system

Abstract

The system according to this embodiment aims to simulate the optimal antenna placement in complex urban environments and support the construction of an efficient 5G network. [Solution] The system according to the embodiment comprises an analysis unit, a simulation unit, a monitoring unit, a specification unit, and a visualization unit. The analysis unit analyzes the density of people in real time. The simulation unit simulates the optimal antenna placement based on the information obtained by the analysis unit. The monitoring unit monitors the communication status based on the antenna placement proposed by the simulation unit. The specification unit identifies areas requiring priority attention and compares coverage rates with other companies based on the information obtained by the monitoring unit. The visualization unit visualizes the difficulty of negotiating with building owners based on the information obtained by the specification unit.

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 character of the chatbot, 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] [[ID=�5]]In the conventional technology, it is difficult to efficiently perform optimal antenna placement in a complex urban environment, and there is room for improvement.

[0005] The system according to the embodiment aims to simulate optimal antenna placement in a complex urban environment and support the construction of an efficient 5G network.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a simulation unit, a monitoring unit, a designation unit, and a visualization unit. The analysis unit analyzes the density of people in real time. The simulation unit simulates the optimal antenna placement based on the information obtained by the analysis unit. The monitoring unit monitors the communication status based on the antenna placement proposed by the simulation unit. The designation unit identifies areas requiring priority attention and compares coverage rates with other companies based on the information obtained by the monitoring unit. The visualization unit visualizes the difficulty of negotiating with building owners based on the information obtained by the designation unit. [Effects of the Invention]

[0007] The system according to this embodiment can simulate the optimal antenna placement in a complex urban environment and support the construction of an efficient 5G network. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memories (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 1 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 AI agent system according to an embodiment of the present invention is a system that simulates radio waves in a mobile phone network and supports the optimal placement of 5G antennas. This AI agent system analyzes the density of people, between buildings, underground, and in areas where antenna clusters are densely located in real time, and simulates and proposes antenna placements to efficiently deliver radio waves in complex urban environments. This enables the construction of an efficient 5G network and provides users with an optimal communication experience. The AI ​​agent system also monitors communication conditions, compares coverage rates with other companies, identifies areas requiring priority attention, and visualizes the difficulty of negotiations with building owners. For example, the AI ​​agent system analyzes the density of people in real time. In this process, the AI ​​agent system analyzes the propagation conditions of radio waves in complex urban environments, such as between buildings, underground, and in areas where antenna clusters are densely located. For example, the AI ​​agent system grasps the radio wave conditions in areas susceptible to building interference and underground in real time and proposes the optimal antenna placement. Next, the AI ​​agent system simulates antenna placements to efficiently deliver radio waves in complex urban environments. Based on the simulation results, the AI ​​agent system proposes the optimal antenna placement. For example, the AI ​​agent system proposes placing antennas between buildings to cover areas where radio waves are difficult to reach. Furthermore, the AI ​​agent system monitors communication conditions. It compares coverage rates with other companies and identifies areas requiring priority attention. For example, it can identify areas with low coverage by other companies and propose antenna placement in those areas. The AI ​​agent system also visualizes the difficulty of negotiations with building owners. For instance, it can identify areas where negotiations with building owners are difficult and propose negotiation strategies for antenna placement in those areas. This enables the construction of an efficient 5G network, providing users with an optimal communication experience. For example, the AI ​​agent system analyzes radio wave conditions in real time and proposes optimal antenna placement, providing users with a high-quality communication experience.Furthermore, the AI ​​agent system enhances the competitiveness of telecommunications carriers by comparing coverage rates with other companies and identifying areas requiring priority support. This allows the AI ​​agent system to support the efficient construction of 5G networks and provide users with an optimal communication experience.

[0029] The AI ​​agent system according to this embodiment comprises an analysis unit, a simulation unit, a monitoring unit, a identification unit, and a visualization unit. The analysis unit analyzes the density of people in real time. For example, the analysis unit analyzes areas between buildings, underground, and areas where antenna clusters are densely concentrated in real time. The analysis unit can use AI to analyze in detail areas susceptible to building interference and underground radio wave conditions. For example, the analysis unit can identify areas susceptible to building interference and grasp the radio wave conditions in those areas in real time. The analysis unit can also grasp underground radio wave conditions in real time and propose the optimal antenna placement. The simulation unit simulates the optimal antenna placement based on the information obtained by the analysis unit. The simulation unit can use AI to simulate the propagation of radio waves in complex urban environments. For example, the simulation unit can propose covering areas where radio waves are difficult to reach by placing antennas between buildings. The simulation unit can also simulate underground radio wave conditions and propose the optimal antenna placement. The monitoring unit monitors the communication status based on the antenna placement proposed by the simulation unit. The Monitoring Department can use AI to compare coverage rates with other companies and identify areas requiring priority attention. For example, the Monitoring Department can identify areas with low coverage rates by other companies and propose the placement of antennas in those areas. The Monitoring Department can also identify areas requiring priority attention and propose the placement of antennas in those areas. The Identification Department compares coverage rates with other companies and identifies areas requiring priority attention based on the information obtained by the Monitoring Department. The Identification Department can use AI to identify areas with low coverage rates by other companies and propose the placement of antennas in those areas. For example, the Identification Department can identify areas with low coverage rates by other companies and propose the placement of antennas in those areas. The Identification Department can also identify areas requiring priority attention and propose the placement of antennas in those areas. The Visualization Department visualizes the difficulty of negotiating with building owners based on the information obtained by the Identification Department.The visualization unit can use AI to identify areas where negotiations with building owners are difficult and propose negotiation strategies for placing antennas in those areas. For example, the visualization unit can identify areas where negotiations with building owners are difficult and propose negotiation strategies for placing antennas in those areas. Furthermore, the visualization unit can identify areas where negotiations with building owners are difficult and propose negotiation strategies for placing antennas in those areas. As a result, the AI ​​agent system according to this embodiment can support the construction of an efficient 5G network and provide users with an optimal communication experience.

[0030] The analysis department analyzes the density of people in real time. Specifically, it analyzes areas between buildings, underground, and areas with a high concentration of antennas in real time. The analysis department uses AI to analyze in detail areas susceptible to building interference and underground radio wave conditions. For example, it can identify areas susceptible to building interference and understand the radio wave conditions in those areas in real time. The AI ​​uses image recognition and data analysis technologies to identify areas susceptible to building interference. Specifically, it simulates radio wave propagation based on information such as building height, location, and materials, and identifies areas where radio waves are difficult to reach. It can also understand underground radio wave conditions in real time and propose the optimal antenna placement. To understand underground radio wave conditions, the AI ​​analyzes information such as underground structure, materials, and existing antenna placement, and simulates radio wave propagation. This allows for a detailed understanding of underground radio wave conditions and the proposal of the optimal antenna placement. Furthermore, the analysis department monitors changes in radio wave conditions based on data collected in real time and can revise antenna placement as needed. This helps maintain optimal radio wave conditions at all times and supports the construction of an efficient 5G network.

[0031] The simulation unit simulates the optimal antenna placement based on information obtained by the analysis unit. Specifically, it can use AI to simulate radio wave propagation in complex urban environments. For example, it can propose covering areas with poor radio wave reception by placing antennas between buildings. The AI ​​simulates radio wave propagation based on information such as building height, location, and materials, and proposes the optimal antenna placement. It can also simulate underground radio wave conditions and propose the optimal antenna placement. To simulate underground radio wave conditions, the AI ​​analyzes information such as underground structure, materials, and existing antenna placements, and simulates radio wave propagation. This allows for a detailed understanding of underground radio wave conditions and the proposal of the optimal antenna placement. Furthermore, the simulation unit monitors changes in radio wave conditions based on data collected in real time and can revise antenna placement as needed. This helps maintain optimal radio wave conditions at all times and supports the construction of an efficient 5G network.

[0032] The monitoring unit monitors communication status based on the antenna placement proposed by the simulation unit. Specifically, it can use AI to compare coverage rates with other companies and identify areas requiring priority attention. For example, it can identify areas with low coverage rates from other companies and propose placing antennas in those areas. The AI ​​analyzes coverage rate data from other companies and compares it with its own to identify areas with low coverage. It can also identify areas requiring priority attention and propose placing antennas in those areas. To identify areas requiring priority attention, the AI ​​analyzes communication traffic data and user feedback data to identify areas with degraded communication quality and areas with high communication demand. This allows the monitoring unit to monitor communication status in real time and revise antenna placement as needed. Furthermore, by comparing coverage rates with other companies and identifying areas requiring priority attention, the monitoring unit can support the construction of an efficient 5G network.

[0033] The Specialized Unit identifies areas requiring priority attention and compares coverage rates with other companies based on information obtained by the Monitoring Unit. Specifically, it can use AI to identify areas with low coverage rates from other companies and propose antenna placement in those areas. The AI ​​analyzes coverage rate data from other companies and compares it with its own to identify areas with low coverage. It can also identify areas requiring priority attention and propose antenna placement in those areas. To identify areas requiring priority attention, the AI ​​analyzes communication traffic data and user feedback data to identify areas with degraded communication quality and areas with high communication demand. This allows the Specialized Unit to monitor communication conditions in real time and revise antenna placement as needed. Furthermore, by comparing coverage rates with other companies and identifying areas requiring priority attention, the Specialized Unit can support the construction of an efficient 5G network.

[0034] The visualization unit visualizes the difficulty of negotiating with building owners based on information obtained by the identification unit. Specifically, it can use AI to identify areas where negotiations with building owners are difficult and propose negotiation strategies for placing antennas in those areas. The AI ​​analyzes information such as the building owner's past negotiation history, building ownership status, and regional characteristics to identify areas where negotiations are difficult. It also proposes the optimal negotiation strategy for these difficult areas. For example, for areas where negotiations with building owners are difficult, it proposes the timing and method of negotiation, as well as the necessary documents. This allows the visualization unit to efficiently advance negotiations with building owners and achieve optimal antenna placement. Furthermore, the visualization unit visualizes the progress of negotiations in real time and can revise the negotiation strategy as needed. This allows for the maintenance of an optimal negotiation strategy at all times and supports the construction of an efficient 5G network.

[0035] The analysis unit can analyze areas between buildings, underground, and areas with a high concentration of antennas in real time. For example, the analysis unit can grasp the radio wave conditions between buildings and underground in real time. The analysis unit can use AI to identify areas susceptible to building interference and analyze the radio wave conditions in those areas in detail. For example, the analysis unit can identify areas susceptible to building interference and grasp the radio wave conditions in those areas in real time. In addition, the analysis unit can grasp the underground radio wave conditions in real time and propose the optimal antenna placement. As a result, by analyzing areas between buildings, underground, and areas with a high concentration of antennas in real time, it is possible to grasp the radio wave propagation conditions in complex urban environments in detail. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to analyze radio wave propagation conditions in order to grasp the radio wave conditions between buildings and underground in real time.

[0036] The simulation unit can simulate the propagation of radio waves in complex urban environments. For example, the simulation unit can propose covering areas where radio waves are difficult to reach by placing antennas between buildings. The simulation unit can use AI to simulate the propagation of radio waves in complex urban environments in detail. For example, the simulation unit can identify areas that are susceptible to the influence of buildings and simulate the radio wave conditions in those areas. The simulation unit can also simulate underground radio wave conditions and propose the optimal antenna placement. In this way, by simulating the propagation of radio waves in complex urban environments, the optimal antenna placement can be proposed. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can use AI to simulate the propagation of radio waves in order to propose covering areas where radio waves are difficult to reach by placing antennas between buildings.

[0037] The monitoring unit can compare coverage rates with other companies. For example, the monitoring unit can identify areas where other companies have low coverage and propose placing antennas in those areas. The monitoring unit can use AI to perform detailed comparisons of coverage rates with other companies. For example, the monitoring unit can identify areas where other companies have low coverage and propose placing antennas in those areas. The monitoring unit can also identify areas where other companies have low coverage and propose placing antennas in those areas. This makes it possible to build a competitive network by comparing coverage rates with other companies. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to compare coverage rates with other companies in order to identify areas where other companies have low coverage and propose placing antennas in those areas.

[0038] The identification unit can identify areas requiring priority attention. For example, the identification unit can identify areas with high communication traffic or areas with important facilities and propose the placement of antennas in those areas. The identification unit can use AI to identify areas requiring priority attention in detail. For example, the identification unit can identify areas with high communication traffic and propose the placement of antennas in those areas. It can also identify areas with important facilities and propose the placement of antennas in those areas. By identifying areas requiring priority attention, efficient network operation becomes possible. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can use AI to identify areas requiring priority attention in order to identify areas with high communication traffic or areas with important facilities and propose the placement of antennas in those areas.

[0039] The visualization unit can visualize the difficulty of negotiating with building owners. For example, the visualization unit can identify areas where negotiations with building owners are difficult and propose negotiation strategies for placing antennas in those areas. The visualization unit can use AI to visualize the difficulty of negotiating with building owners in detail. For example, the visualization unit can identify areas where negotiations with building owners are difficult and propose negotiation strategies for placing antennas in those areas. The visualization unit can also identify areas where negotiations with building owners are difficult and propose negotiation strategies for placing antennas in those areas. By visualizing the difficulty of negotiating with building owners, it becomes easier to formulate negotiation strategies. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can use AI to visualize the difficulty of negotiating with building owners in order to identify areas where negotiations with building owners are difficult and propose negotiation strategies for placing antennas in those areas.

[0040] The analysis unit can predict current congestion levels by referring to past congestion data. For example, the analysis unit can refer to past event data to predict congestion levels when similar events are held. The analysis unit can use AI to analyze past congestion data in detail and predict current congestion levels. For example, the analysis unit can predict current congestion levels based on past congestion data for each time period. The analysis unit can also refer to past seasonal congestion data to predict congestion levels for the current season. This allows for a more accurate prediction of current congestion levels by referring to past data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to analyze past data in order to predict current congestion levels by referring to past congestion data.

[0041] The analysis unit can predict changes in congestion based on specific events or time periods. For example, the analysis unit can predict changes in congestion based on past data when large-scale events are held. The analysis unit can use AI to predict changes in congestion in detail based on specific events or time periods. For example, the analysis unit can predict changes in congestion during commuting hours and propose the optimal antenna placement. The analysis unit can also predict changes in congestion during seasonal events (e.g., fireworks displays). By predicting changes in congestion based on events and time periods, it can propose appropriate antenna placement. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to analyze past data in order to predict changes in congestion based on specific events or time periods.

[0042] The analysis unit can analyze density conditions by considering geographical features and weather conditions. For example, the analysis unit can analyze density conditions by considering topographical features. The analysis unit can use AI to analyze density conditions by considering geographical features and weather conditions in detail. For example, the analysis unit can analyze density conditions by considering weather conditions (e.g., rainy, sunny). The analysis unit can also analyze density conditions by considering geographical obstacles (e.g., rivers, mountains). This makes it possible to analyze density conditions more accurately by considering geographical features and weather conditions. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to analyze geographical and weather data in order to analyze density conditions by considering geographical features and weather conditions.

[0043] The analysis unit can grasp the density situation in real time by referring to social media data. For example, the analysis unit can analyze social media posts to grasp the density situation in real time. The analysis unit can use AI to analyze social media data in detail and grasp the density situation in real time. For example, the analysis unit can identify dense areas based on social media location information. The analysis unit can also analyze social media trends to predict changes in the density situation. As a result, the density situation can be grasped in real time by referring to social media data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to analyze social media data in order to grasp the density situation in real time by referring to social media data.

[0044] The simulation unit can predict current radio wave conditions by referring to past radio wave propagation data. For example, the simulation unit predicts current radio wave conditions based on past radio wave propagation data. The simulation unit can use AI to analyze past radio wave propagation data in detail and predict current radio wave conditions. For example, the simulation unit can predict current radio wave conditions based on past radio wave propagation data for each time period. Furthermore, the simulation unit can predict radio wave conditions for the current season based on past radio wave propagation data for each season. This allows for a more accurate prediction of current radio wave conditions by referring to past data. Some or all of the above-described processes in the simulation unit may be performed using AI or not. For example, the simulation unit can use AI to analyze past data in order to predict current radio wave conditions by referring to past radio wave propagation data.

[0045] The simulation unit can predict changes in radio wave conditions based on specific events or time periods. For example, the simulation unit can predict changes in radio wave conditions based on past data when a large-scale event is held. The simulation unit can use AI to predict changes in radio wave conditions in detail based on specific events or time periods. For example, the simulation unit can predict changes in radio wave conditions during commuting hours and propose the optimal antenna placement. The simulation unit can also predict changes in radio wave conditions during seasonal events (e.g., fireworks displays). By predicting changes in radio wave conditions based on events and time periods, it can propose appropriate antenna placement. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can use AI to analyze past data in order to predict changes in radio wave conditions based on specific events or time periods.

[0046] The simulation unit can simulate radio wave propagation conditions by considering geographical features and weather conditions. For example, the simulation unit can simulate radio wave propagation conditions by considering the topography. The simulation unit can use AI to simulate radio wave propagation conditions by considering geographical features and weather conditions in detail. For example, the simulation unit can simulate radio wave propagation conditions by considering weather conditions (e.g., rainy, sunny). The simulation unit can also simulate radio wave propagation conditions by considering geographical obstacles (e.g., rivers, mountains). This makes it possible to simulate radio wave propagation conditions more accurately by considering geographical features and weather conditions. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can use AI to analyze geographical and weather data in order to simulate radio wave propagation conditions by considering geographical features and weather conditions.

[0047] The simulation unit can propose the optimal antenna placement by referring to other companies' antenna placement data. For example, the simulation unit can propose the optimal antenna placement based on other companies' antenna placement data. The simulation unit can use AI to analyze other companies' antenna placement data in detail and propose the optimal placement. For example, the simulation unit can refer to other companies' coverage rates and propose placing antennas in areas with low coverage rates. The simulation unit can also analyze other companies' antenna placement data and propose the most efficient placement. In this way, the optimal antenna placement can be proposed by referring to other companies' antenna placement data. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can use AI to analyze other companies' data in order to propose the optimal placement by referring to other companies' antenna placement data.

[0048] The monitoring unit can predict the current communication status by referring to past communication data. For example, the monitoring unit predicts the current communication status based on past communication data. The monitoring unit can use AI to analyze past communication data in detail and predict the current communication status. For example, the monitoring unit can predict the communication status for the current time period based on past communication data for each time period. In addition, the monitoring unit can predict the communication status for the current season based on past communication data for each season. This allows for a more accurate prediction of the current communication status by referring to past data. Some or all of the above-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to analyze past data in order to predict the current communication status by referring to past communication data.

[0049] The monitoring unit can predict changes in communication status based on specific events or time periods. For example, the monitoring unit can predict changes in communication status based on past data when a large-scale event is held. The monitoring unit can use AI to predict changes in communication status in detail based on specific events or time periods. For example, the monitoring unit can predict changes in communication status during commuting hours and propose the optimal antenna placement. The monitoring unit can also predict changes in communication status during seasonal events (e.g., fireworks displays). By predicting changes in communication status based on events and time periods, it can propose appropriate antenna placement. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to analyze past data in order to predict changes in communication status based on specific events or time periods.

[0050] The monitoring unit can monitor communication status while considering geographical features and weather conditions. For example, the monitoring unit can monitor communication status while considering the topography. The monitoring unit can use AI to monitor communication status while considering geographical features and weather conditions in detail. For example, the monitoring unit can monitor communication status while considering weather conditions (e.g., rainy, sunny). The monitoring unit can also monitor communication status while considering geographical obstacles (e.g., rivers, mountains). This makes it possible to monitor communication status more accurately by considering geographical features and weather conditions. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to analyze geographical data and weather data in order to monitor communication status while considering geographical features and weather conditions.

[0051] The monitoring unit can compare coverage rates by referring to the communication data of other companies. For example, the monitoring unit can compare coverage rates based on the communication data of other companies. The monitoring unit can use AI to analyze the communication data of other companies in detail and compare coverage rates. For example, the monitoring unit can refer to the coverage rates of other companies and identify areas with low coverage rates. The monitoring unit can also analyze the communication data of other companies and propose the most efficient coverage rate. This makes it possible to compare coverage rates by referring to the communication data of other companies. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to analyze the data of other companies in order to compare coverage rates by referring to their communication data.

[0052] The identification unit can predict the current priority service area by referring to past priority service data. For example, the identification unit predicts the current priority service area based on past priority service data. The identification unit can use AI to analyze past priority service data in detail and predict the current priority service area. For example, the identification unit can predict the priority service area for the current time period based on past priority service data for each time period. In addition, the identification unit can predict the priority service area for the current season based on past priority service data for each season. This allows for a more accurate prediction of the current priority service area by referring to past data. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can use AI to analyze past data in order to predict the current priority service area by referring to past priority service data.

[0053] The specific unit can predict changes in priority service areas based on specific events or time periods. For example, the specific unit predicts changes in priority service areas based on past data when large-scale events are held. The specific unit can use AI to predict changes in priority service areas in detail based on specific events or time periods. For example, the specific unit can predict changes in priority service areas during commuting hours and propose the optimal antenna placement. The specific unit can also predict changes in priority service areas during seasonal events (e.g., fireworks displays). By predicting changes in priority service areas based on events and time periods, it can propose appropriate antenna placement. Some or all of the above processing in the specific unit may be performed using AI or not. For example, the specific unit can use AI to analyze past data in order to predict changes in priority service areas based on specific events or time periods.

[0054] The identification unit can identify priority response areas by considering geographical features and weather conditions. For example, the identification unit can identify priority response areas by considering the topography. The identification unit can use AI to identify priority response areas by considering geographical features and weather conditions in detail. For example, the identification unit can identify priority response areas by considering weather conditions (e.g., rainy, sunny). The identification unit can also identify priority response areas by considering geographical obstacles (e.g., rivers, mountains). This makes it possible to identify priority response areas more accurately by considering geographical features and weather conditions. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can use AI to analyze geographical and weather data in order to identify priority response areas by considering geographical features and weather conditions.

[0055] The identification unit can identify the optimal area by referring to other companies' priority response data. For example, the identification unit can identify the optimal area based on other companies' priority response data. The identification unit can use AI to analyze other companies' priority response data in detail and identify the optimal area. For example, the identification unit can refer to other companies' coverage rates and identify areas with low coverage rates as priority response areas. The identification unit can also analyze other companies' priority response data and identify the most efficient area. This makes it possible to identify the optimal area by referring to other companies' priority response data. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can use AI to analyze other companies' data in order to identify the optimal area by referring to other companies' priority response data.

[0056] The visualization unit can predict the current negotiation difficulty by referring to past negotiation data. For example, the visualization unit predicts the current negotiation difficulty based on past negotiation data. The visualization unit can use AI to analyze past negotiation data in detail and predict the current negotiation difficulty. For example, the visualization unit can predict the negotiation difficulty for the current time period based on past negotiation data for each time period. In addition, the visualization unit can predict the negotiation difficulty for the current season based on past negotiation data for each season. This allows for a more accurate prediction of the current negotiation difficulty by referring to past data. Some or all of the above-described processes in the visualization unit may be performed using AI or not. For example, the visualization unit can use AI to analyze past data in order to predict the current negotiation difficulty by referring to past negotiation data.

[0057] The visualization unit can predict changes in negotiation difficulty based on specific events or time periods. For example, the visualization unit can predict changes in negotiation difficulty based on past data when large-scale events are held. The visualization unit can use AI to predict changes in negotiation difficulty in detail based on specific events or time periods. For example, the visualization unit can predict changes in negotiation difficulty during commuting hours and propose the optimal negotiation strategy. The visualization unit can also predict changes in negotiation difficulty during seasonal events (e.g., fireworks displays). This allows for the proposal of appropriate negotiation strategies by predicting changes in negotiation difficulty based on events and time periods. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can use AI to analyze past data in order to predict changes in negotiation difficulty based on specific events or time periods.

[0058] The visualization unit can visualize negotiation difficulty by considering geographical features and weather conditions. For example, the visualization unit can visualize negotiation difficulty by considering the topography. The visualization unit can use AI to visualize negotiation difficulty by considering geographical features and weather conditions in detail. For example, the visualization unit can visualize negotiation difficulty by considering weather conditions (e.g., rainy, sunny). The visualization unit can also visualize negotiation difficulty by considering geographical obstacles (e.g., rivers, mountains). This makes it possible to visualize negotiation difficulty more accurately by considering geographical features and weather conditions. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can use AI to analyze geographical and weather data in order to visualize negotiation difficulty by considering geographical features and weather conditions.

[0059] The visualization unit can compare negotiation difficulty by referring to negotiation data from other companies. For example, the visualization unit compares negotiation difficulty based on negotiation data from other companies. The visualization unit can use AI to analyze the negotiation data of other companies in detail and compare negotiation difficulty. For example, the visualization unit can refer to the negotiation data of other companies and identify areas where negotiation is difficult. The visualization unit can also analyze the negotiation data of other companies and propose the most efficient negotiation strategy. This makes it possible to compare negotiation difficulty by referring to the negotiation data of other companies. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can use AI to analyze the data of other companies in order to compare negotiation difficulty by referring to their negotiation data.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The AI ​​agent system can also be equipped with an energy consumption unit. This unit can monitor the energy consumption associated with antenna placement in real time and propose optimal energy efficiency. For example, it can monitor the energy consumption of antennas placed between buildings and propose a highly energy-efficient placement. It can also monitor the energy consumption of antennas placed underground and propose a highly energy-efficient placement. Furthermore, it can monitor the energy consumption of areas where antenna clusters are densely concentrated and propose a highly energy-efficient placement. This makes it possible to build an efficient 5G network while minimizing energy consumption.

[0062] The AI ​​agent system can also be equipped with a security unit. This security unit can monitor security risks associated with antenna placement in real time and propose optimal security measures. For example, it can monitor the security risks of antennas placed between buildings and propose security measures. It can also monitor the security risks of antennas placed underground and propose optimal security measures. Furthermore, it can monitor the security risks of areas with a high concentration of antennas and propose security measures. This makes it possible to build an efficient 5G network while minimizing security risks.

[0063] The AI ​​agent system can also be equipped with an environmental impact assessment unit. This unit can monitor the environmental impact of antenna placement in real time and propose optimal environmental protection measures. For example, it can monitor the environmental impact of antennas placed between buildings and propose environmental protection measures. It can also monitor the environmental impact of antennas placed underground and propose optimal environmental protection measures. Furthermore, it can monitor the environmental impact of areas where antenna clusters are densely concentrated and propose environmental protection measures. This makes it possible to build an efficient 5G network while minimizing environmental impact.

[0064] The AI ​​agent system can also be equipped with a cost management unit. This unit can monitor the costs associated with antenna placement in real time and propose optimal cost-efficiency solutions. For example, it can monitor the costs of antennas placed between buildings and propose cost-effective placements. It can also monitor the costs of antennas placed underground and propose optimal placements. Furthermore, it can monitor the costs of areas with high antenna density and propose cost-effective placements. This enables the construction of an efficient 5G network while minimizing costs.

[0065] The AI ​​agent system can also be equipped with a user feedback unit. This unit can collect user feedback in real time and incorporate it into optimizing antenna placement. For example, it can collect user feedback on antennas placed between buildings and propose optimized placement. It can also collect user feedback on antennas placed underground and propose optimal placement. Furthermore, it can collect user feedback on areas with densely packed antenna clusters and propose optimized placement. This enables the construction of an efficient 5G network while incorporating user feedback.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The analysis unit analyzes the density of people in real time. For example, it analyzes areas between buildings, underground, and areas with a high concentration of antennas in real time, and analyzes in detail the radio wave conditions in areas susceptible to building interference and underground. Step 2: The simulation unit simulates the optimal antenna placement based on the information obtained by the analysis unit. For example, it simulates the propagation of radio waves in a complex urban environment and proposes an antenna placement that takes into account the radio wave conditions between buildings and underground. Step 3: The monitoring unit monitors the communication status based on the antenna configuration proposed by the simulation unit. For example, it compares coverage rates with other companies and identifies areas that require priority attention. Step 4: The Identification Unit identifies areas requiring priority attention and compares coverage rates with other companies based on information obtained by the Monitoring Unit. For example, it identifies areas with low coverage rates from other companies or areas requiring priority attention, and proposes placing antennas in those areas. Step 5: The visualization unit visualizes the difficulty of negotiating with building owners based on the information obtained by the identification unit. For example, it identifies areas where negotiations with building owners are difficult and proposes negotiation strategies for placing antennas in those areas.

[0068] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that simulates radio waves in a mobile phone network and supports the optimal placement of 5G antennas. This AI agent system analyzes the density of people, between buildings, underground, and in areas where antenna clusters are densely located in real time, and simulates and proposes antenna placements to efficiently deliver radio waves in complex urban environments. This enables the construction of an efficient 5G network and provides users with an optimal communication experience. The AI ​​agent system also monitors communication conditions, compares coverage rates with other companies, identifies areas requiring priority attention, and visualizes the difficulty of negotiations with building owners. For example, the AI ​​agent system analyzes the density of people in real time. In this process, the AI ​​agent system analyzes the propagation conditions of radio waves in complex urban environments, such as between buildings, underground, and in areas where antenna clusters are densely located. For example, the AI ​​agent system grasps the radio wave conditions in areas susceptible to building interference and underground in real time and proposes the optimal antenna placement. Next, the AI ​​agent system simulates antenna placements to efficiently deliver radio waves in complex urban environments. Based on the simulation results, the AI ​​agent system proposes the optimal antenna placement. For example, the AI ​​agent system proposes placing antennas between buildings to cover areas where radio waves are difficult to reach. Furthermore, the AI ​​agent system monitors communication conditions. It compares coverage rates with other companies and identifies areas requiring priority attention. For example, it can identify areas with low coverage by other companies and propose antenna placement in those areas. The AI ​​agent system also visualizes the difficulty of negotiations with building owners. For instance, it can identify areas where negotiations with building owners are difficult and propose negotiation strategies for antenna placement in those areas. This enables the construction of an efficient 5G network, providing users with an optimal communication experience. For example, the AI ​​agent system analyzes radio wave conditions in real time and proposes optimal antenna placement, providing users with a high-quality communication experience.Furthermore, the AI ​​agent system enhances the competitiveness of telecommunications carriers by comparing coverage rates with other companies and identifying areas requiring priority support. This allows the AI ​​agent system to support the efficient construction of 5G networks and provide users with an optimal communication experience.

[0069] The AI ​​agent system according to this embodiment comprises an analysis unit, a simulation unit, a monitoring unit, a identification unit, and a visualization unit. The analysis unit analyzes the density of people in real time. For example, the analysis unit analyzes areas between buildings, underground, and areas where antenna clusters are densely concentrated in real time. The analysis unit can use AI to analyze in detail areas susceptible to building interference and underground radio wave conditions. For example, the analysis unit can identify areas susceptible to building interference and grasp the radio wave conditions in those areas in real time. The analysis unit can also grasp underground radio wave conditions in real time and propose the optimal antenna placement. The simulation unit simulates the optimal antenna placement based on the information obtained by the analysis unit. The simulation unit can use AI to simulate the propagation of radio waves in complex urban environments. For example, the simulation unit can propose covering areas where radio waves are difficult to reach by placing antennas between buildings. The simulation unit can also simulate underground radio wave conditions and propose the optimal antenna placement. The monitoring unit monitors the communication status based on the antenna placement proposed by the simulation unit. The Monitoring Department can use AI to compare coverage rates with other companies and identify areas requiring priority attention. For example, the Monitoring Department can identify areas with low coverage rates by other companies and propose the placement of antennas in those areas. The Monitoring Department can also identify areas requiring priority attention and propose the placement of antennas in those areas. The Identification Department compares coverage rates with other companies and identifies areas requiring priority attention based on the information obtained by the Monitoring Department. The Identification Department can use AI to identify areas with low coverage rates by other companies and propose the placement of antennas in those areas. For example, the Identification Department can identify areas with low coverage rates by other companies and propose the placement of antennas in those areas. The Identification Department can also identify areas requiring priority attention and propose the placement of antennas in those areas. The Visualization Department visualizes the difficulty of negotiating with building owners based on the information obtained by the Identification Department.The visualization unit can use AI to identify areas where negotiations with building owners are difficult and propose negotiation strategies for placing antennas in those areas. For example, the visualization unit can identify areas where negotiations with building owners are difficult and propose negotiation strategies for placing antennas in those areas. Furthermore, the visualization unit can identify areas where negotiations with building owners are difficult and propose negotiation strategies for placing antennas in those areas. As a result, the AI ​​agent system according to this embodiment can support the construction of an efficient 5G network and provide users with an optimal communication experience.

[0070] The analysis department analyzes the density of people in real time. Specifically, it analyzes areas between buildings, underground, and areas with a high concentration of antennas in real time. The analysis department uses AI to analyze in detail areas susceptible to building interference and underground radio wave conditions. For example, it can identify areas susceptible to building interference and understand the radio wave conditions in those areas in real time. The AI ​​uses image recognition and data analysis technologies to identify areas susceptible to building interference. Specifically, it simulates radio wave propagation based on information such as building height, location, and materials, and identifies areas where radio waves are difficult to reach. It can also understand underground radio wave conditions in real time and propose the optimal antenna placement. To understand underground radio wave conditions, the AI ​​analyzes information such as underground structure, materials, and existing antenna placement, and simulates radio wave propagation. This allows for a detailed understanding of underground radio wave conditions and the proposal of the optimal antenna placement. Furthermore, the analysis department monitors changes in radio wave conditions based on data collected in real time and can revise antenna placement as needed. This helps maintain optimal radio wave conditions at all times and supports the construction of an efficient 5G network.

[0071] The simulation unit simulates the optimal antenna placement based on information obtained by the analysis unit. Specifically, it can use AI to simulate radio wave propagation in complex urban environments. For example, it can propose covering areas with poor radio wave reception by placing antennas between buildings. The AI ​​simulates radio wave propagation based on information such as building height, location, and materials, and proposes the optimal antenna placement. It can also simulate underground radio wave conditions and propose the optimal antenna placement. To simulate underground radio wave conditions, the AI ​​analyzes information such as underground structure, materials, and existing antenna placements, and simulates radio wave propagation. This allows for a detailed understanding of underground radio wave conditions and the proposal of the optimal antenna placement. Furthermore, the simulation unit monitors changes in radio wave conditions based on data collected in real time and can revise antenna placement as needed. This helps maintain optimal radio wave conditions at all times and supports the construction of an efficient 5G network.

[0072] The monitoring unit monitors communication status based on the antenna placement proposed by the simulation unit. Specifically, it can use AI to compare coverage rates with other companies and identify areas requiring priority attention. For example, it can identify areas with low coverage rates from other companies and propose placing antennas in those areas. The AI ​​analyzes coverage rate data from other companies and compares it with its own to identify areas with low coverage. It can also identify areas requiring priority attention and propose placing antennas in those areas. To identify areas requiring priority attention, the AI ​​analyzes communication traffic data and user feedback data to identify areas with degraded communication quality and areas with high communication demand. This allows the monitoring unit to monitor communication status in real time and revise antenna placement as needed. Furthermore, by comparing coverage rates with other companies and identifying areas requiring priority attention, the monitoring unit can support the construction of an efficient 5G network.

[0073] The Specialized Unit identifies areas requiring priority attention and compares coverage rates with other companies based on information obtained by the Monitoring Unit. Specifically, it can use AI to identify areas with low coverage rates from other companies and propose antenna placement in those areas. The AI ​​analyzes coverage rate data from other companies and compares it with its own to identify areas with low coverage. It can also identify areas requiring priority attention and propose antenna placement in those areas. To identify areas requiring priority attention, the AI ​​analyzes communication traffic data and user feedback data to identify areas with degraded communication quality and areas with high communication demand. This allows the Specialized Unit to monitor communication conditions in real time and revise antenna placement as needed. Furthermore, by comparing coverage rates with other companies and identifying areas requiring priority attention, the Specialized Unit can support the construction of an efficient 5G network.

[0074] The visualization unit visualizes the difficulty of negotiating with building owners based on information obtained by the identification unit. Specifically, it can use AI to identify areas where negotiations with building owners are difficult and propose negotiation strategies for placing antennas in those areas. The AI ​​analyzes information such as the building owner's past negotiation history, building ownership status, and regional characteristics to identify areas where negotiations are difficult. It also proposes the optimal negotiation strategy for these difficult areas. For example, for areas where negotiations with building owners are difficult, it proposes the timing and method of negotiation, as well as the necessary documents. This allows the visualization unit to efficiently advance negotiations with building owners and achieve optimal antenna placement. Furthermore, the visualization unit visualizes the progress of negotiations in real time and can revise the negotiation strategy as needed. This allows for the maintenance of an optimal negotiation strategy at all times and supports the construction of an efficient 5G network.

[0075] The analysis unit can analyze areas between buildings, underground, and areas with a high concentration of antennas in real time. For example, the analysis unit can grasp the radio wave conditions between buildings and underground in real time. The analysis unit can use AI to identify areas susceptible to building interference and analyze the radio wave conditions in those areas in detail. For example, the analysis unit can identify areas susceptible to building interference and grasp the radio wave conditions in those areas in real time. In addition, the analysis unit can grasp the underground radio wave conditions in real time and propose the optimal antenna placement. As a result, by analyzing areas between buildings, underground, and areas with a high concentration of antennas in real time, it is possible to grasp the radio wave propagation conditions in complex urban environments in detail. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to analyze radio wave propagation conditions in order to grasp the radio wave conditions between buildings and underground in real time.

[0076] The simulation unit can simulate the propagation of radio waves in complex urban environments. For example, the simulation unit can propose covering areas where radio waves are difficult to reach by placing antennas between buildings. The simulation unit can use AI to simulate the propagation of radio waves in complex urban environments in detail. For example, the simulation unit can identify areas that are susceptible to the influence of buildings and simulate the radio wave conditions in those areas. The simulation unit can also simulate underground radio wave conditions and propose the optimal antenna placement. In this way, by simulating the propagation of radio waves in complex urban environments, the optimal antenna placement can be proposed. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can use AI to simulate the propagation of radio waves in order to propose covering areas where radio waves are difficult to reach by placing antennas between buildings.

[0077] The monitoring unit can compare coverage rates with other companies. For example, the monitoring unit can identify areas where other companies have low coverage and propose placing antennas in those areas. The monitoring unit can use AI to perform detailed comparisons of coverage rates with other companies. For example, the monitoring unit can identify areas where other companies have low coverage and propose placing antennas in those areas. The monitoring unit can also identify areas where other companies have low coverage and propose placing antennas in those areas. This makes it possible to build a competitive network by comparing coverage rates with other companies. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to compare coverage rates with other companies in order to identify areas where other companies have low coverage and propose placing antennas in those areas.

[0078] The identification unit can identify areas requiring priority attention. For example, the identification unit can identify areas with high communication traffic or areas with important facilities and propose the placement of antennas in those areas. The identification unit can use AI to identify areas requiring priority attention in detail. For example, the identification unit can identify areas with high communication traffic and propose the placement of antennas in those areas. It can also identify areas with important facilities and propose the placement of antennas in those areas. By identifying areas requiring priority attention, efficient network operation becomes possible. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can use AI to identify areas requiring priority attention in order to identify areas with high communication traffic or areas with important facilities and propose the placement of antennas in those areas.

[0079] The visualization unit can visualize the difficulty of negotiating with building owners. For example, the visualization unit can identify areas where negotiations with building owners are difficult and propose negotiation strategies for placing antennas in those areas. The visualization unit can use AI to visualize the difficulty of negotiating with building owners in detail. For example, the visualization unit can identify areas where negotiations with building owners are difficult and propose negotiation strategies for placing antennas in those areas. The visualization unit can also identify areas where negotiations with building owners are difficult and propose negotiation strategies for placing antennas in those areas. By visualizing the difficulty of negotiating with building owners, it becomes easier to formulate negotiation strategies. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can use AI to visualize the difficulty of negotiating with building owners in order to identify areas where negotiations with building owners are difficult and propose negotiation strategies for placing antennas in those areas.

[0080] The analysis unit can estimate the user's emotions and adjust the method of analyzing crowd density based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple interface and concisely display the results of the crowd density analysis. The analysis unit can use AI to estimate the user's emotions in detail and adjust the analysis method based on those emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results and visually show changes in crowd density. Also, if the user is in a hurry, the analysis unit can quickly analyze crowd density in real time and display the results immediately. This allows the system to provide the user with the optimal analysis results by adjusting the analysis method according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the analysis unit may be performed using AI or not. For example, the analysis department can use AI to estimate users' emotions and adjust the method of analyzing crowd density based on those emotions.

[0081] The analysis unit can predict current congestion levels by referring to past congestion data. For example, the analysis unit can refer to past event data to predict congestion levels when similar events are held. The analysis unit can use AI to analyze past congestion data in detail and predict current congestion levels. For example, the analysis unit can predict current congestion levels based on past congestion data for each time period. The analysis unit can also refer to past seasonal congestion data to predict congestion levels for the current season. This allows for a more accurate prediction of current congestion levels by referring to past data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to analyze past data in order to predict current congestion levels by referring to past congestion data.

[0082] The analysis unit can predict changes in congestion based on specific events or time periods. For example, the analysis unit can predict changes in congestion based on past data when large-scale events are held. The analysis unit can use AI to predict changes in congestion in detail based on specific events or time periods. For example, the analysis unit can predict changes in congestion during commuting hours and propose the optimal antenna placement. The analysis unit can also predict changes in congestion during seasonal events (e.g., fireworks displays). By predicting changes in congestion based on events and time periods, it can propose appropriate antenna placement. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to analyze past data in order to predict changes in congestion based on specific events or time periods.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the crowd situation analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. The analysis unit can use AI to estimate the user's emotions in detail and adjust the display method based on those emotions. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Also, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method according to the user's emotions, the optimal display result can be provided to the user. Emotion estimation is achieved using an emotion estimation function, such as 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 analysis unit may be performed using AI or not. For example, the analysis unit can use AI to estimate the user's emotions and adjust the display method of the crowd situation analysis results based on those emotions.

[0084] The analysis unit can analyze density conditions by considering geographical features and weather conditions. For example, the analysis unit can analyze density conditions by considering topographical features. The analysis unit can use AI to analyze density conditions by considering geographical features and weather conditions in detail. For example, the analysis unit can analyze density conditions by considering weather conditions (e.g., rainy, sunny). The analysis unit can also analyze density conditions by considering geographical obstacles (e.g., rivers, mountains). This makes it possible to analyze density conditions more accurately by considering geographical features and weather conditions. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to analyze geographical and weather data in order to analyze density conditions by considering geographical features and weather conditions.

[0085] The analysis unit can grasp the density situation in real time by referring to social media data. For example, the analysis unit can analyze social media posts to grasp the density situation in real time. The analysis unit can use AI to analyze social media data in detail and grasp the density situation in real time. For example, the analysis unit can identify dense areas based on social media location information. The analysis unit can also analyze social media trends to predict changes in the density situation. As a result, the density situation can be grasped in real time by referring to social media data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to analyze social media data in order to grasp the density situation in real time by referring to social media data.

[0086] The simulation unit can estimate the user's emotions and adjust the antenna placement simulation method based on the estimated emotions. For example, if the user is stressed, the simulation unit can provide a simple interface and display the simulation results concisely. The simulation unit can use AI to estimate the user's emotions in detail and adjust the simulation method based on those emotions. For example, if the user is relaxed, the simulation unit can provide detailed simulation results and show antenna placement options. Also, if the user is in a hurry, the simulation unit can perform a simulation quickly and display the results immediately. This allows the system to provide the user with the optimal simulation results by adjusting the simulation method according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the simulation unit may be performed using AI or not. For example, the simulation unit can use AI to estimate the user's emotions and adjust the antenna placement simulation method based on those emotions.

[0087] The simulation unit can predict current radio wave conditions by referring to past radio wave propagation data. For example, the simulation unit predicts current radio wave conditions based on past radio wave propagation data. The simulation unit can use AI to analyze past radio wave propagation data in detail and predict current radio wave conditions. For example, the simulation unit can predict current radio wave conditions based on past radio wave propagation data for each time period. Furthermore, the simulation unit can predict radio wave conditions for the current season based on past radio wave propagation data for each season. This allows for a more accurate prediction of current radio wave conditions by referring to past data. Some or all of the above-described processes in the simulation unit may be performed using AI or not. For example, the simulation unit can use AI to analyze past data in order to predict current radio wave conditions by referring to past radio wave propagation data.

[0088] The simulation unit can predict changes in radio wave conditions based on specific events or time periods. For example, the simulation unit can predict changes in radio wave conditions based on past data when a large-scale event is held. The simulation unit can use AI to predict changes in radio wave conditions in detail based on specific events or time periods. For example, the simulation unit can predict changes in radio wave conditions during commuting hours and propose the optimal antenna placement. The simulation unit can also predict changes in radio wave conditions during seasonal events (e.g., fireworks displays). By predicting changes in radio wave conditions based on events and time periods, it can propose appropriate antenna placement. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can use AI to analyze past data in order to predict changes in radio wave conditions based on specific events or time periods.

[0089] 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. The simulation unit can use AI to estimate the user's emotions in detail and adjust the display method based on those emotions. For example, if the user is relaxed, the simulation unit can provide a display method that includes detailed information. Also, if the user is in a hurry, the simulation unit can provide a display method that gets straight to the point. In this way, by adjusting the display method according to the user's emotions, the optimal display result can be provided to the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a 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 or not. For example, the simulation unit can use AI to estimate the user's emotions and adjust the display method of the simulation results based on those emotions.

[0090] The simulation unit can simulate radio wave propagation conditions by considering geographical features and weather conditions. For example, the simulation unit can simulate radio wave propagation conditions by considering the topography. The simulation unit can use AI to simulate radio wave propagation conditions by considering geographical features and weather conditions in detail. For example, the simulation unit can simulate radio wave propagation conditions by considering weather conditions (e.g., rainy, sunny). The simulation unit can also simulate radio wave propagation conditions by considering geographical obstacles (e.g., rivers, mountains). This makes it possible to simulate radio wave propagation conditions more accurately by considering geographical features and weather conditions. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can use AI to analyze geographical and weather data in order to simulate radio wave propagation conditions by considering geographical features and weather conditions.

[0091] The simulation unit can propose the optimal antenna placement by referring to other companies' antenna placement data. For example, the simulation unit can propose the optimal antenna placement based on other companies' antenna placement data. The simulation unit can use AI to analyze other companies' antenna placement data in detail and propose the optimal placement. For example, the simulation unit can refer to other companies' coverage rates and propose placing antennas in areas with low coverage rates. The simulation unit can also analyze other companies' antenna placement data and propose the most efficient placement. In this way, the optimal antenna placement can be proposed by referring to other companies' antenna placement data. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can use AI to analyze other companies' data in order to propose the optimal placement by referring to other companies' antenna placement data.

[0092] The monitoring unit can estimate the user's emotions and adjust the communication status monitoring method based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can provide a simple interface and concisely display the communication status monitoring results. The monitoring unit can use AI to estimate the user's emotions in detail and adjust the monitoring method based on those emotions. For example, if the user is relaxed, the monitoring unit can provide detailed monitoring results and visually show changes in the communication status. Also, if the user is in a hurry, the monitoring unit can quickly monitor the communication status in real time and display the results immediately. In this way, by adjusting the monitoring method according to the user's emotions, the optimal monitoring results can be provided to the user. Emotion estimation is achieved using an emotion estimation function, such as 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 monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to estimate the user's emotions and adjust the communication status monitoring method based on those emotions.

[0093] The monitoring unit can predict the current communication status by referring to past communication data. For example, the monitoring unit predicts the current communication status based on past communication data. The monitoring unit can use AI to analyze past communication data in detail and predict the current communication status. For example, the monitoring unit can predict the communication status for the current time period based on past communication data for each time period. In addition, the monitoring unit can predict the communication status for the current season based on past communication data for each season. This allows for a more accurate prediction of the current communication status by referring to past data. Some or all of the above-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to analyze past data in order to predict the current communication status by referring to past communication data.

[0094] The monitoring unit can predict changes in communication status based on specific events or time periods. For example, the monitoring unit can predict changes in communication status based on past data when a large-scale event is held. The monitoring unit can use AI to predict changes in communication status in detail based on specific events or time periods. For example, the monitoring unit can predict changes in communication status during commuting hours and propose the optimal antenna placement. The monitoring unit can also predict changes in communication status during seasonal events (e.g., fireworks displays). By predicting changes in communication status based on events and time periods, it can propose appropriate antenna placement. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to analyze past data in order to predict changes in communication status based on specific events or time periods.

[0095] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated emotions. For example, if the user is nervous, the monitoring unit can provide a simple and highly visible display method. The monitoring unit can use AI to estimate the user's emotions in detail and adjust the display method based on those emotions. For example, if the user is relaxed, the monitoring unit can provide a display method that includes detailed information. Also, if the user is in a hurry, the monitoring unit can provide a display method that gets straight to the point. In this way, by adjusting the display method according to the user's emotions, the optimal display result can be provided to the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to estimate the user's emotions and adjust the display method of the monitoring results based on those emotions.

[0096] The monitoring unit can monitor communication status while considering geographical features and weather conditions. For example, the monitoring unit can monitor communication status while considering the topography. The monitoring unit can use AI to monitor communication status while considering geographical features and weather conditions in detail. For example, the monitoring unit can monitor communication status while considering weather conditions (e.g., rainy, sunny). The monitoring unit can also monitor communication status while considering geographical obstacles (e.g., rivers, mountains). This makes it possible to monitor communication status more accurately by considering geographical features and weather conditions. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to analyze geographical data and weather data in order to monitor communication status while considering geographical features and weather conditions.

[0097] The monitoring unit can compare coverage rates by referring to the communication data of other companies. For example, the monitoring unit can compare coverage rates based on the communication data of other companies. The monitoring unit can use AI to analyze the communication data of other companies in detail and compare coverage rates. For example, the monitoring unit can refer to the coverage rates of other companies and identify areas with low coverage rates. The monitoring unit can also analyze the communication data of other companies and propose the most efficient coverage rate. This makes it possible to compare coverage rates by referring to the communication data of other companies. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to analyze the data of other companies in order to compare coverage rates by referring to their communication data.

[0098] The identification unit can estimate the user's emotions and adjust the method for identifying areas requiring priority attention based on the estimated emotions. For example, if the user is stressed, the identification unit can provide a simple interface and concisely display the results of the priority area identification. The identification unit can use AI to estimate the user's emotions in detail and adjust the identification method based on those emotions. For example, if the user is relaxed, the identification unit can provide detailed identification results and visually show changes in priority areas. Also, if the user is in a hurry, the identification unit can quickly identify priority areas in real time and display the results immediately. This allows for the provision of optimal identification results for the user by adjusting the identification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 identification unit may be performed using AI or not. For example, the specific unit can use AI to estimate the user's emotions and adjust the method for identifying areas that require priority attention based on those emotions.

[0099] The identification unit can predict the current priority service area by referring to past priority service data. For example, the identification unit predicts the current priority service area based on past priority service data. The identification unit can use AI to analyze past priority service data in detail and predict the current priority service area. For example, the identification unit can predict the priority service area for the current time period based on past priority service data for each time period. In addition, the identification unit can predict the priority service area for the current season based on past priority service data for each season. This allows for a more accurate prediction of the current priority service area by referring to past data. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can use AI to analyze past data in order to predict the current priority service area by referring to past priority service data.

[0100] The specific unit can predict changes in priority service areas based on specific events or time periods. For example, the specific unit predicts changes in priority service areas based on past data when large-scale events are held. The specific unit can use AI to predict changes in priority service areas in detail based on specific events or time periods. For example, the specific unit can predict changes in priority service areas during commuting hours and propose the optimal antenna placement. The specific unit can also predict changes in priority service areas during seasonal events (e.g., fireworks displays). By predicting changes in priority service areas based on events and time periods, it can propose appropriate antenna placement. Some or all of the above processing in the specific unit may be performed using AI or not. For example, the specific unit can use AI to analyze past data in order to predict changes in priority service areas based on specific events or time periods.

[0101] The identification unit can estimate the user's emotions and adjust the display method of the identification results based on the estimated user emotions. For example, if the user is nervous, the identification unit provides a simple and highly visible display method. The identification unit can use AI to estimate the user's emotions in detail and adjust the display method based on those emotions. For example, if the user is relaxed, the identification unit can provide a display method that includes detailed information. Also, if the user is in a hurry, the identification unit can provide a display method that gets straight to the point. In this way, by adjusting the display method according to the user's emotions, the optimal display result can be provided to the user. Emotion estimation is achieved using an emotion estimation function 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 identification unit may be performed using AI or not. For example, the identification unit can use AI to estimate the user's emotions and adjust the display method of the identification results based on those emotions.

[0102] The identification unit can identify priority response areas by considering geographical features and weather conditions. For example, the identification unit can identify priority response areas by considering the topography. The identification unit can use AI to identify priority response areas by considering geographical features and weather conditions in detail. For example, the identification unit can identify priority response areas by considering weather conditions (e.g., rainy, sunny). The identification unit can also identify priority response areas by considering geographical obstacles (e.g., rivers, mountains). This makes it possible to identify priority response areas more accurately by considering geographical features and weather conditions. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can use AI to analyze geographical and weather data in order to identify priority response areas by considering geographical features and weather conditions.

[0103] The identification unit can identify the optimal area by referring to other companies' priority response data. For example, the identification unit can identify the optimal area based on other companies' priority response data. The identification unit can use AI to analyze other companies' priority response data in detail and identify the optimal area. For example, the identification unit can refer to other companies' coverage rates and identify areas with low coverage rates as priority response areas. The identification unit can also analyze other companies' priority response data and identify the most efficient area. This makes it possible to identify the optimal area by referring to other companies' priority response data. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can use AI to analyze other companies' data in order to identify the optimal area by referring to other companies' priority response data.

[0104] The visualization unit can estimate the user's emotions and adjust the visualization method of the difficulty of negotiating with the building owner based on the estimated user emotions. For example, if the user is stressed, the visualization unit provides a simple interface and concisely displays the visualization results of the negotiation difficulty. The visualization unit can use AI to estimate the user's emotions in detail and adjust the visualization method based on those emotions. For example, if the user is relaxed, the visualization unit can provide detailed visualization results and visually show changes in negotiation difficulty. Also, if the user is in a hurry, the visualization unit can quickly visualize the negotiation difficulty in real time and display the results immediately. In this way, by adjusting the visualization method according to the user's emotions, the optimal visualization results can be provided to the user. Emotion estimation is achieved using an emotion estimation function, such as 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 or not. For example, the visualization unit can use AI to estimate the user's emotions and adjust the visualization method for the difficulty of negotiating with the building owner based on those emotions.

[0105] The visualization unit can predict the current negotiation difficulty by referring to past negotiation data. For example, the visualization unit predicts the current negotiation difficulty based on past negotiation data. The visualization unit can use AI to analyze past negotiation data in detail and predict the current negotiation difficulty. For example, the visualization unit can predict the negotiation difficulty for the current time period based on past negotiation data for each time period. In addition, the visualization unit can predict the negotiation difficulty for the current season based on past negotiation data for each season. This allows for a more accurate prediction of the current negotiation difficulty by referring to past data. Some or all of the above-described processes in the visualization unit may be performed using AI or not. For example, the visualization unit can use AI to analyze past data in order to predict the current negotiation difficulty by referring to past negotiation data.

[0106] The visualization unit can predict changes in negotiation difficulty based on specific events or time periods. For example, the visualization unit can predict changes in negotiation difficulty based on past data when large-scale events are held. The visualization unit can use AI to predict changes in negotiation difficulty in detail based on specific events or time periods. For example, the visualization unit can predict changes in negotiation difficulty during commuting hours and propose the optimal negotiation strategy. The visualization unit can also predict changes in negotiation difficulty during seasonal events (e.g., fireworks displays). This allows for the proposal of appropriate negotiation strategies by predicting changes in negotiation difficulty based on events and time periods. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can use AI to analyze past data in order to predict changes in negotiation difficulty based on specific events or time periods.

[0107] The visualization unit can estimate the user's emotions and adjust the display method of the visualization results 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. The visualization unit can use AI to estimate the user's emotions in detail and adjust the display method based on those emotions. For example, if the user is relaxed, the visualization unit can provide a display method that includes detailed information. Also, if the user is in a hurry, the visualization unit can provide a display method that gets straight to the point. In this way, by adjusting the display method according to the user's emotions, the optimal display result can be provided to the user. Emotion estimation is achieved using an emotion estimation function 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 or not. For example, the visualization unit can use AI to estimate the user's emotions and adjust the display method of the visualization results based on those emotions.

[0108] The visualization unit can visualize negotiation difficulty by considering geographical features and weather conditions. For example, the visualization unit can visualize negotiation difficulty by considering the topography. The visualization unit can use AI to visualize negotiation difficulty by considering geographical features and weather conditions in detail. For example, the visualization unit can visualize negotiation difficulty by considering weather conditions (e.g., rainy, sunny). The visualization unit can also visualize negotiation difficulty by considering geographical obstacles (e.g., rivers, mountains). This makes it possible to visualize negotiation difficulty more accurately by considering geographical features and weather conditions. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can use AI to analyze geographical and weather data in order to visualize negotiation difficulty by considering geographical features and weather conditions.

[0109] The visualization unit can compare negotiation difficulty by referring to negotiation data from other companies. For example, the visualization unit compares negotiation difficulty based on negotiation data from other companies. The visualization unit can use AI to analyze the negotiation data of other companies in detail and compare negotiation difficulty. For example, the visualization unit can refer to the negotiation data of other companies and identify areas where negotiation is difficult. The visualization unit can also analyze the negotiation data of other companies and propose the most efficient negotiation strategy. This makes it possible to compare negotiation difficulty by referring to the negotiation data of other companies. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can use AI to analyze the data of other companies in order to compare negotiation difficulty by referring to their negotiation data.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The AI ​​agent system can also be equipped with an energy consumption unit. This unit can monitor the energy consumption associated with antenna placement in real time and propose optimal energy efficiency. For example, it can monitor the energy consumption of antennas placed between buildings and propose a highly energy-efficient placement. It can also monitor the energy consumption of antennas placed underground and propose a highly energy-efficient placement. Furthermore, it can monitor the energy consumption of areas where antenna clusters are densely concentrated and propose a highly energy-efficient placement. This makes it possible to build an efficient 5G network while minimizing energy consumption.

[0112] The AI ​​agent system can also be equipped with a security unit. This security unit can monitor security risks associated with antenna placement in real time and propose optimal security measures. For example, it can monitor the security risks of antennas placed between buildings and propose security measures. It can also monitor the security risks of antennas placed underground and propose optimal security measures. Furthermore, it can monitor the security risks of areas with a high concentration of antennas and propose security measures. This makes it possible to build an efficient 5G network while minimizing security risks.

[0113] The AI ​​agent system can also be equipped with an environmental impact assessment unit. This unit can monitor the environmental impact of antenna placement in real time and propose optimal environmental protection measures. For example, it can monitor the environmental impact of antennas placed between buildings and propose environmental protection measures. It can also monitor the environmental impact of antennas placed underground and propose optimal environmental protection measures. Furthermore, it can monitor the environmental impact of areas where antenna clusters are densely concentrated and propose environmental protection measures. This makes it possible to build an efficient 5G network while minimizing environmental impact.

[0114] The AI ​​agent system can also be equipped with a cost management unit. This unit can monitor the costs associated with antenna placement in real time and propose optimal cost-efficiency solutions. For example, it can monitor the costs of antennas placed between buildings and propose cost-effective placements. It can also monitor the costs of antennas placed underground and propose optimal placements. Furthermore, it can monitor the costs of areas with high antenna density and propose cost-effective placements. This enables the construction of an efficient 5G network while minimizing costs.

[0115] The AI ​​agent system can also be equipped with a user feedback unit. This unit can collect user feedback in real time and incorporate it into optimizing antenna placement. For example, it can collect user feedback on antennas placed between buildings and propose optimized placement. It can also collect user feedback on antennas placed underground and propose optimal placement. Furthermore, it can collect user feedback on areas with densely packed antenna clusters and propose optimized placement. This enables the construction of an efficient 5G network while incorporating user feedback.

[0116] The AI ​​agent system can also be equipped with an emotion estimation unit. This unit can estimate the user's emotions in real time and optimize antenna placement based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit can provide a simple interface and suggest an optimized antenna placement. If the user is relaxed, the emotion estimation unit can provide detailed information and suggest the optimal antenna placement. Furthermore, if the user is in a hurry, the emotion estimation unit can quickly optimize the antenna placement and display the results immediately. This allows for the provision of an optimal communication experience for the user by optimizing antenna placement according to their emotions.

[0117] The AI ​​agent system can also be equipped with an emotion estimation unit. This unit can estimate the user's emotions in real time and adjust the communication status monitoring method based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit can provide a simple interface and concisely display the communication status monitoring results. If the user is relaxed, the emotion estimation unit can provide detailed monitoring results and visually indicate changes in the communication status. Furthermore, if the user is in a hurry, the emotion estimation unit can quickly monitor the communication status in real time and display the results immediately. This allows the system to provide optimal monitoring results for the user by adjusting the monitoring method according to their emotions.

[0118] The AI ​​agent system can also be equipped with an emotion estimation unit. This unit can estimate the user's emotions in real time and adjust the method of identifying priority areas based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit can provide a simple interface and concisely display the results of the priority area identification. If the user is relaxed, the emotion estimation unit can provide detailed identification results and visually show the changes in the priority areas. Furthermore, if the user is in a hurry, the emotion estimation unit can quickly identify priority areas in real time and display the results immediately. This allows the system to provide the optimal identification results for the user by adjusting the identification method according to their emotions.

[0119] The AI ​​agent system can also be equipped with an emotion estimation unit. This unit can estimate the user's emotions in real time and adjust the visualization method of the building owner's negotiation difficulty based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit can provide a simple interface and concisely display the negotiation difficulty visualization results. If the user is relaxed, the emotion estimation unit can provide detailed visualization results and visually show changes in negotiation difficulty. Furthermore, if the user is in a hurry, the emotion estimation unit can quickly visualize the negotiation difficulty in real time and display the results immediately. This allows the system to provide the optimal visualization results for the user by adjusting the visualization method according to their emotions.

[0120] The AI ​​agent system can also be equipped with an emotion estimation unit. This unit can estimate the user's emotions in real time and adjust the display method of the visualization results based on the estimated emotions. For example, if the user is tense, the emotion estimation unit can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise display method. By adjusting the display method according to the user's emotions, the system can provide the optimal display result for the user.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The analysis unit analyzes the density of people in real time. For example, it analyzes areas between buildings, underground, and areas with a high concentration of antennas in real time, and analyzes in detail the radio wave conditions in areas susceptible to building interference and underground. Step 2: The simulation unit simulates the optimal antenna placement based on the information obtained by the analysis unit. For example, it simulates the propagation of radio waves in a complex urban environment and proposes an antenna placement that takes into account the radio wave conditions between buildings and underground. Step 3: The monitoring unit monitors the communication status based on the antenna configuration proposed by the simulation unit. For example, it compares coverage rates with other companies and identifies areas that require priority attention. Step 4: The Identification Unit identifies areas requiring priority attention and compares coverage rates with other companies based on information obtained by the Monitoring Unit. For example, it identifies areas with low coverage rates from other companies or areas requiring priority attention, and proposes placing antennas in those areas. Step 5: The visualization unit visualizes the difficulty of negotiating with building owners based on the information obtained by the identification unit. For example, it identifies areas where negotiations with building owners are difficult and proposes negotiation strategies for placing antennas in those areas.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the analysis unit, simulation unit, monitoring unit, identification unit, and visualization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes the density of people in real time. The simulation unit is implemented by the identification processing unit 290 of the data processing unit 12 and simulates the optimal antenna placement. The monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the communication status. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies areas that require priority attention, such as comparing coverage rates with other companies. The visualization unit is implemented by the control unit 46A of the smart device 14 and visualizes the difficulty of negotiating with building owners. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the analysis unit, simulation unit, monitoring unit, identification unit, and visualization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the density of people in real time. The simulation unit is implemented by the identification unit 290 of the data processing unit 12 and simulates the optimal antenna placement. The monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the communication status. The identification unit is implemented by the identification unit 290 of the data processing unit 12 and identifies areas that require priority attention, such as comparing coverage rates with other companies. The visualization unit is implemented by the control unit 46A of the smart glasses 214 and visualizes the difficulty of negotiating with building owners. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the analysis unit, simulation unit, monitoring unit, identification unit, and visualization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the density of people in real time. The simulation unit is implemented by the identification processing unit 290 of the data processing unit 12 and simulates the optimal antenna placement. The monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the communication status. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies areas that require priority attention and allow for comparison of coverage rates with other companies. The visualization unit is implemented by the control unit 46A of the headset terminal 314 and visualizes the difficulty of negotiating with building owners. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the analysis unit, simulation unit, monitoring unit, identification unit, and visualization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 and analyzes the density of people in real time. The simulation unit is implemented by the identification unit 290 of the data processing unit 12 and simulates the optimal antenna placement. The monitoring unit is implemented by the control unit 46A of the robot 414 and monitors the communication status. The identification unit is implemented by the identification unit 290 of the data processing unit 12 and identifies areas that require priority attention, such as comparing coverage rates with other companies. The visualization unit is implemented by the control unit 46A of the robot 414 and visualizes the difficulty of negotiating with building owners. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) The analysis department analyzes the density of people in real time, Based on the information obtained by the analysis unit, a simulation unit simulates the optimal antenna arrangement. A monitoring unit that monitors the communication status based on the antenna arrangement proposed by the simulation unit, Based on the information obtained from the monitoring unit, the identification unit compares coverage rates with other companies and identifies areas requiring priority attention. The system includes a visualization unit that visualizes the difficulty of negotiating with the building owner based on the information obtained by the specified unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Analyze in real time areas between buildings, underground, and in areas with a high concentration of antennas. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned simulation unit, Simulate the propagation of radio waves in complex urban environments. The system described in Appendix 1, characterized by the features described herein. (Note 4) The monitoring unit, We compared our coverage rate with that of other companies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The specified part is, Identify areas that require priority attention. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned visualization unit, Visualize the difficulty of negotiations with building owners. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is We estimate user emotions and adjust the method of analyzing crowd density based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is Predicting current density levels by referring to past density data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is Predicting changes in crowd density based on specific events and time periods. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is The system estimates the user's emotions and adjusts how the density analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is Analyze the density situation by considering geographical features and weather conditions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We use social media data to understand the density situation in real time. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned simulation unit, The system estimates the user's emotions and adjusts the antenna placement simulation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned simulation unit, Predicting current radio wave conditions by referring to past radio wave propagation data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned simulation unit, Predicting changes in radio wave conditions based on specific events or time periods. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned simulation unit, The system simulates radio wave propagation conditions, taking into account geographical features and weather conditions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned simulation unit, We propose the optimal antenna placement by referring to antenna placement data from other companies. The system described in Appendix 1, characterized by the features described herein. (Note 19) The monitoring unit, The system estimates the user's emotions and adjusts the communication monitoring method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The monitoring unit, Predicting current communication status by referring to past communication data The system described in Appendix 1, characterized by the features described herein. (Note 21) The monitoring unit, Predicting changes in communication conditions based on specific events or time periods. The system described in Appendix 1, characterized by the features described herein. (Note 22) The monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The monitoring unit, Monitoring communication status while considering geographical features and weather conditions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The monitoring unit, Compare coverage rates by referring to communication data from other companies. The system described in Appendix 1, characterized by the features described herein. (Note 25) The specified part is, We estimate user sentiment and adjust the method for identifying areas that require priority attention based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The specified part is, Predict current priority areas by referring to past priority response data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The specified part is, Predict changes in priority service areas based on specific events and time periods. The system described in Appendix 1, characterized by the features described herein. (Note 28) The specified part is, It estimates the user's emotions and adjusts how specific results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The specified part is, Priority response areas are identified by considering geographical features and weather conditions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The specified part is, We identify the optimal area by referring to priority response data from other companies. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned visualization unit, We estimate the user's emotions and adjust the method of visualizing the difficulty of negotiating with the building owner based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned visualization unit, Predicting the current negotiation difficulty by referring to past negotiation data The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned visualization unit, Predicting changes in negotiation difficulty based on specific events or time periods. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned visualization unit, It estimates the user's emotions and adjusts how the visualization results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned visualization unit, Visualize the difficulty of negotiations by taking into account geographical features and weather conditions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned visualization unit, Compare the difficulty of negotiations by referring to negotiation data from other companies. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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. The analysis department analyzes the density of people in real time, Based on the information obtained by the analysis unit, a simulation unit simulates the optimal antenna arrangement. A monitoring unit that monitors the communication status based on the antenna arrangement proposed by the simulation unit, Based on the information obtained from the monitoring unit, the identification unit compares coverage rates with other companies and identifies areas requiring priority attention. The system includes a visualization unit that visualizes the difficulty of negotiating with the building owner based on the information obtained by the specified unit. A system characterized by the following features.

2. The aforementioned analysis unit is Analyze in real time areas between buildings, underground, and in areas with a high concentration of antennas. The system according to feature 1.

3. The aforementioned simulation unit, Simulate the propagation of radio waves in complex urban environments. The system according to feature 1.

4. The monitoring unit, We compared our coverage rate with that of other companies. The system according to feature 1.

5. The specified part is, Identify areas that require priority attention. The system according to feature 1.

6. The aforementioned visualization unit, Visualize the difficulty of negotiations with building owners. The system according to feature 1.

7. The aforementioned analysis unit is We estimate user emotions and adjust the method of analyzing crowd density based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit is Predicting current density levels by referring to past density data. The system according to feature 1.

9. The aforementioned analysis unit is Predicting changes in crowd density based on specific events and time periods. The system according to feature 1.

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