system

Abstract

The system according to the embodiment aims to accurately simulate the radio wave conditions of wireless communication equipment. [Solution] A system according to an embodiment includes a receiving unit, an identifying unit, a simulation unit, and a providing unit. The receiving unit receives input of an address or latitude and longitude. The identifying unit identifies the location of existing wireless communication equipment based on the information received by the receiving unit. The simulation unit performs a simulation using a radio wave propagation model based on the location information identified by the identifying unit. The providing unit visually provides the simulation results obtained by the simulation unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional techniques, it is difficult to accurately simulate the radio wave conditions of wireless communication equipment, and there is room for improvement.

[0005] The system according to the embodiment aims to accurately simulate the radio wave conditions of wireless communication equipment. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an identification unit, a simulation unit, and a provision unit. The reception unit receives input of an address or latitude and longitude. The identification unit identifies the location of existing wireless communication equipment based on the information received by the reception unit. The simulation unit performs a simulation using a radio wave propagation model based on the location information identified by the identification unit. The provision unit visually provides the simulation results obtained by the simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately simulate the radio wave conditions of wireless communication equipment. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A wireless communication simulation system according to an embodiment of the present invention simulates the radio wave conditions of existing wireless communication equipment at each frequency with high accuracy by inputting an address and latitude and longitude. The system begins with a user inputting the address and latitude and longitude. Next, the system identifies the location of the existing wireless communication equipment based on the input information. Furthermore, the system simulates the radio wave propagation conditions in each frequency band based on this location information. The simulation results are visually presented to the user, allowing them to check the radio wave strength and coverage area. This system enables efficient design and optimization of wireless communication. For example, when a user inputs an address and latitude and longitude, they only need to input a starting point and a destination. For example, they might input "I want to go from my home to the train station." This information is entered into the system. Next, the system analyzes the input information and identifies the location of the existing wireless communication equipment. The system identifies the location of the existing wireless communication equipment by referencing a database or using GPS information. Furthermore, the system uses a radio wave propagation model to simulate the radio wave propagation conditions in each frequency band. For example, if a user inputs a route from their home to the train station, the system simulates the radio wave strength and coverage area along that route. Simulation results are provided to the user visually. For example, radio wave strength values ​​and coverage area ranges are displayed on a map. This allows the user to intuitively understand the radio wave conditions. This system enables efficient design and optimization of wireless communications. For example, when installing new wireless communication equipment, the optimal installation location can be identified. In addition, the coverage area of ​​existing wireless communication equipment can be confirmed and adjustments made as necessary. This allows the wireless communication simulation system to efficiently design and optimize wireless communications.

[0029] A wireless communication simulation system according to an embodiment includes a receiving unit, an identifying unit, a simulation unit, and a providing unit. The receiving unit receives an input of an address or latitude and longitude. The input of the address or latitude and longitude includes, but is not limited to, an address format and a notation method for latitude and longitude. For example, the receiving unit receives the address and latitude and longitude input by a user. The identifying unit identifies the location of the existing wireless communication equipment based on the information received by the receiving unit. The identifying unit identifies the location of the existing wireless communication equipment by, for example, referring to a database or using GPS information. For example, the identifying unit identifies the location of the existing wireless communication equipment by referring to a database. The identifying unit can also identify the location of the existing wireless communication equipment by using GPS information. The simulation unit performs a simulation using a radio wave propagation model based on the location information identified by the identifying unit. The simulation unit performs a simulation using a radio wave propagation model, such as a free space model or a Rayleigh fading model. For example, the simulation unit simulates the propagation status of radio waves using the free space model. The simulation unit can also simulate the propagation status of radio waves using the Rayleigh fading model. The providing unit visually provides the simulation results obtained by the simulation unit. The providing unit visually provides the simulation results using methods such as graphs, heat maps, and 3D models. For example, the providing unit displays the simulation results as graphs. The providing unit can also display the simulation results as heat maps. The providing unit can also display the simulation results as 3D models. As a result, the wireless communication simulation system according to the embodiment can simulate the radio wave conditions of wireless communication equipment with high accuracy based on input of addresses and latitude and longitude, and visually provide the simulation results.

[0030] The simulation unit can simulate the propagation status of radio waves in each frequency band. The simulation unit simulates the propagation status of radio waves in each frequency band, such as 2.4 GHz, 5 GHz, and 700 MHz. For example, the simulation unit simulates the propagation status of radio waves in the 2.4 GHz band. The simulation unit can also simulate the propagation status of radio waves in the 5 GHz band. The simulation unit can also simulate the propagation status of radio waves in the 700 MHz band. In this way, by simulating the propagation status of radio waves in each frequency band, it is possible to grasp the radio wave status in more detail.

[0031] The providing unit can visually display the simulation results on a map. The providing unit visually displays the simulation results on a map such as Google (registered trademark) Maps or OpenStreetMap. For example, the providing unit displays the simulation results on a map using Google Maps. The providing unit can also display the simulation results on a map using OpenStreetMap. By visually displaying the simulation results on a map, the user can intuitively understand the radio wave conditions.

[0032] The identification unit can identify the location of the existing wireless communication equipment by referring to a database or by using GPS information. The identification unit identifies the location of the existing wireless communication equipment by referring to the database using, for example, an SQL query or API access. For example, the identification unit references the database using an SQL query. The identification unit can also reference the database using API access. The identification unit can also identify the location of the existing wireless communication equipment by using a GPS module or a smartphone location information service. For example, the identification unit acquires location information using a GPS module. The identification unit can also acquire location information by using a smartphone location information service. As a result, the location of the existing wireless communication equipment can be identified with high accuracy by referring to the database or using GPS information.

[0033] The simulation unit can calculate the numerical value of the radio wave strength. The simulation unit calculates the numerical value of the radio wave strength in units such as dBm or mW. For example, the simulation unit calculates the radio wave strength in units of dBm. The simulation unit can also calculate the radio wave strength in units of mW. In this way, by calculating the numerical value of the radio wave strength, the radio wave situation can be quantitatively evaluated.

[0034] The providing unit can provide the user with the signal strength and the range of the coverage area. The providing unit provides the signal strength and the range of the coverage area, for example, by displaying an area on a map or as numerical data. For example, the providing unit displays an area of ​​signal strength on a map. The providing unit can also provide the signal strength as numerical data. By providing the signal strength and the range of the coverage area, the user can understand the signal conditions in detail.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit analyzes the user's past input history, for example, by analyzing log data or using a machine learning algorithm. For example, the reception unit analyzes the user's past input history by analyzing log data. The reception unit can also analyze the user's past input history using a machine learning algorithm. Furthermore, the reception unit suggests the optimal input method based on the analysis results. For example, the reception unit can automatically display addresses and latitude / longitude that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest addresses and latitude / longitude to be used in a specific time period based on the user's past input history. In this way, the reception unit can suggest the optimal input method to the user by analyzing the past input history.

[0036] The reception unit can automatically acquire the user's current location information and assist with input when entering an address or latitude and longitude. The reception unit acquires the user's current location information using, for example, GPS or a Wi-Fi location information service. For example, the reception unit acquires the user's current location information using GPS. The reception unit can also acquire the user's current location information using a Wi-Fi location information service. The reception unit then assists with input of the address and latitude and longitude based on the acquired location information. For example, when the user opens the app, the current location can be automatically acquired, simplifying the input of the address and latitude and longitude. Furthermore, when the user enters a destination, the app can suggest optimal candidate locations taking into account the distance from the current location. Furthermore, when the user uses the app while on the move, the current location can be updated in real time to assist with input of the address and latitude and longitude. Thus, automatically acquiring the current location information simplifies input and reduces the burden on the user.

[0037] The reception unit can provide an appropriate input method when inputting an address or latitude and longitude by taking into consideration device information of the user. The reception unit provides an input method by taking into consideration device information such as the type of device, OS, and browser. For example, if the user is using a smartphone, the reception unit can provide an input method optimized for touch operation. Furthermore, if the user is using a tablet, the reception unit can also provide an input method optimized for a large screen. Furthermore, if the user is using a desktop, the reception unit can also provide an input method optimized for keyboard input. In this way, the optimal input method can be provided to the user by taking into consideration device information.

[0038] The reception unit can analyze the user's social media activity when entering an address or latitude and longitude, and automatically complete related information. The reception unit analyzes social media activity, such as the content of posts, the number of likes, and the number of followers. For example, the reception unit automatically completes the address and latitude and longitude based on the location where the user checked in on social media. The reception unit can also assist with input based on location information shared by the user on social media. The reception unit can also predict frequently visited places from the user's social media activity and simplify input. In this way, analyzing social media activity simplifies input and reduces the burden on the user.

[0039] When referencing a database, the identification unit can select an optimal identification method based on past reference history. The identification unit analyzes the past reference history, for example, by analyzing log data or using a machine learning algorithm. For example, the identification unit analyzes the past reference history by analyzing log data. The identification unit can also analyze the past reference history using a machine learning algorithm. Furthermore, the identification unit selects an optimal identification method based on the analysis results. For example, the identification unit can prioritize the use of databases that the user has frequently referenced in the past. The identification unit can also suggest the most efficient identification method based on the user's past reference history. The identification unit can also select an optimal method based on an identification method that the user has used in the past. In this way, by selecting an optimal identification method based on the past reference history, identification accuracy is improved.

[0040] When using GPS information, the identification unit can update the location information in real time to improve identification accuracy. The identification unit updates the location information in real time, for example, by streaming GPS data or using a real-time API. For example, the identification unit updates the location information in real time by streaming GPS data. The identification unit can also update the location information using a real-time API. The identification unit further improves identification accuracy based on the location information updated in real time. For example, the GPS information can be updated in real time while the user is moving to improve location identification accuracy. The GPS information can also be updated in real time when the user arrives at a specific location to improve location identification accuracy. If the user gets lost, the GPS information can be updated in real time and the location can be identified again. In this way, updating the location information in real time improves identification accuracy.

[0041] The identification unit can improve the identification accuracy by taking into account the user's geographical location information when identifying the location of existing wireless communication equipment. The identification unit considers the user's geographical location information by using, for example, GPS data or map data. For example, the identification unit acquires the user's geographical location information using GPS data. The identification unit can also acquire the user's geographical location information using map data. The identification unit further improves the identification accuracy based on the acquired geographical location information. For example, the location of the nearest wireless communication equipment is identified based on the user's current location. Corrections can also be made to improve the identification accuracy based on the user's geographical location information. The user's geographical location information can also be updated in real time to improve the identification accuracy. In this way, the identification accuracy is improved by taking the geographical location information into account.

[0042] The identification unit can improve the identification accuracy by referring to related literature information when identifying the location of existing wireless communication equipment. The identification unit references the related literature information by, for example, a database search or API access. For example, the identification unit references the related literature information using a database search. The identification unit can also reference the related literature information using API access. Furthermore, the identification unit improves the identification accuracy based on the referenced literature information. For example, the accuracy of identifying the location of the wireless communication equipment is improved based on the related literature information. The location can also be corrected based on data obtained from the literature information. The optimal location method can also be selected by referring to the literature information. In this way, the identification accuracy is improved by referring to the literature information.

[0043] During a simulation, the simulation unit can improve accuracy by referring to past simulation results. The simulation unit refers to past simulation results by, for example, database search or log data analysis. For example, the simulation unit refers to past simulation results using a database search. The simulation unit can also refer to past simulation results by analyzing log data. Furthermore, the simulation unit improves the accuracy of the current simulation based on the referred past simulation results. For example, the accuracy of the current simulation is improved based on the past simulation results. Simulation parameters can also be adjusted based on data obtained from the past simulation results. The optimal simulation method can also be selected by referring to the past simulation results. In this way, the accuracy of the simulation is improved by referring to the past simulation results.

[0044] The simulation unit acquires environmental data in real time when simulating the propagation conditions of radio waves in each frequency band, thereby improving the accuracy of the simulation. The simulation unit acquires real-time environmental data using methods such as sensor data and weather data. For example, the simulation unit simulates the radio wave propagation conditions based on real-time weather data. The simulation unit can also simulate the radio wave propagation conditions based on real-time topographical data. The simulation unit can also simulate the radio wave propagation conditions based on real-time building data. In this way, acquiring environmental data in real time improves the accuracy of the simulation.

[0045] The simulation unit can improve the accuracy of the simulation by taking into account the user's geographical location information during the simulation. The simulation unit can consider the user's geographical location information by using, for example, GPS data or map data. For example, the simulation unit acquires the user's geographical location information using GPS data. The simulation unit can also acquire the user's geographical location information using map data. The simulation unit can then improve the accuracy of the simulation based on the acquired geographical location information. For example, the simulation unit can simulate the radio wave propagation conditions of the nearest wireless communication equipment based on the user's current location. The simulation unit can also make corrections to improve the accuracy of the simulation based on the user's geographical location information. The simulation accuracy can also be improved by updating the user's geographical location information in real time. In this way, the accuracy of the simulation is improved by taking the geographical location information into account.

[0046] The simulation unit can improve the accuracy of the simulation by referring to related literature information during the simulation. The simulation unit refers to the related literature information by, for example, a database search or API access. For example, the simulation unit refers to the related literature information by using a database search. The simulation unit can also refer to the related literature information by using API access. Furthermore, the simulation unit improves the accuracy of the simulation based on the referenced literature information. For example, the simulation unit improves the accuracy of the simulation of the radio wave propagation situation based on the related literature information. The simulation parameters can also be adjusted based on data obtained from the literature information. The literature information can also be referenced to select an optimal simulation method. In this way, the accuracy of the simulation is improved by referring to the literature information.

[0047] When visually displaying the simulation results on a map, the providing unit can select an optimal display method by referring to the user's past operation history. The providing unit analyzes the user's past operation history, for example, by analyzing log data or using a machine learning algorithm. For example, the providing unit analyzes the user's past operation history by analyzing log data. The providing unit can also analyze the user's past operation history using a machine learning algorithm. Furthermore, the providing unit selects an optimal display method based on the analysis results. For example, the providing unit provides an optimal display method based on display methods used by the user in the past. It can also select a display method with high visibility from the user's past operation history. It can also provide a customized display method based on display methods that the user has preferred in the past. In this way, it is possible to provide an optimal display method to the user by referring to the past operation history.

[0048] When providing simulation results, the providing unit can customize the display content taking into account the user's current location information. The providing unit acquires the user's current location information, for example, using a method such as GPS or a Wi-Fi location information service. For example, the providing unit acquires the user's current location information using GPS. The providing unit can also acquire the user's current location information using a Wi-Fi location information service. Furthermore, the providing unit customizes the display content of the simulation results based on the acquired location information. For example, the most relevant simulation results can be displayed based on the user's current location. The display content can also be customized taking into account the distance from the user's current location. The user's current location can also be updated in real time to dynamically adjust the display content. This makes it possible to provide the user with optimal display content by taking into account the current location information.

[0049] When providing the simulation results, the providing unit can provide the optimal display method by taking into account device information of the user. The providing unit provides the display method by taking into account device information such as the device type, OS, and browser. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can also provide a high-resolution display method. In this way, the optimal display method can be provided to the user by taking into account device information.

[0050] When providing simulation results, the providing unit can analyze the user's social media activity and display related information. The providing unit analyzes social media activity, such as the content of posts, the number of likes, and the number of followers. For example, the providing unit displays related simulation results based on location information shared by the user on social media. The providing unit can also display information related to frequently visited places based on the user's social media activity. The providing unit can also analyze the user's social media activity and display information that is of most interest to the user. In this way, information related to the user can be provided by analyzing social media activity.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception unit can analyze the user's input and automatically suggest related past simulation results based on the input. For example, if the user inputs a specific address or latitude and longitude, the results of simulations conducted in the past at the same location can be displayed. Also, if the user specifies a specific frequency band, past simulation results for that frequency band can be displayed preferentially. Furthermore, if the user inputs a specific route, past simulation results along that route can be suggested. In this way, by utilizing past simulation results, optimal information can be provided based on the user's input.

[0053] The simulation unit can dynamically update simulation results based on environmental data acquired in real time. For example, it can acquire weather data and simulate radio wave propagation conditions in rainy and sunny weather. It can also acquire traffic volume data and perform simulations that take into account the radio wave shielding effect of moving vehicles. It can also acquire building construction status data and perform simulations that take into account the reflection and shielding of radio waves by newly constructed buildings. In this way, by utilizing real-time environmental data, it is possible to provide simulation results that are more realistic.

[0054] The identification unit can analyze the user's past location information and suggest the optimal location of wireless communication equipment. For example, the identification unit can identify the optimal location of wireless communication equipment based on places the user has frequently visited in the past. The identification unit can also suggest the optimal location based on location information of wireless communication equipment used by the user in the past. Furthermore, the identification unit can analyze the user's past movement patterns and predict the optimal location of wireless communication equipment. In this way, by utilizing past location information, the identification unit can suggest the optimal location of wireless communication equipment to the user.

[0055] The providing unit can provide the optimal display format by taking into account the device information of the user. For example, if the user is using a smartphone, a display format that matches the screen size can be provided. Also, if the user is using a tablet, a display format optimized for a large screen can be provided. Furthermore, if the user is using a desktop, a high-resolution display format can be provided. In this way, the optimal display format can be provided to the user by taking into account the device information.

[0056] The simulation unit can improve the accuracy of a simulation by referring to past simulation results. For example, past simulation results can be referenced by database search, log data analysis, or other methods. The accuracy of the current simulation can also be improved based on data obtained from past simulation results. Furthermore, the optimal simulation method can be selected based on past simulation results. In this way, the accuracy of the simulation can be improved by referring to past simulation results.

[0057] When providing simulation results, the providing unit can analyze the user's social media activity and display related information. For example, it analyzes social media activity such as the content of posts, the number of likes, and the number of followers. It displays related simulation results based on location information shared by the user on social media. It can also display information related to frequently visited places based on the user's social media activity. It can also analyze the user's social media activity and display information that is of most interest to the user. In this way, it is possible to provide information relevant to the user by analyzing social media activity.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit receives an input of an address or latitude and longitude. For example, the reception unit receives an address or latitude and longitude input by a user. Step 2: The identifying unit identifies the location of the existing wireless communication equipment based on the information received by the receiving unit. For example, the identifying unit identifies the location of the existing wireless communication equipment by referring to a database or by using GPS information. Step 3: The simulation unit performs a simulation using a radio wave propagation model based on the location information identified by the identification unit, such as a free space model or a Rayleigh fading model. Step 4: The providing unit visually provides the simulation results obtained by the simulation unit, for example, by using a graph, a heat map, a 3D model, or the like.

[0060] (Example 2) A wireless communication simulation system according to an embodiment of the present invention simulates the radio wave conditions of existing wireless communication equipment at each frequency with high accuracy by inputting an address and latitude and longitude. The system begins with a user inputting the address and latitude and longitude. Next, the system identifies the location of the existing wireless communication equipment based on the input information. Furthermore, the system simulates the radio wave propagation conditions in each frequency band based on this location information. The simulation results are visually presented to the user, allowing them to check the radio wave strength and coverage area. This system enables efficient design and optimization of wireless communication. For example, when a user inputs an address and latitude and longitude, they only need to input a starting point and a destination. For example, they might input "I want to go from my home to the train station." This information is entered into the system. Next, the system analyzes the input information and identifies the location of the existing wireless communication equipment. The system identifies the location of the existing wireless communication equipment by referencing a database or using GPS information. Furthermore, the system uses a radio wave propagation model to simulate the radio wave propagation conditions in each frequency band. For example, if a user inputs a route from their home to the train station, the system simulates the radio wave strength and coverage area along that route. Simulation results are provided to the user visually. For example, radio wave strength values ​​and coverage area ranges are displayed on a map. This allows the user to intuitively understand the radio wave conditions. This system enables efficient design and optimization of wireless communications. For example, when installing new wireless communication equipment, the optimal installation location can be identified. In addition, the coverage area of ​​existing wireless communication equipment can be confirmed and adjustments made as necessary. This allows the wireless communication simulation system to efficiently design and optimize wireless communications.

[0061] A wireless communication simulation system according to an embodiment includes a receiving unit, an identifying unit, a simulation unit, and a providing unit. The receiving unit receives an input of an address or latitude and longitude. The input of the address or latitude and longitude includes, but is not limited to, an address format and a notation method for latitude and longitude. For example, the receiving unit receives the address and latitude and longitude input by a user. The identifying unit identifies the location of the existing wireless communication equipment based on the information received by the receiving unit. The identifying unit identifies the location of the existing wireless communication equipment by, for example, referring to a database or using GPS information. For example, the identifying unit identifies the location of the existing wireless communication equipment by referring to a database. The identifying unit can also identify the location of the existing wireless communication equipment by using GPS information. The simulation unit performs a simulation using a radio wave propagation model based on the location information identified by the identifying unit. The simulation unit performs a simulation using a radio wave propagation model, such as a free space model or a Rayleigh fading model. For example, the simulation unit simulates the propagation status of radio waves using the free space model. The simulation unit can also simulate the propagation status of radio waves using the Rayleigh fading model. The providing unit visually provides the simulation results obtained by the simulation unit. The providing unit visually provides the simulation results using methods such as graphs, heat maps, and 3D models. For example, the providing unit displays the simulation results as graphs. The providing unit can also display the simulation results as heat maps. The providing unit can also display the simulation results as 3D models. As a result, the wireless communication simulation system according to the embodiment can simulate the radio wave conditions of wireless communication equipment with high accuracy based on input of addresses and latitude and longitude, and visually provide the simulation results.

[0062] The simulation unit can simulate the propagation status of radio waves in each frequency band. The simulation unit simulates the propagation status of radio waves in each frequency band, such as 2.4 GHz, 5 GHz, and 700 MHz. For example, the simulation unit simulates the propagation status of radio waves in the 2.4 GHz band. The simulation unit can also simulate the propagation status of radio waves in the 5 GHz band. The simulation unit can also simulate the propagation status of radio waves in the 700 MHz band. In this way, by simulating the propagation status of radio waves in each frequency band, it is possible to grasp the radio wave status in more detail.

[0063] The providing unit can visually display the simulation results on a map. The providing unit visually displays the simulation results on a map such as Google Maps or OpenStreetMap. For example, the providing unit displays the simulation results on a map using Google Maps. The providing unit can also display the simulation results on a map using OpenStreetMap. By visually displaying the simulation results on a map, the user can intuitively understand the radio wave conditions.

[0064] The identification unit can identify the location of the existing wireless communication equipment by referring to a database or by using GPS information. The identification unit identifies the location of the existing wireless communication equipment by referring to the database using, for example, an SQL query or API access. For example, the identification unit references the database using an SQL query. The identification unit can also reference the database using API access. The identification unit can also identify the location of the existing wireless communication equipment by using a GPS module or a smartphone location information service. For example, the identification unit acquires location information using a GPS module. The identification unit can also acquire location information by using a smartphone location information service. As a result, the location of the existing wireless communication equipment can be identified with high accuracy by referring to the database or using GPS information.

[0065] The simulation unit can calculate the numerical value of the radio wave strength. The simulation unit calculates the numerical value of the radio wave strength in units such as dBm or mW. For example, the simulation unit calculates the radio wave strength in units of dBm. The simulation unit can also calculate the radio wave strength in units of mW. In this way, by calculating the numerical value of the radio wave strength, the radio wave situation can be quantitatively evaluated.

[0066] The providing unit can provide the user with the signal strength and the range of the coverage area. The providing unit provides the signal strength and the range of the coverage area, for example, by displaying an area on a map or as numerical data. For example, the providing unit displays an area of ​​signal strength on a map. The providing unit can also provide the signal strength as numerical data. By providing the signal strength and the range of the coverage area, the user can understand the signal conditions in detail.

[0067] The reception unit can estimate the user's emotion and adjust the input interface for addresses and latitude and longitude based on the estimated user's emotion. The reception unit estimates the user's emotion using methods such as facial expression recognition and voice analysis. For example, the reception unit estimates the user's emotion using facial expression recognition technology. The reception unit can also estimate the user's emotion using voice analysis technology. Furthermore, the reception unit adjusts the input interface for addresses and latitude and longitude based on the estimated user's emotion. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Alternatively, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of addresses and latitude and longitude. In this way, the input interface can be adjusted according to the user's emotion, improving the user's input experience.

[0068] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit analyzes the user's past input history, for example, by analyzing log data or using a machine learning algorithm. For example, the reception unit analyzes the user's past input history by analyzing log data. The reception unit can also analyze the user's past input history using a machine learning algorithm. Furthermore, the reception unit suggests the optimal input method based on the analysis results. For example, the reception unit can automatically display addresses and latitude / longitude that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest addresses and latitude / longitude to be used in a specific time period based on the user's past input history. In this way, the reception unit can suggest the optimal input method to the user by analyzing the past input history.

[0069] The reception unit can automatically acquire the user's current location information and assist with input when entering an address or latitude and longitude. The reception unit acquires the user's current location information using, for example, GPS or a Wi-Fi location information service. For example, the reception unit acquires the user's current location information using GPS. The reception unit can also acquire the user's current location information using a Wi-Fi location information service. The reception unit then assists with input of the address and latitude and longitude based on the acquired location information. For example, when the user opens the app, the current location can be automatically acquired, simplifying the input of the address and latitude and longitude. Furthermore, when the user enters a destination, the app can suggest optimal candidate locations taking into account the distance from the current location. Furthermore, when the user uses the app while on the move, the current location can be updated in real time to assist with input of the address and latitude and longitude. Thus, automatically acquiring the current location information simplifies input and reduces the burden on the user.

[0070] The reception unit can estimate the user's emotion and determine the priority of inputs based on the estimated user's emotion. The reception unit estimates the user's emotion using methods such as facial expression recognition and voice analysis. For example, the reception unit estimates the user's emotion using facial expression recognition technology. The reception unit can also estimate the user's emotion using voice analysis technology. Furthermore, the reception unit determines the priority of inputs based on the estimated user's emotion. For example, if the user is nervous, important input items can be displayed first, and other items can be postponed. If the user is relaxed, all input items can be displayed at once, allowing the user to enter input freely. If the user is in a hurry, only the most important input items can be displayed, allowing the user to complete input quickly. In this way, the input priority can be determined according to the user's emotion, thereby improving the user's input experience.

[0071] The reception unit can provide an appropriate input method when inputting an address or latitude and longitude by taking into consideration device information of the user. The reception unit provides an input method by taking into consideration device information such as the type of device, OS, and browser. For example, if the user is using a smartphone, the reception unit can provide an input method optimized for touch operation. Furthermore, if the user is using a tablet, the reception unit can also provide an input method optimized for a large screen. Furthermore, if the user is using a desktop, the reception unit can also provide an input method optimized for keyboard input. In this way, the optimal input method can be provided to the user by taking into consideration device information.

[0072] The reception unit can analyze the user's social media activity when entering an address or latitude and longitude, and automatically complete related information. The reception unit analyzes social media activity, such as the content of posts, the number of likes, and the number of followers. For example, the reception unit automatically completes the address and latitude and longitude based on the location where the user checked in on social media. The reception unit can also assist with input based on location information shared by the user on social media. The reception unit can also predict frequently visited places from the user's social media activity and simplify input. In this way, analyzing social media activity simplifies input and reduces the burden on the user.

[0073] The identification unit can estimate a user's emotion and adjust a location determination method of the existing wireless communication equipment based on the estimated user's emotion. The identification unit estimates the user's emotion using methods such as facial expression recognition and voice analysis. For example, the identification unit can estimate the user's emotion using facial expression recognition technology. The identification unit can also estimate the user's emotion using voice analysis technology. Furthermore, the identification unit adjusts a location determination method of the existing wireless communication equipment based on the estimated user's emotion. For example, if the user is stressed, the identification unit can provide a simple location determination method to minimize steps. If the user is relaxed, the identification unit can provide detailed location determination options and suggest a customizable method. If the user is in a hurry, the identification unit can provide a simplified method for quick location determination. In this way, the location determination method can be adjusted according to the user's emotion, improving the user experience.

[0074] When referencing a database, the identification unit can select an optimal identification method based on past reference history. The identification unit analyzes the past reference history, for example, by analyzing log data or using a machine learning algorithm. For example, the identification unit analyzes the past reference history by analyzing log data. The identification unit can also analyze the past reference history using a machine learning algorithm. Furthermore, the identification unit selects an optimal identification method based on the analysis results. For example, the identification unit can prioritize the use of databases that the user has frequently referenced in the past. The identification unit can also suggest the most efficient identification method based on the user's past reference history. The identification unit can also select an optimal method based on an identification method that the user has used in the past. In this way, by selecting an optimal identification method based on the past reference history, identification accuracy is improved.

[0075] When using GPS information, the identification unit can update the location information in real time to improve identification accuracy. The identification unit updates the location information in real time, for example, by streaming GPS data or using a real-time API. For example, the identification unit updates the location information in real time by streaming GPS data. The identification unit can also update the location information using a real-time API. The identification unit further improves identification accuracy based on the location information updated in real time. For example, the GPS information can be updated in real time while the user is moving to improve location identification accuracy. The GPS information can also be updated in real time when the user arrives at a specific location to improve location identification accuracy. If the user gets lost, the GPS information can be updated in real time and the location can be identified again. In this way, updating the location information in real time improves identification accuracy.

[0076] The identification unit can estimate the user's emotion and adjust the display method of the identification result based on the estimated user's emotion. The identification unit estimates the user's emotion using methods such as facial expression recognition and voice analysis. For example, the identification unit estimates the user's emotion using facial expression recognition technology. The identification unit can also estimate the user's emotion using voice analysis technology. Furthermore, the identification unit adjusts the display method of the identification result based on the estimated user's emotion. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the user's experience is improved by adjusting the display method according to the user's emotion.

[0077] The identification unit can improve the identification accuracy by taking into account the user's geographical location information when identifying the location of existing wireless communication equipment. The identification unit considers the user's geographical location information by using, for example, GPS data or map data. For example, the identification unit acquires the user's geographical location information using GPS data. The identification unit can also acquire the user's geographical location information using map data. The identification unit further improves the identification accuracy based on the acquired geographical location information. For example, the location of the nearest wireless communication equipment is identified based on the user's current location. Corrections can also be made to improve the identification accuracy based on the user's geographical location information. The user's geographical location information can also be updated in real time to improve the identification accuracy. In this way, the identification accuracy is improved by taking the geographical location information into account.

[0078] The identification unit can improve the identification accuracy by referring to related literature information when identifying the location of existing wireless communication equipment. The identification unit references the related literature information by, for example, a database search or API access. For example, the identification unit references the related literature information using a database search. The identification unit can also reference the related literature information using API access. Furthermore, the identification unit improves the identification accuracy based on the referenced literature information. For example, the accuracy of identifying the location of the wireless communication equipment is improved based on the related literature information. The location can also be corrected based on data obtained from the literature information. The optimal location method can also be selected by referring to the literature information. In this way, the identification accuracy is improved by referring to the literature information.

[0079] The simulation unit can estimate the user's emotions and adjust simulation parameters based on the estimated user's emotions. The simulation unit estimates the user's emotions using methods such as facial expression recognition and voice analysis. For example, the simulation unit estimates the user's emotions using facial expression recognition technology. The simulation unit can also estimate the user's emotions using voice analysis technology. Furthermore, the simulation unit adjusts the simulation parameters based on the estimated user's emotions. For example, if the user is relaxed, detailed simulation parameters can be provided. If the user is in a hurry, simplified simulation parameters can be provided. If the user is excited, visually stimulating simulation parameters can be provided. In this way, the user's experience can be improved by adjusting the simulation parameters according to the user's emotions.

[0080] During a simulation, the simulation unit can improve accuracy by referring to past simulation results. The simulation unit refers to past simulation results by, for example, database search or log data analysis. For example, the simulation unit refers to past simulation results using a database search. The simulation unit can also refer to past simulation results by analyzing log data. Furthermore, the simulation unit improves the accuracy of the current simulation based on the referred past simulation results. For example, the accuracy of the current simulation is improved based on the past simulation results. Simulation parameters can also be adjusted based on data obtained from the past simulation results. The optimal simulation method can also be selected by referring to the past simulation results. In this way, the accuracy of the simulation is improved by referring to the past simulation results.

[0081] The simulation unit acquires environmental data in real time when simulating the propagation conditions of radio waves in each frequency band, thereby improving the accuracy of the simulation. The simulation unit acquires real-time environmental data using methods such as sensor data and weather data. For example, the simulation unit simulates the radio wave propagation conditions based on real-time weather data. The simulation unit can also simulate the radio wave propagation conditions based on real-time topographical data. The simulation unit can also simulate the radio wave propagation conditions based on real-time building data. In this way, acquiring environmental data in real time improves the accuracy of the simulation.

[0082] The simulation unit can estimate the user's emotions and adjust the display method of the simulation results based on the estimated user's emotions. The simulation unit estimates the user's emotions using methods such as facial expression recognition and voice analysis. For example, the simulation unit estimates the user's emotions using facial expression recognition technology. The simulation unit can also estimate the user's emotions using voice analysis technology. Furthermore, the simulation unit adjusts the display method of the simulation results based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the user's experience is improved by adjusting the display method according to the user's emotions.

[0083] The simulation unit can improve the accuracy of the simulation by taking into account the user's geographical location information during the simulation. The simulation unit can consider the user's geographical location information by using, for example, GPS data or map data. For example, the simulation unit acquires the user's geographical location information using GPS data. The simulation unit can also acquire the user's geographical location information using map data. The simulation unit can then improve the accuracy of the simulation based on the acquired geographical location information. For example, the simulation unit can simulate the radio wave propagation conditions of the nearest wireless communication equipment based on the user's current location. The simulation unit can also make corrections to improve the accuracy of the simulation based on the user's geographical location information. The simulation accuracy can also be improved by updating the user's geographical location information in real time. In this way, the accuracy of the simulation is improved by taking the geographical location information into account.

[0084] The simulation unit can improve the accuracy of the simulation by referring to related literature information during the simulation. The simulation unit refers to the related literature information by, for example, a database search or API access. For example, the simulation unit refers to the related literature information by using a database search. The simulation unit can also refer to the related literature information by using API access. Furthermore, the simulation unit improves the accuracy of the simulation based on the referenced literature information. For example, the simulation unit improves the accuracy of the simulation of the radio wave propagation situation based on the related literature information. The simulation parameters can also be adjusted based on data obtained from the literature information. The literature information can also be referenced to select an optimal simulation method. In this way, the accuracy of the simulation is improved by referring to the literature information.

[0085] The providing unit can estimate the user's emotions and adjust the display method of the simulation results based on the estimated user's emotions. The providing unit estimates the user's emotions using methods such as facial expression recognition and voice analysis. For example, the providing unit estimates the user's emotions using facial expression recognition technology. The providing unit can also estimate the user's emotions using voice analysis technology. Furthermore, the providing unit adjusts the display method of the simulation results based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the user's experience is improved by adjusting the display method according to the user's emotions.

[0086] When visually displaying the simulation results on a map, the providing unit can select an optimal display method by referring to the user's past operation history. The providing unit analyzes the user's past operation history, for example, by analyzing log data or using a machine learning algorithm. For example, the providing unit analyzes the user's past operation history by analyzing log data. The providing unit can also analyze the user's past operation history using a machine learning algorithm. Furthermore, the providing unit selects an optimal display method based on the analysis results. For example, the providing unit provides an optimal display method based on display methods used by the user in the past. It can also select a display method with high visibility from the user's past operation history. It can also provide a customized display method based on display methods that the user has preferred in the past. In this way, it is possible to provide an optimal display method to the user by referring to the past operation history.

[0087] When providing simulation results, the providing unit can customize the display content taking into account the user's current location information. The providing unit acquires the user's current location information, for example, using a method such as GPS or a Wi-Fi location information service. For example, the providing unit acquires the user's current location information using GPS. The providing unit can also acquire the user's current location information using a Wi-Fi location information service. Furthermore, the providing unit customizes the display content of the simulation results based on the acquired location information. For example, the most relevant simulation results can be displayed based on the user's current location. The display content can also be customized taking into account the distance from the user's current location. The user's current location can also be updated in real time to dynamically adjust the display content. This makes it possible to provide the user with optimal display content by taking into account the current location information.

[0088] The providing unit can estimate the user's emotions and prioritize the simulation results based on the estimated user's emotions. The providing unit estimates the user's emotions using methods such as facial expression recognition and voice analysis. For example, the providing unit estimates the user's emotions using facial expression recognition technology. The providing unit can also estimate the user's emotions using voice analysis technology. Furthermore, the providing unit prioritizes the simulation results based on the estimated user's emotions. For example, if the user is nervous, important simulation results can be displayed preferentially. If the user is relaxed, all simulation results can be displayed at once so that the user can freely select them. If the user is in a hurry, only the most important simulation results can be displayed so that the user can quickly check them. This improves the user experience by determining priorities according to the user's emotions.

[0089] When providing the simulation results, the providing unit can provide the optimal display method by taking into account device information of the user. The providing unit provides the display method by taking into account device information such as the device type, OS, and browser. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can also provide a high-resolution display method. In this way, the optimal display method can be provided to the user by taking into account device information.

[0090] When providing simulation results, the providing unit can analyze the user's social media activity and display related information. The providing unit analyzes social media activity, such as the content of posts, the number of likes, and the number of followers. For example, the providing unit displays related simulation results based on location information shared by the user on social media. The providing unit can also display information related to frequently visited places based on the user's social media activity. The providing unit can also analyze the user's social media activity and display information that is of most interest to the user. In this way, information related to the user can be provided by analyzing social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, identification unit, simulation unit, and providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives an address and latitude and longitude input by a user. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the location of existing wireless communication equipment by referring to a database or using GPS information. The simulation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and performs a simulation using a radio wave propagation model. The providing unit is realized, for example, by the control unit 46A of the smart device 14 and visually provides the simulation results. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, identification unit, simulation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives an address and latitude and longitude input by a user. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the location of existing wireless communication equipment by referencing a database or using GPS information. The simulation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and performs a simulation using a radio wave propagation model. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and visually provides the simulation results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, identification unit, simulation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives an address and latitude and longitude input by a user. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the location of existing wireless communication equipment by referring to a database or by using GPS information. The simulation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and performs a simulation using a radio wave propagation model. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and visually provides the simulation results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, identification unit, simulation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives an address and latitude and longitude input by a user. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the location of existing wireless communication equipment by referring to a database or using GPS information. The simulation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and performs a simulation using a radio wave propagation model. The provision unit is realized, for example, by the control unit 46A of the robot 414 and visually provides the simulation results.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The reception unit can analyze the user's input and automatically suggest related past simulation results based on the input. For example, if the user inputs a specific address or latitude and longitude, the results of simulations conducted in the past at the same location can be displayed. Also, if the user specifies a specific frequency band, past simulation results for that frequency band can be displayed preferentially. Furthermore, if the user inputs a specific route, past simulation results along that route can be suggested. In this way, by utilizing past simulation results, optimal information can be provided based on the user's input.

[0093] The simulation unit can dynamically update simulation results based on environmental data acquired in real time. For example, it can acquire weather data and simulate radio wave propagation conditions in rainy and sunny weather. It can also acquire traffic volume data and perform simulations that take into account the radio wave shielding effect of moving vehicles. It can also acquire building construction status data and perform simulations that take into account the reflection and shielding of radio waves by newly constructed buildings. In this way, by utilizing real-time environmental data, it is possible to provide simulation results that are more realistic.

[0094] The providing unit can estimate the user's emotions and customize the display format of the simulation results based on the estimated user's emotions. For example, if the user is excited, a display format using visually stimulating colors and animations can be provided. If the user is relaxed, a display format using calming colors and a simple display format can be provided. Furthermore, if the user is stressed, a display format that emphasizes important information and omits unnecessary information can be provided. In this way, the user's experience is improved by providing a display format that corresponds to the user's emotions.

[0095] The identification unit can analyze the user's past location information and suggest the optimal location of wireless communication equipment. For example, the identification unit can identify the optimal location of wireless communication equipment based on places the user has frequently visited in the past. The identification unit can also suggest the optimal location based on location information of wireless communication equipment used by the user in the past. Furthermore, the identification unit can analyze the user's past movement patterns and predict the optimal location of wireless communication equipment. In this way, by utilizing past location information, the identification unit can suggest the optimal location of wireless communication equipment to the user.

[0096] The simulation unit can estimate the user's emotions and adjust the level of detail of the simulation based on the estimated user's emotions. For example, if the user is relaxed, a detailed simulation result can be provided. If the user is in a hurry, a simplified simulation result can be provided. Furthermore, if the user is excited, a visually appealing simulation result can be provided. In this way, the user's experience can be improved by providing simulation results according to the user's emotions.

[0097] The providing unit can provide the optimal display format by taking into account the device information of the user. For example, if the user is using a smartphone, a display format that matches the screen size can be provided. Also, if the user is using a tablet, a display format optimized for a large screen can be provided. Furthermore, if the user is using a desktop, a high-resolution display format can be provided. In this way, the optimal display format can be provided to the user by taking into account the device information.

[0098] The reception unit can estimate the user's emotions and determine the priority of input based on the estimated user's emotions. For example, if the user is nervous, important input items can be displayed first, and other items can be postponed. Also, if the user is relaxed, all input items can be displayed at once, allowing the user to input freely. Also, if the user is in a hurry, only the most important input items can be displayed, allowing the user to complete input quickly. In this way, the input priority can be determined according to the user's emotions, thereby improving the user's input experience.

[0099] The identification unit can estimate the user's emotions and adjust the location method of the existing wireless communication equipment based on the estimated user emotions. For example, if the user is stressed, a simple location method can be provided to minimize the steps. Alternatively, if the user is relaxed, detailed location options can be provided and a customizable method can be suggested. Alternatively, if the user is in a hurry, a simplified method for quick location can be provided. In this way, the user's experience can be improved by adjusting the location method according to the user's emotions.

[0100] The simulation unit can improve the accuracy of a simulation by referring to past simulation results. For example, past simulation results can be referenced by database search, log data analysis, or other methods. The accuracy of the current simulation can also be improved based on data obtained from past simulation results. Furthermore, the optimal simulation method can be selected based on past simulation results. In this way, the accuracy of the simulation can be improved by referring to past simulation results.

[0101] When providing simulation results, the providing unit can analyze the user's social media activity and display related information. For example, it analyzes social media activity such as the content of posts, the number of likes, and the number of followers. It displays related simulation results based on location information shared by the user on social media. It can also display information related to frequently visited places based on the user's social media activity. It can also analyze the user's social media activity and display information that is of most interest to the user. In this way, it is possible to provide information relevant to the user by analyzing social media activity.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The reception unit receives an input of an address or latitude and longitude. For example, the reception unit receives an address or latitude and longitude input by a user. Step 2: The identifying unit identifies the location of the existing wireless communication equipment based on the information received by the receiving unit. For example, the identifying unit identifies the location of the existing wireless communication equipment by referring to a database or by using GPS information. Step 3: The simulation unit performs a simulation using a radio wave propagation model based on the location information identified by the identification unit, such as a free space model or a Rayleigh fading model. Step 4: The providing unit visually provides the simulation results obtained by the simulation unit, for example, by using a graph, a heat map, a 3D model, or the like.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives an input of an address or latitude and longitude; an identifying unit that identifies a location of the existing wireless communication equipment based on the information received by the receiving unit; a simulation unit that performs a simulation using a radio wave propagation model based on the location information identified by the identification unit; a presentation unit that visually presents the simulation results obtained by the simulation unit; Equipped with A system characterized by:

2. The simulation unit Simulating radio wave propagation conditions in each frequency band The system of claim 1 .

3. The providing unit Visually displaying simulation results on a map The system of claim 1 .

4. The identification unit Identifying the location of existing wireless communication equipment by referencing a database or using GPS information The system of claim 1 .

5. The simulation unit Calculate the signal strength value The system of claim 1 .

6. The providing unit Provide users with information on signal strength and coverage area The system of claim 1 .

7. The reception unit Estimate the user's emotions and adjust the input interface for addresses and latitude and longitude based on the estimated user emotions. The system of claim 1 .

8. The reception unit Analyzes the user's past input history and suggests appropriate input methods The system of claim 1 .

Images