system

Abstract

The system according to this embodiment aims to efficiently propose and modify communication parameters in accordance with user requests. [Solution] The system according to the embodiment comprises a collection unit, a proposal unit, an explanation unit, and a creation unit. The collection unit collects information relating to the communication parameters of a base station. The proposal unit learns the information collected by the collection unit and proposes parameters according to the user's requests. The explanation unit searches for or explains the parameters proposed by the proposal unit. The creation unit creates a parameter change file based on the parameters proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the setting and change of communication parameters of a base station depend on skilled personnel, and there is a problem that efficient operation is difficult.

[0005] The system according to the embodiment aims to efficiently propose and change communication parameters according to user requirements.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, a proposal unit, an explanation unit, and a creation unit. The collection unit collects information relating to the communication parameters of a base station. The proposal unit learns the information collected by the collection unit and proposes parameters according to the user's requests. The explanation unit searches for or explains the parameters proposed by the proposal unit. The creation unit creates a parameter change file based on the parameters proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently propose and modify communication parameters in response to user requests. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.<000094>

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The communication parameter management system according to an embodiment of the present invention is a system that databases information on base station communication parameters and uses a generating AI to provide parameter suggestions, searches, and explanations according to user requests. This communication parameter management system aims to improve work efficiency and accuracy by having the generating AI automatically perform parameter verification and setting tasks that were conventionally performed manually by skilled personnel. For example, the communication parameter management system registers information on base station communication parameters in a database. This database includes past communication history, setting values, and detailed information on various parameters. Next, the generating AI learns from this database and proposes optimal parameters according to user requests. For example, it can propose optimal parameter settings under specific communication conditions. Furthermore, users can use the generating AI to search for and receive explanations of parameters. For example, the generating AI provides explanations about the meaning and setting methods of specific parameters. The system also has a function to automatically create parameter change files according to user requests. This automates parameter change tasks that were conventionally performed manually, improving work efficiency and accuracy. With this system, even inexperienced personnel can easily set and change base station communication parameters, and it is expected to change the conventional wisdom of area tuning. In the future, this will be the first step towards achieving fully automated tuning without human intervention. This will allow the communication parameter management system to streamline and improve the accuracy of setting and changing base station communication parameters.

[0029] The communication parameter management system according to the embodiment comprises a collection unit, a proposal unit, an explanation unit, and a creation unit. The collection unit collects information relating to the communication parameters of a base station. For example, the collection unit collects past communication history, setting values, and detailed information on various parameters. For example, the collection unit can collect data such as the start time, end time, and communication volume of a communication as communication history. The collection unit can also collect data such as initial setting values ​​and change history as setting values. Furthermore, the collection unit can collect detailed information on various parameters, such as detailed descriptions of parameters and related metadata. The proposal unit learns the information collected by the collection unit and proposes parameters according to the user's requests. For example, the proposal unit proposes optimal parameter settings under specific communication conditions. For example, the proposal unit can propose optimal parameters based on conditions such as the communication environment and the devices used. The explanation unit searches for or explains the parameters proposed by the proposal unit. For example, the explanation unit provides explanations about the meaning and setting methods of specific parameters. For example, the explanation unit can provide detailed explanations about parameters such as frequency, bandwidth, and transmission power. The creation unit creates a parameter change file based on the parameters proposed by the proposal unit. The creation unit can, for example, automatically create a parameter change file according to the user's request. The creation unit can, for example, create a parameter change file according to criteria such as file format and content. As a result, the communication parameter management system according to the embodiment can streamline and improve the accuracy of setting and changing communication parameters of base stations.

[0030] The data collection unit can collect past communication history, settings, and detailed information on various parameters. For example, the data collection unit can collect past communication history. For example, the data collection unit can collect data such as the start time, end time, and communication volume of communications. The data collection unit can also collect settings. For example, the data collection unit can collect data such as initial settings and change history. Furthermore, the data collection unit can also collect detailed information on various parameters. For example, the data collection unit can collect detailed descriptions of parameters and related metadata. By collecting past communication history, settings, and detailed information on various parameters, it becomes possible to make more accurate parameter suggestions. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input past communication history data into a generation AI and have the generation AI perform data collection and analysis.

[0031] The suggestion unit can propose parameter settings under specific communication conditions. For example, the suggestion unit can propose optimal parameter settings under specific communication conditions. For example, the suggestion unit can propose optimal parameters based on conditions such as the communication environment and the devices used. For example, the suggestion unit can propose parameters considering geographical conditions such as urban areas, suburbs, and mountainous areas as the communication environment. Furthermore, the suggestion unit can propose parameters considering the characteristics of devices such as smartphones, tablets, and laptops as the devices used. By proposing optimal parameter settings under specific communication conditions, the efficiency and quality of communication can be improved. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input specific communication condition data into a generation AI and have the generation AI execute a proposal for optimal parameter settings.

[0032] The explanation section can provide explanations about the meaning or setting methods of specific parameters. For example, the explanation section can provide explanations about the meaning and setting methods of specific parameters. For example, the explanation section can provide detailed explanations about parameters such as frequency, bandwidth, and transmission power. For example, as a method for setting frequency, the explanation section can explain how to select a specific frequency band or how to adjust the frequency. The explanation section can also explain as a method for setting bandwidth, how to select a specific bandwidth or how to adjust the bandwidth. Furthermore, the explanation section can also explain as a method for setting transmission power, how to select a specific transmission power or how to adjust the transmission power. This allows for a deeper understanding of the user by providing explanations about the meaning and setting methods of specific parameters. Some or all of the above processing in the explanation section may be performed using a generation AI, or it may be performed without a generation AI. For example, the explanation section can input specific parameter data into a generation AI and have the generation AI perform an explanation of the meaning and setting methods of the parameters.

[0033] The creation unit can create parameter change files according to user requests. For example, the creation unit can automatically create parameter change files according to user requests. For example, the creation unit can create parameter change files according to criteria such as file format and content. For example, if a user requests parameter changes under specific communication conditions, the creation unit can create a parameter change file based on that request. Furthermore, if a user requests changes to specific parameters, the creation unit can create a parameter change file based on that request. This enables improved work efficiency and accuracy by automatically creating parameter change files according to user requests. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit can input user request data into a generation AI and have the generation AI create the parameter change file.

[0034] The suggestion unit can propose communication settings for high traffic or settings for low power consumption. For example, the suggestion unit can propose communication settings for high traffic. For example, the suggestion unit can propose optimal communication settings for high traffic based on conditions such as communication volume thresholds and peak time periods. The suggestion unit can also propose settings for low power consumption. For example, the suggestion unit can propose optimal settings for low power consumption based on criteria such as power consumption measurement methods and power saving mode settings. By proposing optimal communication settings for high traffic and settings for low power consumption, the efficiency and quality of communication can be improved. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input high-traffic communication data or low-power consumption data into a generation AI and have the generation AI propose optimal settings.

[0035] The data collection unit can analyze past communication history and collect information on the conditions under which specific parameters function. For example, the data collection unit can analyze past communication history. For example, the data collection unit can collect the optimal parameters for a specific time period. The data collection unit can also collect the optimal parameters under specific weather conditions. Furthermore, the data collection unit can collect the optimal parameters under specific geographical conditions. This allows for the collection of optimal parameters under specific conditions by analyzing past communication history. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input past communication history data into a generative AI and have the generative AI perform data analysis and collection.

[0036] The data collection unit can monitor real-time communication status when collecting communication parameters and collect data when an anomaly occurs. For example, the data collection unit monitors real-time communication status when collecting communication parameters. For example, the data collection unit can automatically collect relevant parameters when a communication delay occurs. The data collection unit can also automatically collect affected parameters when a communication interruption occurs. Furthermore, the data collection unit can automatically collect the parameters causing the error when a communication error occurs. This enables rapid response by monitoring real-time communication status and automatically collecting data when an anomaly occurs. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input real-time communication data into a generation AI and have the generation AI perform anomaly detection and data collection.

[0037] The data collection unit can collect data while considering differences in geographical communication environments when collecting communication parameters. For example, the data collection unit can consider differences in geographical communication environments when collecting communication parameters. For example, the data collection unit can collect parameters while considering differences in communication environments between urban and suburban areas. Furthermore, the data collection unit can collect parameters while considering differences in communication environments between mountainous and plains areas. In addition, the data collection unit can collect parameters while considering differences in communication environments between coastal and inland areas. By collecting data while considering differences in geographical communication environments, more accurate parameter collection becomes possible. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input geographical communication environment data into a generation AI and have the generation AI perform data collection.

[0038] The data collection unit can select information to collect by comparing it with communication histories with other base stations when collecting communication parameters. For example, the data collection unit can compare it with communication histories with other base stations when collecting communication parameters. For example, the data collection unit can compare it with communication histories with other base stations and collect the optimal parameters. The data collection unit can also collect parameters when an anomaly occurs based on the communication histories with other base stations. Furthermore, the data collection unit can analyze the communication histories with other base stations and collect the most efficient parameters. This makes it possible to collect parameters more effectively by selecting information by comparing it with communication histories with other base stations. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input communication history data with other base stations into a generation AI and have the generation AI perform data comparison and collection.

[0039] The proposal unit can improve the accuracy of its proposals by referring to past successes and failures when suggesting optimal parameter settings under specific communication conditions. For example, the proposal unit can refer to past successes and failures when suggesting optimal parameter settings under specific communication conditions. For example, the proposal unit can suggest optimal parameter settings based on past successes. The proposal unit can also refer to past failures and suggest parameter settings to avoid the same failures. Furthermore, the proposal unit can compare successes and failures and suggest the most effective parameter settings. In this way, the accuracy of the proposals is improved by referring to past successes and failures. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input data on past successes and failures into a generative AI and have the generative AI perform the improvement of the accuracy of the proposals.

[0040] The proposal unit can propose optimal parameters by considering the balance between communication stability and speed during the proposal process. For example, the proposal unit can propose optimal parameters by considering the balance between communication stability and speed. For example, the proposal unit can propose parameters that prioritize communication stability and provide stable communication even at the expense of speed. Alternatively, the proposal unit can propose parameters that prioritize communication speed and provide high-speed communication even at the expense of stability. Furthermore, the proposal unit can propose optimal parameters by considering the balance between stability and speed. This makes it possible to propose more effective parameters by considering the balance between communication stability and speed. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input data on communication stability and speed into a generative AI and have the generative AI propose optimal parameters.

[0041] The suggestion unit can propose optimal parameters by considering the user's geographical location information during the proposal process. For example, the suggestion unit can propose optimal parameters by considering the user's geographical location information. For example, the suggestion unit can propose optimal parameters by considering the differences in communication environments between urban and suburban areas. Furthermore, the suggestion unit can propose optimal parameters by considering the differences in communication environments between mountainous and flat areas. In addition, the suggestion unit can propose optimal parameters by considering the differences in communication environments between coastal and inland areas. This makes it possible to propose more effective parameters by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input the user's geographical location information data into a generation AI and have the generation AI propose optimal parameters.

[0042] The suggestion unit can, when making a suggestion, refer to the setting history of other users to propose the optimal parameters under similar conditions. For example, the suggestion unit can refer to the setting history of other users to propose the optimal parameters under similar conditions. For example, the suggestion unit can propose the optimal parameters based on the setting history of other users. The suggestion unit can also refer to successful examples of other users under similar conditions to propose the optimal parameters. Furthermore, the suggestion unit can analyze the setting history of other users to propose the most effective parameters. In this way, by referring to the setting history of other users, it can propose the optimal parameters under similar conditions. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input other users' setting history data into a generation AI and have the generation AI perform the task of proposing the optimal parameters.

[0043] The explanatory section can provide concrete examples by referring to past usage examples when explaining the meaning and setting methods of specific parameters. For example, the explanatory section can refer to past usage examples when explaining the meaning and setting methods of specific parameters. The explanatory section can explain the meaning of specific parameters based on past usage examples. The explanatory section can also explain how to set specific parameters by referring to past usage examples. Furthermore, the explanatory section can provide concrete examples and explain the effects of specific parameters. In this way, by referring to past usage examples, concrete examples can be provided, making it easier to understand the meaning and setting methods of specific parameters. Some or all of the above processing in the explanatory section may be performed using a generation AI, or it may be performed without a generation AI. For example, the explanatory section can input past usage example data into a generation AI and have the generation AI perform the task of providing concrete examples.

[0044] The explanation section can simulate the impact of parameter changes on communication during the explanation process and include the results in the explanation. For example, the explanation section can simulate the impact of parameter changes on communication during the explanation process. For example, the explanation section can simulate the impact of parameter changes on communication speed and explain the results. The explanation section can also simulate the impact of parameter changes on communication stability and explain the results. Furthermore, the explanation section can simulate the impact of parameter changes on communication error rates and explain the results. This allows for a more detailed explanation by simulating the impact of parameter changes on communication. Some or all of the above-described processes in the explanation section may be performed using a generation AI, or they may be performed without a generation AI. For example, the explanation section can input parameter change data into a generation AI and have the generation AI perform the simulation and explanation.

[0045] The explanation section can provide different explanation methods depending on the user's technical level. For example, the explanation section can provide basic explanations for beginners, detailed explanations for intermediate users, and specialized explanations for advanced users. By providing explanation methods tailored to the user's technical level, more appropriate explanations become possible. Some or all of the above processing in the explanation section may be performed using a generation AI, or not. For example, the explanation section can input user technical level data into a generation AI and have the generation AI adjust the explanation method.

[0046] The explanatory section can explain the interrelationships with other related parameters during the explanation, thereby promoting overall understanding. For example, the explanatory section can explain the interrelationships with other related parameters during the explanation. For example, the explanatory section can explain the interrelationships between a specific parameter and other parameters. The explanatory section can also explain the effect that changes to one parameter have on other parameters. Furthermore, the explanatory section can explain the interrelationships of related parameters to promote an understanding of the overall communication settings. This promotes overall understanding by explaining the interrelationships with other related parameters. Some or all of the above processing in the explanatory section may be performed using a generating AI, or it may be performed without a generating AI. For example, the explanatory section can input related parameter data into a generating AI and have the generating AI perform the explanation of the interrelationships.

[0047] The creation unit can select the optimal modification method by referring to past modification history when creating a parameter modification file. For example, the creation unit can refer to past modification history when creating a parameter modification file. For example, the creation unit can select the optimal parameter modification method based on past modification history. The creation unit can also refer to past modification history and select a modification method to avoid the same mistakes. Furthermore, the creation unit can select the most effective modification method based on past successes. In this way, the optimal modification method can be selected by referring to past modification history. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit can input past modification history data into a generation AI and have the generation AI select the optimal modification method.

[0048] The creation unit can automatically include backup settings to maintain communication stability during creation. For example, the creation unit can automatically include backup settings to maintain communication stability during creation. For example, the creation unit can automatically include backup settings to maintain communication stability. The creation unit can also automatically include backup settings to prevent communication interruptions. Furthermore, the creation unit can automatically include backup settings to minimize communication errors. By automatically including backup settings to maintain communication stability, communication stability can be improved. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit can input backup setting data into a generation AI and have the generation AI perform the automatic creation of backup settings.

[0049] The creation unit can include optimal settings when creating a parameter change file, taking into account cooperation with other base stations. For example, the creation unit considers cooperation with other base stations when creating a parameter change file. The creation unit can include optimal parameter settings, taking into account cooperation with other base stations. The creation unit can also include optimal parameter settings based on communication history with other base stations. Furthermore, the creation unit can include parameter settings to strengthen cooperation with other base stations. This makes it possible to create a more effective parameter change file by considering cooperation with other base stations. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit can input cooperation data with other base stations into a generation AI and have the generation AI create the optimal settings.

[0050] The creation unit can create an optimal parameter change file by considering the user's geographical location information during creation. For example, the creation unit can create an optimal parameter change file by considering the user's geographical location information during creation. For example, the creation unit can create an optimal parameter change file by considering the differences in communication environments between urban and suburban areas. Furthermore, the creation unit can create an optimal parameter change file by considering the differences in communication environments between mountainous and flat areas. In addition, the creation unit can create an optimal parameter change file by considering the differences in communication environments between coastal and inland areas. This makes it possible to create a more effective parameter change file by considering the user's geographical location information. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit can input the user's geographical location information data into a generation AI and have the generation AI create an optimal parameter change file.

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

[0052] The data collection unit monitors real-time communication status when collecting communication parameters and can collect data when an anomaly occurs. For example, it can automatically collect relevant parameters when a communication delay occurs. It can also automatically collect affected parameters when a communication interruption occurs. Furthermore, it can automatically collect the parameters causing the error when a communication error occurs. This enables rapid response by monitoring real-time communication status and automatically collecting data when an anomaly occurs.

[0053] The proposal unit can improve the accuracy of its proposals by referring to past successes and failures when suggesting optimal parameter settings under specific communication conditions. For example, it can propose optimal parameter settings based on past successes. It can also refer to past failures and propose parameter settings to avoid the same mistakes. Furthermore, it can compare successes and failures and propose the most effective parameter settings. In this way, the accuracy of the proposals is improved by referring to past successes and failures.

[0054] The proposal team can suggest optimal parameters when making a proposal, taking into account the balance between communication stability and speed. For example, it can propose parameters that prioritize communication stability, even at the expense of speed, to provide stable communication. It can also propose parameters that prioritize communication speed, even at the expense of stability, to provide high-speed communication. Furthermore, it can propose optimal parameters that consider the balance between stability and speed. This allows for more effective parameter proposals by considering the balance between communication stability and speed.

[0055] The proposal function can suggest optimal parameters by considering the user's geographical location. For example, it can suggest optimal parameters by considering the differences in communication environments between urban and suburban areas. It can also suggest optimal parameters by considering the differences in communication environments between mountainous and flat areas. Furthermore, it can suggest optimal parameters by considering the differences in communication environments between coastal and inland areas. This makes it possible to suggest more effective parameters by considering the user's geographical location.

[0056] The explanation section can simulate the impact of parameter changes on communication and include the results in the explanation. For example, it can simulate the impact of parameter changes on communication speed and explain the results. It can also simulate the impact of parameter changes on communication stability and explain the results. Furthermore, it can simulate the impact of parameter changes on the communication error rate and explain the results. This allows for more detailed explanations by simulating the impact of parameter changes on communication.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects information about the base station's communication parameters. Specifically, it collects past communication history, settings, and detailed information on various parameters. For example, it collects data such as the start time, end time, and communication volume as communication history, and data such as initial settings and change history as settings. In addition, it collects detailed information on various parameters, such as detailed descriptions of the parameters and related metadata. Step 2: The proposal unit learns from the information collected by the collection unit and proposes parameters that meet the user's requirements. Specifically, it proposes optimal parameter settings under specific communication conditions and suggests optimal parameters based on conditions such as the communication environment and the devices used. Step 3: The explanation section searches for or explains the parameters proposed by the proposal section. Specifically, it provides explanations of the meaning and setting methods of specific parameters, and provides detailed explanations of parameters such as frequency, bandwidth, and transmit power. Step 4: The creation department creates a parameter change file based on the parameters proposed by the proposal department. Specifically, it automatically creates a parameter change file according to the user's request and creates the parameter change file according to standards such as file format and content.

[0059] (Example of form 2) The communication parameter management system according to an embodiment of the present invention is a system that databases information on base station communication parameters and uses a generating AI to provide parameter suggestions, searches, and explanations according to user requests. This communication parameter management system aims to improve work efficiency and accuracy by having the generating AI automatically perform parameter verification and setting tasks that were conventionally performed manually by skilled personnel. For example, the communication parameter management system registers information on base station communication parameters in a database. This database includes past communication history, setting values, and detailed information on various parameters. Next, the generating AI learns from this database and proposes optimal parameters according to user requests. For example, it can propose optimal parameter settings under specific communication conditions. Furthermore, users can use the generating AI to search for and receive explanations of parameters. For example, the generating AI provides explanations about the meaning and setting methods of specific parameters. The system also has a function to automatically create parameter change files according to user requests. This automates parameter change tasks that were conventionally performed manually, improving work efficiency and accuracy. With this system, even inexperienced personnel can easily set and change base station communication parameters, and it is expected to change the conventional wisdom of area tuning. In the future, this will be the first step towards achieving fully automated tuning without human intervention. This will allow the communication parameter management system to streamline and improve the accuracy of setting and changing base station communication parameters.

[0060] The communication parameter management system according to the embodiment comprises a collection unit, a proposal unit, an explanation unit, and a creation unit. The collection unit collects information relating to the communication parameters of a base station. For example, the collection unit collects past communication history, setting values, and detailed information on various parameters. For example, the collection unit can collect data such as the start time, end time, and communication volume of a communication as communication history. The collection unit can also collect data such as initial setting values ​​and change history as setting values. Furthermore, the collection unit can collect detailed information on various parameters, such as detailed descriptions of parameters and related metadata. The proposal unit learns the information collected by the collection unit and proposes parameters according to the user's requests. For example, the proposal unit proposes optimal parameter settings under specific communication conditions. For example, the proposal unit can propose optimal parameters based on conditions such as the communication environment and the devices used. The explanation unit searches for or explains the parameters proposed by the proposal unit. For example, the explanation unit provides explanations about the meaning and setting methods of specific parameters. For example, the explanation unit can provide detailed explanations about parameters such as frequency, bandwidth, and transmission power. The creation unit creates a parameter change file based on the parameters proposed by the proposal unit. The creation unit can, for example, automatically create a parameter change file according to the user's request. The creation unit can, for example, create a parameter change file according to criteria such as file format and content. As a result, the communication parameter management system according to the embodiment can streamline and improve the accuracy of setting and changing communication parameters of base stations.

[0061] The data collection unit can collect past communication history, settings, and detailed information on various parameters. For example, the data collection unit can collect past communication history. For example, the data collection unit can collect data such as the start time, end time, and communication volume of communications. The data collection unit can also collect settings. For example, the data collection unit can collect data such as initial settings and change history. Furthermore, the data collection unit can also collect detailed information on various parameters. For example, the data collection unit can collect detailed descriptions of parameters and related metadata. By collecting past communication history, settings, and detailed information on various parameters, it becomes possible to make more accurate parameter suggestions. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input past communication history data into a generation AI and have the generation AI perform data collection and analysis.

[0062] The suggestion unit can propose parameter settings under specific communication conditions. For example, the suggestion unit can propose optimal parameter settings under specific communication conditions. For example, the suggestion unit can propose optimal parameters based on conditions such as the communication environment and the devices used. For example, the suggestion unit can propose parameters considering geographical conditions such as urban areas, suburbs, and mountainous areas as the communication environment. Furthermore, the suggestion unit can propose parameters considering the characteristics of devices such as smartphones, tablets, and laptops as the devices used. By proposing optimal parameter settings under specific communication conditions, the efficiency and quality of communication can be improved. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input specific communication condition data into a generation AI and have the generation AI execute a proposal for optimal parameter settings.

[0063] The explanation section can provide explanations about the meaning or setting methods of specific parameters. For example, the explanation section can provide explanations about the meaning and setting methods of specific parameters. For example, the explanation section can provide detailed explanations about parameters such as frequency, bandwidth, and transmission power. For example, as a method for setting frequency, the explanation section can explain how to select a specific frequency band or how to adjust the frequency. The explanation section can also explain as a method for setting bandwidth, how to select a specific bandwidth or how to adjust the bandwidth. Furthermore, the explanation section can also explain as a method for setting transmission power, how to select a specific transmission power or how to adjust the transmission power. This allows for a deeper understanding of the user by providing explanations about the meaning and setting methods of specific parameters. Some or all of the above processing in the explanation section may be performed using a generation AI, or it may be performed without a generation AI. For example, the explanation section can input specific parameter data into a generation AI and have the generation AI perform an explanation of the meaning and setting methods of the parameters.

[0064] The creation unit can create parameter change files according to user requests. For example, the creation unit can automatically create parameter change files according to user requests. For example, the creation unit can create parameter change files according to criteria such as file format and content. For example, if a user requests parameter changes under specific communication conditions, the creation unit can create a parameter change file based on that request. Furthermore, if a user requests changes to specific parameters, the creation unit can create a parameter change file based on that request. This enables improved work efficiency and accuracy by automatically creating parameter change files according to user requests. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit can input user request data into a generation AI and have the generation AI create the parameter change file.

[0065] The suggestion unit can propose communication settings for high traffic or settings for low power consumption. For example, the suggestion unit can propose communication settings for high traffic. For example, the suggestion unit can propose optimal communication settings for high traffic based on conditions such as communication volume thresholds and peak time periods. The suggestion unit can also propose settings for low power consumption. For example, the suggestion unit can propose optimal settings for low power consumption based on criteria such as power consumption measurement methods and power saving mode settings. By proposing optimal communication settings for high traffic and settings for low power consumption, the efficiency and quality of communication can be improved. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input high-traffic communication data or low-power consumption data into a generation AI and have the generation AI propose optimal settings.

[0066] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, the data collection unit can estimate the user's emotions using an emotion recognition algorithm. For example, the data collection unit can estimate emotions based on data such as the user's facial expressions, voice, and text. Furthermore, the data collection unit can determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting important communication parameters. If the user is relaxed, the data collection unit can also collect detailed communication history. Additionally, if the user is in a hurry, the data collection unit can prioritize collecting the most impactful parameters. This allows for more appropriate information collection by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the information.

[0067] The data collection unit can analyze past communication history and collect information on the conditions under which specific parameters function. For example, the data collection unit can analyze past communication history. For example, the data collection unit can collect the optimal parameters for a specific time period. The data collection unit can also collect the optimal parameters under specific weather conditions. Furthermore, the data collection unit can collect the optimal parameters under specific geographical conditions. This allows for the collection of optimal parameters under specific conditions by analyzing past communication history. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input past communication history data into a generative AI and have the generative AI perform data analysis and collection.

[0068] The data collection unit can monitor real-time communication status when collecting communication parameters and collect data when an anomaly occurs. For example, the data collection unit monitors real-time communication status when collecting communication parameters. For example, the data collection unit can automatically collect relevant parameters when a communication delay occurs. The data collection unit can also automatically collect affected parameters when a communication interruption occurs. Furthermore, the data collection unit can automatically collect the parameters causing the error when a communication error occurs. This enables rapid response by monitoring real-time communication status and automatically collecting data when an anomaly occurs. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input real-time communication data into a generation AI and have the generation AI perform anomaly detection and data collection.

[0069] The data collection unit can estimate the user's emotions and adjust the level of detail of the information collected based on the estimated emotions. For example, the data collection unit can estimate the user's emotions. For example, the data collection unit can estimate the user's emotions using an emotion recognition algorithm. For example, the data collection unit can estimate emotions based on data such as the user's facial expressions, voice, and text. The data collection unit can also adjust the level of detail of the information collected based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect concise information. If the user is relaxed, the data collection unit can also collect detailed information. Furthermore, if the user is in a hurry, the data collection unit can collect only the most important information. This allows for more appropriate information collection by adjusting the level of detail of the information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the level of detail of the information.

[0070] The data collection unit can collect data while considering differences in geographical communication environments when collecting communication parameters. For example, the data collection unit can consider differences in geographical communication environments when collecting communication parameters. For example, the data collection unit can collect parameters while considering differences in communication environments between urban and suburban areas. Furthermore, the data collection unit can collect parameters while considering differences in communication environments between mountainous and plains areas. In addition, the data collection unit can collect parameters while considering differences in communication environments between coastal and inland areas. By collecting data while considering differences in geographical communication environments, more accurate parameter collection becomes possible. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input geographical communication environment data into a generation AI and have the generation AI perform data collection.

[0071] The data collection unit can select information to collect by comparing it with communication histories with other base stations when collecting communication parameters. For example, the data collection unit can compare it with communication histories with other base stations when collecting communication parameters. For example, the data collection unit can compare it with communication histories with other base stations and collect the optimal parameters. The data collection unit can also collect parameters when an anomaly occurs based on the communication histories with other base stations. Furthermore, the data collection unit can analyze the communication histories with other base stations and collect the most efficient parameters. This makes it possible to collect parameters more effectively by selecting information by comparing it with communication histories with other base stations. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input communication history data with other base stations into a generation AI and have the generation AI perform data comparison and collection.

[0072] The suggestion unit can estimate the user's emotions and adjust the way it expresses the suggested parameters based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions using an emotion recognition algorithm. For example, the suggestion unit can estimate emotions based on data such as the user's facial expressions, voice, and text. Furthermore, the suggestion unit can adjust the way it expresses the suggested parameters based on the estimated emotions. For example, if the user is stressed, the suggestion unit can suggest a concise and easy-to-understand expression. If the user is relaxed, the suggestion unit can suggest an expression that includes detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can suggest a concise expression. This allows for more appropriate suggestions by adjusting the parameter expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the proposal unit can input user emotion data into a generative AI and have the generative AI adjust the way the parameters are expressed.

[0073] The proposal unit can improve the accuracy of its proposals by referring to past successes and failures when suggesting optimal parameter settings under specific communication conditions. For example, the proposal unit can refer to past successes and failures when suggesting optimal parameter settings under specific communication conditions. For example, the proposal unit can suggest optimal parameter settings based on past successes. The proposal unit can also refer to past failures and suggest parameter settings to avoid the same failures. Furthermore, the proposal unit can compare successes and failures and suggest the most effective parameter settings. In this way, the accuracy of the proposals is improved by referring to past successes and failures. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input data on past successes and failures into a generative AI and have the generative AI perform the improvement of the accuracy of the proposals.

[0074] The proposal unit can propose optimal parameters by considering the balance between communication stability and speed during the proposal process. For example, the proposal unit can propose optimal parameters by considering the balance between communication stability and speed. For example, the proposal unit can propose parameters that prioritize communication stability and provide stable communication even at the expense of speed. Alternatively, the proposal unit can propose parameters that prioritize communication speed and provide high-speed communication even at the expense of stability. Furthermore, the proposal unit can propose optimal parameters by considering the balance between stability and speed. This makes it possible to propose more effective parameters by considering the balance between communication stability and speed. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input data on communication stability and speed into a generative AI and have the generative AI propose optimal parameters.

[0075] The suggestion unit can estimate the user's emotions and determine the priority of suggested parameters based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions using an emotion recognition algorithm. For example, the suggestion unit can estimate emotions based on data such as the user's facial expressions, voice, and text. Furthermore, the suggestion unit can determine the priority of suggested parameters based on the estimated emotions. For example, if the user is stressed, the suggestion unit can prioritize suggesting the most important parameters. If the user is relaxed, the suggestion unit can also suggest more detailed parameters. Additionally, if the user is in a hurry, the suggestion unit can prioritize suggesting the most impactful parameters. This allows for more appropriate suggestions by prioritizing parameters based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the proposal unit can input user emotion data into a generative AI and have the generative AI determine the priority of parameters.

[0076] The suggestion unit can propose optimal parameters by considering the user's geographical location information during the proposal process. For example, the suggestion unit can propose optimal parameters by considering the user's geographical location information. For example, the suggestion unit can propose optimal parameters by considering the differences in communication environments between urban and suburban areas. Furthermore, the suggestion unit can propose optimal parameters by considering the differences in communication environments between mountainous and flat areas. In addition, the suggestion unit can propose optimal parameters by considering the differences in communication environments between coastal and inland areas. This makes it possible to propose more effective parameters by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input the user's geographical location information data into a generation AI and have the generation AI propose optimal parameters.

[0077] The suggestion unit can, when making a suggestion, refer to the setting history of other users to propose the optimal parameters under similar conditions. For example, the suggestion unit can refer to the setting history of other users to propose the optimal parameters under similar conditions. For example, the suggestion unit can propose the optimal parameters based on the setting history of other users. The suggestion unit can also refer to successful examples of other users under similar conditions to propose the optimal parameters. Furthermore, the suggestion unit can analyze the setting history of other users to propose the most effective parameters. In this way, by referring to the setting history of other users, it can propose the optimal parameters under similar conditions. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input other users' setting history data into a generation AI and have the generation AI perform the task of proposing the optimal parameters.

[0078] The commentary unit can estimate the user's emotions and adjust the way the commentary is presented based on the estimated emotions. For example, the commentary unit can estimate the user's emotions. For example, the commentary unit can estimate the user's emotions using an emotion recognition algorithm. For example, the commentary unit can estimate emotions based on data such as the user's facial expressions, voice, and text. The commentary unit can also adjust the way the commentary is presented based on the estimated emotions. For example, if the user is feeling stressed, the commentary unit can provide a concise and easy-to-understand explanation. If the user is relaxed, the commentary unit can provide an explanation that includes detailed descriptions. Furthermore, if the user is in a hurry, the commentary unit can provide an explanation that gets straight to the point. By adjusting the way the commentary is presented based on the user's emotions, more appropriate explanations become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the commentary section may be performed using a generative AI, or they may be performed without a generative AI. For example, the commentary section can input user emotion data into a generative AI and have the generative AI adjust the way the commentary is expressed.

[0079] The explanatory section can provide concrete examples by referring to past usage examples when explaining the meaning and setting methods of specific parameters. For example, the explanatory section can refer to past usage examples when explaining the meaning and setting methods of specific parameters. The explanatory section can explain the meaning of specific parameters based on past usage examples. The explanatory section can also explain how to set specific parameters by referring to past usage examples. Furthermore, the explanatory section can provide concrete examples and explain the effects of specific parameters. In this way, by referring to past usage examples, concrete examples can be provided, making it easier to understand the meaning and setting methods of specific parameters. Some or all of the above processing in the explanatory section may be performed using a generation AI, or it may be performed without a generation AI. For example, the explanatory section can input past usage example data into a generation AI and have the generation AI perform the task of providing concrete examples.

[0080] The explanation section can simulate the impact of parameter changes on communication during the explanation process and include the results in the explanation. For example, the explanation section can simulate the impact of parameter changes on communication during the explanation process. For example, the explanation section can simulate the impact of parameter changes on communication speed and explain the results. The explanation section can also simulate the impact of parameter changes on communication stability and explain the results. Furthermore, the explanation section can simulate the impact of parameter changes on communication error rates and explain the results. This allows for a more detailed explanation by simulating the impact of parameter changes on communication. Some or all of the above-described processes in the explanation section may be performed using a generation AI, or they may be performed without a generation AI. For example, the explanation section can input parameter change data into a generation AI and have the generation AI perform the simulation and explanation.

[0081] The commentary unit can estimate the user's emotions and adjust the level of detail in the commentary based on the estimated emotions. For example, the commentary unit can estimate the user's emotions. For example, the commentary unit can estimate the user's emotions using an emotion recognition algorithm. For example, the commentary unit can estimate emotions based on data such as the user's facial expressions, voice, and text. The commentary unit can also adjust the level of detail in the commentary based on the estimated emotions. For example, if the user is feeling stressed, the commentary unit can provide a concise commentary. If the user is relaxed, the commentary unit can provide a detailed commentary. Furthermore, if the user is in a hurry, the commentary unit can provide a concise commentary. By adjusting the level of detail in the commentary based on the user's emotions, more appropriate commentary becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the commentary unit may be performed using generative AI or not. For example, the commentary section can input user emotion data into a generating AI and have the AI ​​adjust the level of detail in the commentary.

[0082] The explanation section can provide different explanation methods depending on the user's technical level. For example, the explanation section can provide basic explanations for beginners, detailed explanations for intermediate users, and specialized explanations for advanced users. By providing explanation methods tailored to the user's technical level, more appropriate explanations become possible. Some or all of the above processing in the explanation section may be performed using a generation AI, or not. For example, the explanation section can input user technical level data into a generation AI and have the generation AI adjust the explanation method.

[0083] The explanatory section can explain the interrelationships with other related parameters during the explanation, thereby promoting overall understanding. For example, the explanatory section can explain the interrelationships with other related parameters during the explanation. For example, the explanatory section can explain the interrelationships between a specific parameter and other parameters. The explanatory section can also explain the effect that changes to one parameter have on other parameters. Furthermore, the explanatory section can explain the interrelationships of related parameters to promote an understanding of the overall communication settings. This promotes overall understanding by explaining the interrelationships with other related parameters. Some or all of the above processing in the explanatory section may be performed using a generating AI, or it may be performed without a generating AI. For example, the explanatory section can input related parameter data into a generating AI and have the generating AI perform the explanation of the interrelationships.

[0084] The creation unit can estimate the user's emotions and adjust the method of creating the parameter change file based on the estimated emotions. For example, the creation unit can estimate the user's emotions using an emotion recognition algorithm. For example, the creation unit can estimate emotions based on data such as the user's facial expressions, voice, and text. Furthermore, the creation unit can adjust the method of creating the parameter change file based on the estimated emotions. For example, if the user is stressed, the creation unit can create a concise parameter change file. If the user is relaxed, the creation unit can create a detailed parameter change file. Furthermore, if the user is in a hurry, the creation unit can create a parameter change file containing only the most important parameters. This allows for the creation of more appropriate files by adjusting the method of creating the parameter change file based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the creation unit can input user emotion data into the generation AI and have the generation AI adjust the method for creating the parameter change file.

[0085] The creation unit can select the optimal modification method by referring to past modification history when creating a parameter modification file. For example, the creation unit can refer to past modification history when creating a parameter modification file. For example, the creation unit can select the optimal parameter modification method based on past modification history. The creation unit can also refer to past modification history and select a modification method to avoid the same mistakes. Furthermore, the creation unit can select the most effective modification method based on past successes. In this way, the optimal modification method can be selected by referring to past modification history. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit can input past modification history data into a generation AI and have the generation AI select the optimal modification method.

[0086] The creation unit can automatically include backup settings to maintain communication stability during creation. For example, the creation unit can automatically include backup settings to maintain communication stability during creation. For example, the creation unit can automatically include backup settings to maintain communication stability. The creation unit can also automatically include backup settings to prevent communication interruptions. Furthermore, the creation unit can automatically include backup settings to minimize communication errors. By automatically including backup settings to maintain communication stability, communication stability can be improved. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit can input backup setting data into a generation AI and have the generation AI perform the automatic creation of backup settings.

[0087] The creation unit can estimate the user's emotions and determine the priority of parameter change files based on the estimated emotions. For example, the creation unit can estimate the user's emotions using an emotion recognition algorithm. For example, the creation unit can estimate emotions based on data such as the user's facial expressions, voice, and text. Furthermore, the creation unit can determine the priority of parameter change files based on the estimated emotions. For example, if the user is stressed, the creation unit can prioritize creating the most important parameter change files. If the user is relaxed, the creation unit can also create detailed parameter change files. Furthermore, if the user is in a hurry, the creation unit can prioritize creating the most impactful parameter change files. This allows for more appropriate file creation by prioritizing parameter change files based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the creation unit can input user emotion data into the generation AI and have the generation AI determine the priority of parameter change files.

[0088] The creation unit can include optimal settings when creating a parameter change file, taking into account cooperation with other base stations. For example, the creation unit considers cooperation with other base stations when creating a parameter change file. The creation unit can include optimal parameter settings, taking into account cooperation with other base stations. The creation unit can also include optimal parameter settings based on communication history with other base stations. Furthermore, the creation unit can include parameter settings to strengthen cooperation with other base stations. This makes it possible to create a more effective parameter change file by considering cooperation with other base stations. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit can input cooperation data with other base stations into a generation AI and have the generation AI create the optimal settings.

[0089] The creation unit can create an optimal parameter change file by considering the user's geographical location information during creation. For example, the creation unit can create an optimal parameter change file by considering the user's geographical location information during creation. For example, the creation unit can create an optimal parameter change file by considering the differences in communication environments between urban and suburban areas. Furthermore, the creation unit can create an optimal parameter change file by considering the differences in communication environments between mountainous and flat areas. In addition, the creation unit can create an optimal parameter change file by considering the differences in communication environments between coastal and inland areas. This makes it possible to create a more effective parameter change file by considering the user's geographical location information. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit can input the user's geographical location information data into a generation AI and have the generation AI create an optimal parameter change file. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, proposal unit, explanation unit, and creation unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information on the base station's communication parameters using the camera 42 and communication I / F 44 of the smart device 14, and the control unit 46A transmits the collected information to the data processing unit 12. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and proposes optimal parameters based on the collected information. The explanation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and provides an explanation of the meaning and setting method of the proposed parameters. The creation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and creates a parameter change file based on the proposed parameters. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, proposal unit, explanation unit, and creation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information on the base station's communication parameters using the camera 42 and communication I / F 44 of the smart glasses 214, and the control unit 46A transmits the collected information to the data processing unit 12. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and proposes optimal parameters based on the collected information. The explanation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and provides an explanation of the meaning and setting method of the proposed parameters. The creation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and creates a parameter change file based on the proposed parameters. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, proposal unit, explanation unit, and creation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information on the base station's communication parameters using the camera 42 and communication I / F 44 of the headset terminal 314, and the control unit 46A transmits the collected information to the data processing unit 12. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes optimal parameters based on the collected information. The explanation unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides an explanation of the meaning and setting method of the proposed parameters. The creation unit is implemented in the specific processing unit 290 of the data processing unit 12 and creates a parameter change file based on the proposed parameters. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, proposal unit, explanation unit, and creation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information on the base station's communication parameters using the robot 414's camera 42 and communication I / F 44, and the control unit 46A transmits the collected information to the data processing unit 12. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and proposes optimal parameters based on the collected information. The explanation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and provides an explanation of the meaning and setting method of the proposed parameters. The creation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and creates a parameter change file based on the proposed parameters.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The suggestion function can estimate the user's emotions and adjust the way it expresses the suggested parameters based on those emotions. For example, if the user is stressed, it can suggest a concise and easy-to-understand expression. If the user is relaxed, it can suggest an expression that includes detailed explanations. Furthermore, if the user is in a hurry, it can suggest an expression that gets straight to the point. By adjusting the expression of parameters based on the user's emotions, more appropriate suggestions can be made.

[0092] The data collection unit monitors real-time communication status when collecting communication parameters and can collect data when an anomaly occurs. For example, it can automatically collect relevant parameters when a communication delay occurs. It can also automatically collect affected parameters when a communication interruption occurs. Furthermore, it can automatically collect the parameters causing the error when a communication error occurs. This enables rapid response by monitoring real-time communication status and automatically collecting data when an anomaly occurs.

[0093] The explanation section can estimate the user's emotions and adjust the way the explanation is presented based on those emotions. For example, if the user is stressed, it can provide a concise and easy-to-understand explanation. If the user is relaxed, it can provide a more detailed explanation. Furthermore, if the user is in a hurry, it can provide a concise and to-the-point explanation. By adjusting the explanation's presentation based on the user's emotions, more appropriate explanations become possible.

[0094] The proposal unit can improve the accuracy of its proposals by referring to past successes and failures when suggesting optimal parameter settings under specific communication conditions. For example, it can propose optimal parameter settings based on past successes. It can also refer to past failures and propose parameter settings to avoid the same mistakes. Furthermore, it can compare successes and failures and propose the most effective parameter settings. In this way, the accuracy of the proposals is improved by referring to past successes and failures.

[0095] The creation unit can estimate the user's emotions and adjust how the parameter change file is created based on those emotions. For example, if the user is stressed, a concise parameter change file can be created. If the user is relaxed, a detailed parameter change file can be created. Furthermore, if the user is in a hurry, a parameter change file containing only the most important parameters can be created. This allows for the creation of more appropriate files by adjusting how the parameter change file is created based on the user's emotions.

[0096] The proposal team can suggest optimal parameters when making a proposal, taking into account the balance between communication stability and speed. For example, it can propose parameters that prioritize communication stability, even at the expense of speed, to provide stable communication. It can also propose parameters that prioritize communication speed, even at the expense of stability, to provide high-speed communication. Furthermore, it can propose optimal parameters that consider the balance between stability and speed. This allows for more effective parameter proposals by considering the balance between communication stability and speed.

[0097] The data collection unit can estimate the user's emotions and prioritize the information to collect based on those emotions. For example, if the user is stressed, important communication parameters can be prioritized for collection. If the user is relaxed, detailed communication history can be collected. Furthermore, if the user is in a hurry, the most impactful parameters can be prioritized for collection. This allows for more appropriate information collection by prioritizing information based on the user's emotions.

[0098] The proposal function can suggest optimal parameters by considering the user's geographical location. For example, it can suggest optimal parameters by considering the differences in communication environments between urban and suburban areas. It can also suggest optimal parameters by considering the differences in communication environments between mountainous and flat areas. Furthermore, it can suggest optimal parameters by considering the differences in communication environments between coastal and inland areas. This makes it possible to suggest more effective parameters by considering the user's geographical location.

[0099] The explanation section can simulate the impact of parameter changes on communication and include the results in the explanation. For example, it can simulate the impact of parameter changes on communication speed and explain the results. It can also simulate the impact of parameter changes on communication stability and explain the results. Furthermore, it can simulate the impact of parameter changes on the communication error rate and explain the results. This allows for more detailed explanations by simulating the impact of parameter changes on communication.

[0100] The creation unit can estimate the user's emotions and prioritize parameter change files based on those emotions. For example, if the user is stressed, it can prioritize creating the most important parameter change files. If the user is relaxed, it can create more detailed parameter change files. Furthermore, if the user is in a hurry, it can prioritize creating the parameter change files with the greatest impact. This allows for more appropriate file creation by prioritizing parameter change files based on the user's emotions.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The data collection unit collects information about the base station's communication parameters. Specifically, it collects past communication history, settings, and detailed information on various parameters. For example, it collects data such as the start time, end time, and communication volume as communication history, and data such as initial settings and change history as settings. In addition, it collects detailed information on various parameters, such as detailed descriptions of the parameters and related metadata. Step 2: The proposal unit learns from the information collected by the collection unit and proposes parameters that meet the user's requirements. Specifically, it proposes optimal parameter settings under specific communication conditions and suggests optimal parameters based on conditions such as the communication environment and the devices used. Step 3: The explanation section searches for or explains the parameters proposed by the proposal section. Specifically, it provides explanations of the meaning and setting methods of specific parameters, and provides detailed explanations of parameters such as frequency, bandwidth, and transmit power. Step 4: The creation department creates a parameter change file based on the parameters proposed by the proposal department. Specifically, it automatically creates a parameter change file according to the user's request and creates the parameter change file according to standards such as file format and content.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information regarding the communication parameters of the base station, A proposal unit learns the information collected by the aforementioned collection unit and proposes parameters that meet the user's requirements. An explanation unit that searches for or explains the parameters proposed by the proposal unit, The system includes a creation unit that creates a parameter change file based on the parameters proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect past communication history, settings, and detailed information on various parameters. The system according to feature 1.

3. The aforementioned proposal section is, Suggests parameter settings under specific communication conditions. The system according to feature 1.

4. The aforementioned explanatory section is, Provides explanations about the meaning or setting methods of specific parameters. The system according to feature 1.

5. The aforementioned creation unit, Create parameter change files according to user requests. The system according to feature 1.

6. The aforementioned proposal section is, Suggests communication settings for high traffic or settings for low power consumption. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and determines the priority of information to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past communication history to collect information on the conditions under which specific parameters function. The system according to feature 1.

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