners on a map, making it easy to understand visually. This makes it easier for negotiators to decide which building owners to prioritize approaching. In addition to the difficulty level of dealing with building owners, the visualization unit can also visualize the advantages and disadvantages of antenna installation. For example, it can visually display the improvement in radio wave coverage rate due to antenna installation, installation costs, and installation difficulty, providing information to facilitate negotiations. This allows the visualization unit to efficiently negotiate with building owners and provide support for achieving optimal antenna placement.

[0069] The simulation unit can perform radio wave simulations that take into account the effects of buildings and underground structures. For example, the simulation unit can simulate radio wave reflection and absorption considering the effects of buildings. The simulation unit can also simulate radio wave propagation considering underground structures. The simulation unit can use AI to perform simulations that take into account the effects of buildings and underground structures. This makes it possible to perform more realistic radio wave simulations by taking into account the effects of buildings and underground structures.

[0070] The proposal function can present multiple antenna placement options and evaluate the radio wave coverage and interference for each placement. For example, the proposal function can present multiple antenna placement options and evaluate the radio wave coverage and interference for each placement. The proposal function can use AI to present multiple antenna placement options and evaluate the radio wave coverage and interference for each placement. This allows the system to propose the optimal antenna placement by presenting multiple options and evaluating radio wave coverage and interference.

[0071] The comparison unit can identify areas where competitors have low coverage and prioritize the placement of antennas in those areas. For example, the comparison unit can identify areas where competitors have low coverage and prioritize the placement of antennas in those areas. The comparison unit can use AI to identify areas where competitors have low coverage and prioritize the placement of antennas in those areas. This allows for increased competitiveness by identifying areas where competitors have low coverage and prioritizing antenna placement.

[0072] The visualization unit can visualize the difficulty level of dealing with building owners and provide information to facilitate negotiations for antenna installation. For example, the visualization unit can visualize the difficulty level of dealing with building owners and provide information to facilitate negotiations for antenna installation. The visualization unit can use AI to visualize the difficulty level of dealing with building owners and provide information to facilitate negotiations for antenna installation. This allows for smoother negotiations for antenna installation by visualizing the difficulty level of dealing with building owners.

[0073] The simulation unit can estimate the user's emotions and adjust the simulation parameters based on the estimated emotions. For example, if the user is stressed, the simulation unit can provide simple parameter settings to reduce the complexity of the simulation. If the user is relaxed, the simulation unit can also provide detailed parameter settings and suggest a customizable simulation. If the user is in a hurry, the simulation unit can also automatically optimize the parameters to obtain results quickly. This allows for the provision of optimal simulation results for the user by adjusting the simulation parameters according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI.

[0074] The simulation unit can predict radio wave propagation by taking into account changes in weather and time of day during the simulation. For example, the simulation unit performs simulations considering radio wave attenuation during rainy weather. The simulation unit can also perform simulations considering radio wave reflection and interference at night. The simulation unit can also predict radio wave propagation throughout the year by considering seasonal weather patterns. This makes it possible to predict radio wave propagation more realistically by taking into account changes in weather and time of day. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI.

[0075] The simulation unit can reflect the materials and heights of surrounding buildings in detail during the simulation. For example, the simulation unit simulates the reflection and absorption of radio waves considering the building materials (concrete, glass, metal, etc.). The simulation unit can also simulate the shielding effect of radio waves considering the height of buildings. The simulation unit can also simulate the propagation path of radio waves in detail considering the arrangement and density of buildings. This makes it possible to perform more accurate radio wave simulations by reflecting the materials and heights of surrounding buildings in detail. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI.

[0076] The simulation unit can estimate the user's emotions and adjust the display method of the simulation results based on the estimated user emotions. For example, if the user is nervous, the simulation unit can provide a simple and highly visible display method. If the user is relaxed, the simulation unit can also provide a display method that includes detailed information. If the user is in a hurry, the simulation unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of the simulation results according to the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI.

[0077] The simulation unit can predict radio wave propagation by considering traffic volume and people's movement patterns during simulations. For example, during periods of heavy traffic, the simulation unit considers radio wave shielding by vehicles during simulations. The simulation unit can also simulate radio wave propagation during congestion by considering people's movement patterns. The simulation unit can also predict radio wave propagation by considering the gathering of people during specific events. This makes it possible to predict radio wave propagation in a more realistic way by considering traffic volume and people's movement patterns. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI.

[0078] The simulation unit can improve accuracy by referring to past simulation data during the simulation. For example, the simulation unit can improve the accuracy of the current simulation based on past simulation data. The simulation unit can also compare past simulation results with actual measurement data and adjust the simulation model. The simulation unit can also analyze past simulation data, extract specific patterns, and reflect them in the simulation. In this way, the accuracy of the current simulation can be improved by referring to past simulation data. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without using AI.

[0079] The suggestion section can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion section may provide simple suggestions and minimize the amount of information. If the user is relaxed, the suggestion section may provide detailed suggestions and offer customizable options. If the user is in a hurry, the suggestion section may provide concise suggestions that are easy to understand quickly. This allows the suggestion section to provide the best possible suggestions for the user by adjusting the way suggestions are presented according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion section may be performed using AI or not using AI.

[0080] The proposal department can propose the optimal placement of antennas, taking into account the installation costs. For example, the proposal department can propose a cost-effective placement to minimize antenna installation costs. The proposal department can also propose the optimal placement by considering the balance between installation costs and coverage. The proposal department can also propose a placement that considers installation costs while also allowing for future expansion. This allows for the proposal of a cost-effective placement by considering antenna installation costs. Some or all of the above processing in the proposal department may be performed using AI, for example, or without using AI.

[0081] The proposal unit can determine the placement of antennas while considering the frequency of antenna maintenance. For example, the proposal unit may prioritize the placement of antennas that require less maintenance. The proposal unit may also place antennas in locations where maintenance is easy. The proposal unit may also propose a placement that maximizes coverage while considering the frequency of maintenance. In this way, by considering the frequency of antenna maintenance, it is possible to propose a placement that is easy to maintain. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0082] The suggestion section can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion section will prioritize displaying important suggestions. If the user is relaxed, the suggestion section may also prioritize displaying detailed suggestions. If the user is in a hurry, the suggestion section may also prioritize displaying suggestions that require immediate attention. This allows the suggestion section to provide the user with the most suitable suggestions by prioritizing suggestions according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion section may be performed using AI or not using AI.

[0083] The proposal unit can propose antenna placements that take into account the safety of the installation location. For example, the proposal unit may place the antenna in a highly safe location. The proposal unit can also evaluate safety by considering the surrounding environment of the installation location. The proposal unit can also propose placements that maximize coverage while ensuring safety. In this way, by considering the safety of the antenna installation location, it is possible to propose highly safe placements. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0084] The proposal team can evaluate the impact of antenna installation on the landscape and decide on the placement at the time of proposal. For example, the proposal team may place the antenna in a location with minimal impact on the landscape. The proposal team can also select an installation location that does not impair the landscape. The proposal team can also propose a placement that maximizes coverage while minimizing the impact on the landscape. In this way, by evaluating the impact of antenna installation on the landscape, it is possible to propose a placement that does not impair the landscape. Some or all of the above processing in the proposal team may be performed using AI, for example, or without using AI.

[0085] The comparison unit can estimate the user's emotions and adjust the display method of the comparison results based on the estimated user emotions. For example, if the user is nervous, the comparison unit can provide a simple and highly visible display method. If the user is relaxed, the comparison unit can also provide a display method that includes detailed information. If the user is in a hurry, the comparison unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of the comparison results according to the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI.

[0086] The comparison unit can evaluate coverage rates by considering the antenna installation plans of other companies during the comparison process. For example, the comparison unit evaluates coverage rates based on the antenna installation plans of other companies. The comparison unit can also compare the antenna installation plans of other companies with its own plan and propose the optimal coverage rate. The comparison unit can also evaluate coverage rates that enhance competitiveness while considering the antenna installation plans of other companies. This allows for the evaluation of more competitive coverage rates by considering the antenna installation plans of other companies. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI.

[0087] The comparison unit can evaluate coverage rates while considering the service quality of other companies during the comparison process. For example, the comparison unit evaluates coverage rates based on the service quality of other companies. The comparison unit can also compare the service quality of other companies with its own quality and propose an optimal coverage rate. The comparison unit can also evaluate a coverage rate that enhances competitiveness while considering the service quality of other companies. This allows for the evaluation of a more competitive coverage rate by considering the service quality of other companies. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI.

[0088] The comparison unit can estimate the user's emotions and determine the priority of comparison results based on the estimated emotions. For example, if the user is stressed, the comparison unit can prioritize displaying important comparison results. If the user is relaxed, the comparison unit can also prioritize displaying detailed comparison results. If the user is in a hurry, the comparison unit can also prioritize displaying comparison results that require immediate attention. This allows the system to provide the user with the most optimal comparison results by prioritizing them according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the comparison unit may be performed using AI, for example, or without AI.

[0089] The comparison unit can evaluate coverage rates by considering the pricing plans of other companies during the comparison process. For example, the comparison unit evaluates coverage rates based on the pricing plans of other companies. The comparison unit can also compare the pricing plans of other companies with its own plan and propose the optimal coverage rate. The comparison unit can also evaluate coverage rates that enhance competitiveness while considering the pricing plans of other companies. This allows for the evaluation of more competitive coverage rates by considering the pricing plans of other companies. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI.

[0090] The comparison unit can evaluate coverage rates while considering the customer satisfaction of other companies during the comparison process. For example, the comparison unit evaluates coverage rates based on the customer satisfaction of other companies. The comparison unit can also compare the customer satisfaction of other companies with that of the company itself and propose the optimal coverage rate. The comparison unit can also evaluate a coverage rate that enhances competitiveness while considering the customer satisfaction of other companies. This allows for the evaluation of a more competitive coverage rate by considering the customer satisfaction of other companies. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI.

[0091] The visualization unit can estimate the user's emotions and adjust the display method of the visualization based on the estimated user emotions. For example, if the user is nervous, the visualization unit can provide a simple and highly visible display method. If the user is relaxed, the visualization unit can also provide a display method that includes detailed information. If the user is in a hurry, the visualization unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of the visualization according to the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI.

[0092] The visualization unit can evaluate the difficulty level by referring to the building owner's past response history during visualization. For example, if the building owner has been cooperative with antenna installation in the past, the visualization unit will evaluate the difficulty level as low. If the building owner has opposed antenna installation in the past, the visualization unit may evaluate the difficulty level as high. The visualization unit can also analyze the building owner's past response history and predict the success rate of negotiations. This allows for an accurate evaluation of the difficulty level of negotiations by referring to the building owner's past response history. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI.

[0093] The visualization unit can evaluate the difficulty level by considering the building owner's attribute information during visualization. For example, the visualization unit evaluates the difficulty level of negotiations by considering the building owner's age and occupation. The visualization unit can also evaluate the difficulty level by combining the building owner's past interaction history with attribute information. The visualization unit can also propose the optimal negotiation method based on the building owner's attribute information. This allows for an accurate evaluation of the difficulty level of negotiations by considering the building owner's attribute information. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI.

[0094] The visualization unit can estimate the user's emotions and determine the priority of visualizations based on the estimated emotions. For example, if the user is stressed, the visualization unit can prioritize displaying important information. If the user is relaxed, the visualization unit can also prioritize displaying detailed information. If the user is in a hurry, the visualization unit can also prioritize displaying information that requires immediate attention. In this way, by determining the priority of visualizations according to the user's emotions, the system can provide the user with the most appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI.

[0095] The visualization unit can evaluate the difficulty level by considering the building owner's geographical location information during visualization. For example, the visualization unit may rate the difficulty level higher if the building owner is located in an urban area. The visualization unit may also rate the difficulty level lower if the building owner is located in a suburban area. The visualization unit can also evaluate the difficulty level of negotiations by considering the building owner's location and surrounding environment. This allows for an accurate evaluation of the difficulty level of negotiations by considering the building owner's geographical location information. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI.

[0096] The visualization unit can evaluate the difficulty level by considering the building owner's financial situation during visualization. For example, if the building owner's financial situation is good, the visualization unit will evaluate the difficulty level as low. If the building owner's financial situation is unstable, the visualization unit may evaluate the difficulty level as high. The visualization unit can also evaluate the difficulty level by combining the building owner's financial situation with past interaction history. This allows for an accurate evaluation of the difficulty level of negotiations by considering the building owner's financial situation. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The simulation unit can take into account the surrounding natural environment (e.g., trees and bodies of water) when simulating radio wave propagation. For example, the simulation unit can simulate the attenuation of radio waves due to tree leaves and branches. The simulation unit can also simulate radio wave propagation considering reflection and absorption by bodies of water. The simulation unit can also predict radio wave propagation throughout the year by considering seasonal changes in the natural environment. This makes it possible to perform more realistic radio wave simulations by taking the surrounding natural environment into account.

[0099] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion function will refrain from making suggestions and will make suggestions when the user is relaxed. If the user is in a hurry, the suggestion function can make suggestions quickly, and if the user has time, it can make detailed suggestions. The suggestion function can also adjust the frequency of suggestions according to the user's emotions. By adjusting the timing of suggestions according to the user's emotions, it can provide the most suitable suggestions for the user.

[0100] The comparison unit can consider the performance and technical specifications of other companies' antennas when evaluating coverage rates in consideration of other companies' antenna installation plans. For example, the comparison unit can evaluate coverage rates based on the output and sensitivity of other companies' antennas. The comparison unit can also compare the technical specifications of other companies' antennas with those of the company's own company and propose the optimal coverage rate. The comparison unit can also evaluate coverage rates that enhance competitiveness while considering the performance of other companies' antennas. This allows for the evaluation of more competitive coverage rates by considering the performance and technical specifications of other companies' antennas.

[0101] The visualization unit can evaluate the difficulty of dealing with a building owner by referring to the building owner's past interaction history. For example, if the building owner has been cooperative with antenna installation in the past, the visualization unit will rate the difficulty lower. If the building owner has opposed antenna installation in the past, the visualization unit can rate the difficulty higher. The visualization unit can also analyze the building owner's past interaction history and predict the success rate of negotiations. This allows for an accurate assessment of the difficulty of negotiations by referring to the building owner's past interaction history.

[0102] The simulation unit can estimate the user's emotions and adjust the simulation parameters based on those emotions. For example, if the user is stressed, the simulation unit can provide simple parameter settings to reduce the complexity of the simulation. If the user is relaxed, the simulation unit can also provide detailed parameter settings and suggest a customizable simulation. If the user is in a hurry, the simulation unit can automatically optimize the parameters to obtain results quickly. This allows the system to provide the best possible simulation results for the user by adjusting the simulation parameters according to their emotions.

[0103] The proposal department can propose the optimal placement of antennas, taking into account the installation costs. For example, the proposal department can propose a cost-effective placement to minimize installation costs. The proposal department can also propose an optimal placement that balances installation costs with coverage. The proposal department can also propose a placement that considers installation costs while also allowing for future expansion. In this way, a cost-effective placement can be proposed by taking antenna installation costs into consideration.

[0104] The comparison unit can estimate the user's emotions and adjust the display method of the comparison results based on the estimated emotions. For example, if the user is nervous, the comparison unit can provide a simple and highly visible display method. If the user is relaxed, the comparison unit can also provide a display method that includes detailed information. If the user is in a hurry, the comparison unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of the comparison results according to the user's emotions, the optimal display method can be provided to the user.

[0105] The visualization unit can evaluate the difficulty level by considering the building owner's attribute information during visualization. For example, the visualization unit evaluates the difficulty level of negotiations by considering the building owner's age and occupation. The visualization unit can also evaluate the difficulty level by combining the building owner's past interaction history with attribute information. Based on the building owner's attribute information, the visualization unit can also suggest the optimal negotiation method. In this way, the difficulty level of negotiations can be accurately evaluated by considering the building owner's attribute information.

[0106] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function can offer simple suggestions and minimize the amount of information. If the user is relaxed, the suggestion function can offer detailed suggestions and even suggest customizable options. If the user is in a hurry, the suggestion function can offer concise suggestions that are easy to understand quickly. By adjusting the way suggestions are presented according to the user's emotions, the system can provide the most suitable suggestions for the user.

[0107] The simulation unit can predict radio wave propagation by taking into account changes in weather and time of day during simulations. For example, the simulation unit will perform simulations considering radio wave attenuation during rainy weather. The simulation unit can also perform simulations considering radio wave reflection and interference at night. The simulation unit can also predict radio wave propagation throughout the year by considering seasonal weather patterns. This makes it possible to make more realistic predictions of radio wave propagation by taking into account changes in weather and time of day.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The simulation unit simulates radio wave propagation. The simulation unit uses AI to achieve high-precision radio wave simulations, taking into account the effects of buildings and underground structures. For example, it simulates radio wave reflection and absorption considering the effects of buildings, and simulates radio wave propagation considering underground structures. Step 2: The proposal unit proposes the optimal antenna placement based on the simulation results obtained by the simulation unit. The proposal unit presents multiple candidate antenna placements and evaluates the radio wave coverage and interference status for each placement. Using AI, it is possible to present multiple candidate antenna placements and evaluate the radio wave coverage and interference status for each placement. Step 3: The comparison unit compares coverage rates with other companies. The comparison unit identifies areas where other companies have low coverage and prioritizes antenna placement in those areas. Using AI, it is possible to identify areas where other companies have low coverage and prioritize antenna placement in those areas. Step 4: The visualization unit visualizes the difficulty level of dealing with the building owner. The visualization unit visualizes the difficulty level of dealing with the building owner and provides information to facilitate negotiations for antenna installation. By using AI, it is possible to visualize the difficulty level of dealing with the building owner and provide information to facilitate negotiations for antenna installation.

[0110] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] Each of the multiple elements described above, including the simulation unit, proposal unit, comparison unit, and visualization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the simulation unit is implemented by the processor 46 of the smart device 14 and the processor 28 of the data processing unit 12, and performs high-precision radio wave simulation using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, and proposes the optimal antenna placement based on the simulation results. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12, and compares coverage rates with those of other companies. The visualization unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and visualizes the difficulty of dealing with the building owner's needs. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 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.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the simulation unit, proposal unit, comparison unit, and visualization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the simulation unit is implemented by the processor 46 of the smart glasses 214 and the processor 28 of the data processing unit 12, and performs high-precision radio wave simulation using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, and proposes the optimal antenna placement based on the simulation results. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12, and compares coverage rates with those of other companies. The visualization unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and visualizes the difficulty of dealing with the building owner. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the simulation unit, proposal unit, comparison unit, and visualization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the simulation unit is implemented by the processor 46 of the headset terminal 314 and the processor 28 of the data processing unit 12, and performs high-precision radio wave simulation using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, and proposes the optimal antenna placement based on the simulation results. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12, and compares coverage rates with those of other companies. The visualization unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and visualizes the difficulty of dealing with the building owner. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the simulation unit, proposal unit, comparison unit, and visualization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the simulation unit is implemented by the processor 46 of the robot 414 and the processor 28 of the data processing unit 12, and performs high-precision radio wave simulation using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, and proposes the optimal antenna placement based on the simulation results. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12, and compares coverage rates with those of other companies. The visualization unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12, and visualizes the difficulty of dealing with the building owner. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0163] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A simulation unit that simulates the propagation of radio waves, A proposal unit proposes the optimal antenna arrangement based on the simulation results obtained by the simulation unit, A comparison section that compares coverage rates with other companies, It includes a visualization unit that visualizes the difficulty level of dealing with the building owner. A system characterized by the following features. (Note 2) The aforementioned simulation unit, Conduct radio wave simulations that take into account the influence of buildings and underground structures. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We present multiple antenna placement options and evaluate the radio wave coverage and interference levels for each placement. The system described in Appendix 1, characterized by the features described herein. (Note 4) The comparison unit is, Identify areas where other companies have low coverage and prioritize placing antennas in those areas. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned visualization unit, This service visualizes the difficulty level of dealing with building owners and provides information to facilitate negotiations regarding antenna installation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation parameters based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned simulation unit, During the simulation, the propagation of radio waves is predicted, taking into account changes in weather and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned simulation unit, During the simulation, the materials and heights of surrounding buildings are reflected in detail. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned simulation unit, It estimates the user's emotions and adjusts how the simulation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned simulation unit, During the simulation, the propagation of radio waves is predicted by considering traffic volume and people's movement patterns. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned simulation unit, During simulations, past simulation data is referenced to improve accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, we will suggest the optimal placement, taking into account the cost of antenna installation. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, the placement of the antennas will be determined considering the frequency of antenna maintenance. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, we will consider the safety of the antenna installation location when suggesting placement options. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, The impact of antenna installation on the landscape will be evaluated and the placement will be determined during the proposal stage. The system described in Appendix 1, characterized by the features described herein. (Note 18) The comparison unit is, It estimates the user's emotions and adjusts how comparison results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The comparison unit is, When making comparisons, evaluate coverage rates by taking into account the antenna installation plans of other companies. The system described in Appendix 1, characterized by the features described herein. (Note 20) The comparison unit is, When making comparisons, evaluate coverage while taking into account the service quality of other companies. The system described in Appendix 1, characterized by the features described herein. (Note 21) The comparison unit is, It estimates the user's emotions and determines the priority of the comparison results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The comparison unit is, When making comparisons, evaluate coverage by taking into account the pricing plans of other companies. The system described in Appendix 1, characterized by the features described herein. (Note 23) The comparison unit is, When making comparisons, we evaluate coverage rate while taking into account customer satisfaction levels of other companies. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned visualization unit, It estimates the user's emotions and adjusts the display method of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned visualization unit, When visualizing the problem, the difficulty level is assessed by referring to the building owner's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned visualization unit, When visualizing the difficulty level, the difficulty is evaluated by considering the attribute information of the building owner. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned visualization unit, It estimates the user's emotions and determines the visualization priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned visualization unit, When visualizing the difficulty level, the building owner's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned visualization unit, When visualizing the difficulty level, the building owner's financial situation is taken into consideration. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A simulation unit that simulates the propagation of radio waves, A proposal unit proposes the optimal antenna arrangement based on the simulation results obtained by the simulation unit, A comparison section that compares coverage rates with other companies, It includes a visualization unit that visualizes the difficulty level of dealing with the building owner. A system characterized by the following features.

2. The aforementioned simulation unit, Conduct radio wave simulations that take into account the influence of buildings and underground structures. The system according to feature 1.

3. The aforementioned proposal section is, We present multiple antenna placement options and evaluate the radio wave coverage and interference levels for each placement. The system according to feature 1.

4. The comparison unit is, Identify areas where other companies have low coverage and prioritize placing antennas in those areas. The system according to feature 1.

5. The aforementioned visualization unit, This service visualizes the difficulty level of dealing with building owners and provides information to facilitate negotiations regarding antenna installation. The system according to feature 1.

6. The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation parameters based on the estimated user emotions. The system according to feature 1.

7. The aforementioned simulation unit, During the simulation, the propagation of radio waves is predicted, taking into account changes in weather and time of day. The system according to feature 1.

8. The aforementioned simulation unit, During the simulation, the materials and heights of surrounding buildings are reflected in detail. The system according to feature 1.

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