system

Abstract

The system according to this embodiment aims to streamline station parameter management and prevent omissions, errors, and incorrect parameter input. [Solution] The system according to the embodiment comprises a learning unit, a database creation unit, a checking unit, an inquiry handling unit, a pre-check unit, and a suggestion unit. The learning unit learns master parameters, configuration patterns, and station information. The database creation unit creates a database of stations in which the relevant parameters are entered. The checking unit periodically checks whether there are any omissions or errors in the parameters of existing stations based on the database created by the database creation unit. The inquiry handling unit responds to inquiries regarding various parameters using a chatbot. The pre-check unit performs pre-checks to prevent incorrect parameters from being entered when new stations are introduced or configurations are changed. The suggestion unit suggests appropriate parameters in special situations such as events or disasters.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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, there is a risk of omission or incorrect parameter input in the local parameter management, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of local parameter management and prevent omission or incorrect parameter input.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, a database creation unit, a checking unit, an inquiry handling unit, a pre-check unit, and a proposal unit. The learning unit learns master parameters, configuration patterns, and station information. The database creation unit creates a database of stations in which the relevant parameters are entered. The checking unit periodically checks whether there are any omissions or errors in the parameters of existing stations based on the database created by the database creation unit. The inquiry handling unit responds to inquiries about various parameters using a chatbot. The pre-check unit performs pre-checks to prevent incorrect parameters from being entered when new stations are introduced or configurations are changed. The proposal unit proposes appropriate parameters in special situations such as events or disasters. [Effects of the Invention]

[0007] The system according to this embodiment can streamline parameter management at stations and prevent omissions, errors, or incorrect parameter input. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The parameter management system according to an embodiment of the present invention is a system that uses an AI agent to streamline the parameter management of base stations. This parameter management system first trains the AI ​​agent on master parameters, configuration patterns, and station information. Next, it creates a database of stations where the relevant parameters are entered. This allows the AI ​​agent to understand the parameters of each station and periodically check for any omissions or gaps in the parameters of existing stations. It can also respond to inquiries about various parameters via a chatbot. Furthermore, it can perform pre-checks to prevent incorrect parameters from being entered when new stations are introduced or configurations are changed. Moreover, it can suggest appropriate parameters even in special situations such as events or disasters. For example, the AI ​​agent learns from a large amount of data while maintaining relationships and understands the combinations of each parameter. For example, base station parameters have many items, and are often designed by combining multiple parameters, so by understanding these relationships, the AI ​​agent can efficiently retrieve the necessary information during configuration. Next, it creates a database of stations where the relevant parameters are entered. This allows the AI ​​agent to understand the parameters of each station and periodically check for any omissions or gaps in the parameters of existing stations. For example, the AI ​​agent can periodically and automatically acquire data from all stations, check the settings of each station by comparing them with master parameters and configuration patterns, and report alerts if there are any misconfigurations. Furthermore, it can respond to inquiries about various parameters via a chatbot. For example, if an operator asks a question about a parameter they want to check via chat, the AI ​​agent can extract the relevant parameter and answer the question, including related parameters. In addition, it can perform pre-checks to prevent incorrect parameters from being entered when a new station is installed or a configuration is changed. For example, by using the AI ​​agent to check the configuration data before setting work, misconfigurations and unexpected omissions can be detected instantly. Furthermore, it can suggest appropriate parameters even in special situations such as events or disasters.For example, an AI agent can learn special patterns and suggest parameter settings tailored to the situation. This allows the parameter management system to streamline base station parameter management, preventing misconfigurations and reducing workload.

[0029] The parameter management system according to this embodiment comprises a learning unit, a database creation unit, a checking unit, an inquiry handling unit, a pre-check unit, and a proposal unit. The learning unit learns master parameters, configuration patterns, and station information. The learning unit learns a large amount of data while maintaining relationships between them, for example, using AI, and understands the combinations of each parameter. For example, the learning unit understands that base station parameters have many items and are often designed by combining multiple parameters, so by understanding these relationships, it can efficiently extract the information necessary during configuration. The database creation unit creates a database of stations in which the relevant parameters are entered. The database creation unit can automatically acquire data from all stations using AI, for example, check the configuration status of each station by comparing it with master parameters and configuration patterns, and report an alert if there is a misconfiguration. The checking unit periodically checks whether there are any omissions or errors in the parameters of existing stations based on the database created by the database creation unit. The checking unit can periodically automatically acquire data from all stations using AI, check the configuration status of each station by comparing it with master parameters and configuration patterns, and report an alert if there is a misconfiguration. The inquiry handling unit responds to inquiries regarding various parameters using a chatbot. The inquiry response unit can, for example, when a worker asks a question about a parameter they want to confirm via chat, use AI to extract the relevant parameter and provide an answer including related parameters. The pre-check unit performs pre-checks to prevent incorrect parameters from being entered when a new station is installed or a configuration change is made. For example, the pre-check unit can instantly detect misconfigurations or unexpected omissions by checking the configuration data using AI before the configuration work is performed. The proposal unit proposes appropriate parameters in special situations such as events or disasters. For example, the proposal unit can learn special patterns using AI and propose parameter settings that are appropriate for the situation. As a result, the parameter management system according to this embodiment can streamline the parameter management of base stations, prevent misconfigurations, and reduce man-hours.

[0030] The learning unit learns master parameters, configuration patterns, and station information. For example, the learning unit uses AI to learn large amounts of data while maintaining relationships between them, and understands the combinations of each parameter. Specifically, the AI ​​uses machine learning algorithms to analyze past configuration and operational data, revealing the interrelationships and dependencies between parameters. For example, base station parameters include many items such as frequency, transmit power, antenna direction, and coverage area. By appropriately combining these parameters, optimal communication quality can be achieved. The AI ​​learns these parameter combination patterns and builds a knowledge base to derive optimal settings. Furthermore, the learning unit continuously learns whenever new data is added, maintaining a knowledge base that reflects the latest information. This allows the learning unit to always provide the most up-to-date parameter setting information, improving the operational efficiency of base stations.

[0031] The database creation unit creates a database of stations where the relevant parameters have been entered. For example, the database creation unit can use AI to automatically acquire data from all stations, check the settings of each station by comparing them with master parameters and configuration patterns, and report alerts if there are any misconfigurations. Specifically, the database creation unit collects data from each base station in real time and integrates it into a central database. The AI ​​analyzes the collected data and compares the settings of each station with master parameters and configuration patterns. For example, if the transmission power of a base station exceeds the setting standard, the AI ​​will detect this and issue an alert. The database creation unit also saves the setting history of each station, allowing users to refer to past setting changes and troubleshooting history. This enables the database creation unit to centrally manage the setting status of base stations and support rapid problem solving.

[0032] The checking unit periodically verifies that there are no omissions or errors in the parameters of existing stations based on the database created by the database creation unit. For example, the checking unit can periodically use AI to automatically acquire data from all stations, check the settings of each station by comparing them with master parameters and configuration patterns, and report alerts if there are any misconfigurations. Specifically, the checking unit collects data from all stations based on a regular schedule and performs analysis using AI. The AI ​​compares the setting data of each station with the master parameters and detects any omissions or inconsistencies in the settings. For example, if the direction of an antenna at a station is set incorrectly, the AI ​​will detect this and issue an alert. In addition, the checking unit can save past check results and use them for trend analysis and anomaly detection. This allows the checking unit to continuously monitor the setting status of base stations and support rapid problem detection and response.

[0033] The inquiry support department uses a chatbot to handle inquiries about various parameters. For example, when a worker asks a question about a parameter they want to confirm via chat, the inquiry support department can use AI to extract the relevant parameter and provide an answer including related parameters. Specifically, the inquiry support department is equipped with a chatbot that uses natural language processing technology to understand the worker's question and generate an appropriate answer. For example, if a worker asks, "What is the setting value for the base station's transmission power?", the chatbot will extract the relevant setting value from the database and provide the answer. It also provides background information on related parameters and settings to help workers understand more deeply. Furthermore, the inquiry support department can save past inquiry history and analyze frequently asked questions to automatically generate FAQs. This allows the inquiry support department to provide information quickly and accurately, improving worker efficiency.

[0034] The pre-check unit performs checks to prevent incorrect parameters from being entered when setting up a new station or making configuration changes. For example, by using AI to check the configuration data before the setup work is performed, the pre-check unit can instantly detect misconfigurations or unexpected omissions. Specifically, before the setup work is performed, the pre-check unit inputs the configuration data into the AI, which analyzes the configuration content. The AI ​​compares it with master parameters and configuration patterns to confirm that there are no errors or inconsistencies in the configuration content. For example, if the frequency setting of a new base station may interfere with an existing station, the AI ​​will detect this and issue a warning. The pre-check unit also provides the operator with procedures and points to note for the setup work, offering guidelines to prevent misconfigurations. In this way, the pre-check unit can minimize the risks during new setups and configuration changes and support the stable operation of base stations.

[0035] The proposal unit proposes appropriate parameters in special situations such as events and disasters. For example, the proposal unit can use AI to learn special patterns and propose parameter settings tailored to the situation. Specifically, the proposal unit learns data from past events and disasters and derives optimal parameter settings for similar situations. For example, when a large-scale event is held, the AI ​​predicts an increase in communication traffic based on past event data and proposes appropriate parameter settings. In the event of a disaster, the AI ​​analyzes past disaster data and proposes parameter settings necessary for restoring communication infrastructure and ensuring emergency communications. Furthermore, the proposal unit can monitor the situation in real time and dynamically adjust parameter settings as needed. This allows the proposal unit to maintain an optimal communication environment even in special situations and support a rapid response.

[0036] The learning unit can learn master parameters, configuration patterns, and station information. For example, the learning unit can learn a large amount of data while maintaining relationships between them using AI, and understand the combinations of each parameter. For example, since base station parameters have many items and are often designed by combining multiple parameters, the learning unit can efficiently extract the information necessary during configuration by understanding these relationships. As a result, by the learning unit learning master parameters, configuration patterns, and station information, the AI ​​agent can understand the combinations of each parameter and efficiently extract the information necessary during configuration. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input master parameters, configuration patterns, and station information into a generating AI and allow the generating AI to learn.

[0037] The database creation unit can create a database of stations where the relevant parameter is entered. The database creation unit can, for example, automatically acquire data from all stations using AI, check the settings of each station by comparing them with master parameters and configuration patterns, and report alerts if there are any misconfigurations. As a result, by creating a database of stations where the relevant parameter is entered, the AI ​​agent can understand the parameters of each station and periodically check for any omissions or errors in the parameters of existing stations. Some or all of the above processing in the database creation unit may be performed using AI, or not using AI. For example, the database creation unit can input data of stations where the relevant parameter is entered into a generating AI, and have the generating AI create the database.

[0038] The checking unit can periodically verify that there are no omissions or errors in the parameters of existing stations based on the database created by the database creation unit. For example, the checking unit can periodically use AI to automatically acquire data from all stations, check the settings of each station by comparing them with master parameters and configuration patterns, and report an alert if there are any misconfigurations. This prevents misconfigurations by allowing the checking unit to periodically verify that there are no omissions or errors in the parameters of existing stations. Some or all of the above processing in the checking unit may be performed using AI, for example, or without using AI. For example, the checking unit can input the database created by the database creation unit into a generating AI and have the generating AI perform the checks.

[0039] The inquiry response department can handle inquiries regarding various parameters using a chatbot. For example, if a worker asks a question via chat about a parameter they want to confirm, the inquiry response department can use AI to extract the relevant parameter and provide an answer including related parameters. This allows the inquiry response department to respond quickly to inquiries regarding various parameters by using a chatbot. Some or all of the above processing in the inquiry response department may be performed using AI, or not. For example, the inquiry response department can input the question entered into the chatbot into a generating AI and have the generating AI generate an answer.

[0040] The pre-check unit can perform pre-checks to prevent incorrect parameters from being entered when a new station is installed or a configuration is changed. For example, by using AI to check the configuration data before the setup work is performed, the pre-check unit can instantly detect misconfigurations or unexpected omissions. In this way, the pre-check unit can prevent incorrect parameters from being entered when a new station is installed or a configuration is changed. Some or all of the above-described processes in the pre-check unit may be performed using AI, or they may not. For example, the pre-check unit can input configuration data into a generating AI and have the generating AI perform the pre-check.

[0041] The proposal unit can suggest appropriate parameters in special situations such as events or disasters. For example, the proposal unit can learn special patterns using AI and suggest parameter settings tailored to the situation. This allows for appropriate parameter settings according to the situation, such as events or disasters, by having the proposal unit suggest appropriate parameters in special situations. Some or all of the above processing in the proposal unit may be performed using AI, or not using AI. For example, the proposal unit can input parameter settings for special situations into a generating AI and have the generating AI generate suggestions.

[0042] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can use AI to analyze past learning data and select the most effective learning algorithm. For example, the learning unit can adjust the learning progress in real time based on past learning data. For example, the learning unit can refer to past learning data to determine learning priorities. This improves learning efficiency by allowing the learning unit to optimize the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0043] The learning unit can simulate different parameter combinations during training to find the optimal configuration pattern. For example, the learning unit can use AI to simulate different parameter combinations and find the optimal configuration pattern. For example, the learning unit can propose the optimal parameter combination based on the simulation results. For example, the learning unit can analyze the simulation results and select the optimal configuration pattern. In this way, the learning unit can find the optimal configuration pattern by simulating different parameter combinations. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input different parameter combinations into a generating AI and have the generating AI perform a simulation.

[0044] The learning unit can integrate information from different data sources during the learning process. For example, the learning unit can use AI to integrate information from different data sources and improve the accuracy of learning. For example, the learning unit can analyze information from different data sources and select the optimal learning method. For example, the learning unit can adjust the progress of learning in real time based on information from different data sources. As a result, the learning unit improves the accuracy of learning by integrating information from different data sources. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input information from different data sources into a generating AI and have the generating AI perform the information integration.

[0045] The learning unit can perform learning by referencing parameter settings from different industries. For example, the learning unit can use AI to reference parameter settings from different industries and improve the accuracy of learning. For example, the learning unit can analyze parameter settings from different industries and select the optimal learning method. For example, the learning unit can adjust the progress of learning in real time based on parameter settings from different industries. This improves the accuracy of learning by allowing the learning unit to reference parameter settings from different industries. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input parameter settings from different industries into a generating AI and have the generating AI perform learning.

[0046] The database creation unit can automatically convert and unify different data formats during database creation. For example, the database creation unit can use AI to automatically convert different data formats and maintain database integrity. For example, the database creation unit can analyze different data formats and select the optimal conversion method. For example, the database creation unit can unify the database based on the different data formats. This ensures database integrity by automatically converting different data formats. Some or all of the above processes in the database creation unit may be performed using AI, or without AI. For example, the database creation unit can input different data formats into a generating AI and have the generating AI perform the data format conversion.

[0047] The database creation unit can check data integrity and automatically correct outliers during database creation. For example, the database creation unit can use AI to check data integrity and automatically correct outliers during database creation. For example, the database creation unit can detect outliers and select the optimal correction method. For example, the database creation unit maintains data integrity based on the outliers. This improves the reliability of the database by allowing the database creation unit to check data integrity and automatically correct outliers. Some or all of the above processes in the database creation unit may be performed using AI, or without AI. For example, the database creation unit can input data integrity checks into a generation AI and have the generation AI perform the correction of outliers.

[0048] The database creation unit can integrate with different database systems during database creation. For example, the database creation unit can use AI to integrate with different database systems and maintain data consistency. For example, the database creation unit can analyze different database systems and select the optimal integration method. For example, the database creation unit can integrate data based on different database systems. This ensures data consistency through the database creation unit's integration with different database systems. Some or all of the above processes in the database creation unit may be performed using AI, or without AI. For example, the database creation unit can input the integration with different database systems into a generating AI and have the generating AI execute the integration.

[0049] The database creation unit can back up data using cloud storage when creating a database. The database creation unit can, for example, use AI to back up data using cloud storage and ensure data security. The database creation unit can, for example, analyze cloud storage and select the optimal backup method. The database creation unit can, for example, back up data based on cloud storage. In this way, data security is ensured by the database creation unit backing up data using cloud storage. Some or all of the above processes in the database creation unit may be performed using AI, for example, or without AI. For example, the database creation unit can input cloud storage into a generation AI and have the generation AI perform the data backup.

[0050] The checking unit can optimize check items by referring to past check history during the check process. For example, the checking unit can use AI to refer to past check history and select the most suitable check items. For example, the checking unit can adjust the progress of the check in real time based on past check history. For example, the checking unit can analyze past check history and determine the priority of checks. As a result, the efficiency of the check is improved by the checking unit optimizing check items by referring to past check history. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input past check history into a generating AI and have the generating AI perform the optimization of check items.

[0051] The checking unit can improve the accuracy of its checks by considering the interrelationships of different parameters during the check process. For example, the checking unit can use AI to consider the interrelationships of different parameters and improve the accuracy of its checks. For example, the checking unit can analyze the interrelationships of different parameters and select the optimal checking method. For example, the checking unit can adjust the progress of the check in real time based on the interrelationships of different parameters. As a result, the accuracy of the check is improved by the checking unit considering the interrelationships of different parameters. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the interrelationships of different parameters into a generating AI and have the generating AI perform the check accuracy improvement.

[0052] The checking unit can integrate data from different devices during the checking process. For example, the checking unit can use AI to integrate data from different devices and improve the accuracy of the check. For example, the checking unit can analyze data from different devices and select the optimal checking method. For example, the checking unit can adjust the progress of the check in real time based on data from different devices. This improves the accuracy of the check by integrating data from different devices. Some or all of the above-described processes in the checking unit may be performed using AI, or without AI. For example, the checking unit can input data from different devices into a generating AI and have the generating AI perform data integration.

[0053] The checking unit can perform checks by referring to checking standards from different industries. For example, the checking unit can use AI to refer to checking standards from different industries and improve the accuracy of the checks. For example, the checking unit can analyze checking standards from different industries and select the optimal checking method. For example, the checking unit can adjust the progress of the checks in real time based on checking standards from different industries. As a result, the accuracy of the checks is improved by the checking unit referring to checking standards from different industries. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input checking standards from different industries into a generating AI and have the generating AI perform the checks.

[0054] The inquiry response unit can provide the best possible answer by referring to past inquiry history when responding to an inquiry. For example, the inquiry response unit may use AI to refer to past inquiry history and select the best answer. For example, the inquiry response unit may adjust the progress of the answer in real time based on past inquiry history. For example, the inquiry response unit may analyze past inquiry history and determine the priority of the answers. As a result, the inquiry response unit can provide the best possible answer by referring to past inquiry history. Some or all of the above processes in the inquiry response unit may be performed using AI, for example, or without AI. For example, the inquiry response unit may input past inquiry history into a generating AI and have the generating AI execute the best possible answer.

[0055] The inquiry handling unit can automatically handle inquiries in different languages. For example, the inquiry handling unit can use AI to automatically handle inquiries in different languages, thereby eliminating language barriers. For example, the inquiry handling unit can analyze different languages ​​and select the optimal response method. For example, the inquiry handling unit can adjust the progress of the inquiry handling in real time based on the different languages. In this way, the inquiry handling unit can eliminate language barriers by automatically handling inquiries in different languages. Some or all of the above processes in the inquiry handling unit may be performed using AI, for example, or without AI. For example, the inquiry handling unit can input inquiries in different languages ​​into a generating AI and have the generating AI execute the response.

[0056] The inquiry handling department can integrate responses from different channels (email, chat, telephone) when handling inquiries. For example, the inquiry handling department can use AI to handle inquiries from different channels in an integrated manner, improving user convenience. For example, the inquiry handling department can analyze different channels and select the optimal response method. For example, the inquiry handling department can adjust the progress of inquiry handling in real time based on different channels. As a result, user convenience is improved by the inquiry handling department integrating responses from different channels. Some or all of the above processes in the inquiry handling department may be performed using AI, for example, or without AI. For example, the inquiry handling department can input inquiries from different channels into a generating AI and have the generating AI execute the response.

[0057] The inquiry handling department can provide answers by referring to inquiry handling examples from different industries when handling inquiries. For example, the inquiry handling department can use AI to refer to inquiry handling examples from different industries and provide the optimal answer. For example, the inquiry handling department can analyze inquiry handling examples from different industries and select the optimal response method. For example, the inquiry handling department can adjust the progress of inquiry handling in real time based on inquiry handling examples from different industries. As a result, the inquiry handling department can provide the optimal answer by referring to inquiry handling examples from different industries. Some or all of the above processes in the inquiry handling department may be performed using AI, for example, or without AI. For example, the inquiry handling department can input inquiry handling examples from different industries into a generating AI and have the generating AI execute the answer.

[0058] The pre-check unit can optimize check items by referring to past pre-check history during the pre-check process. For example, the pre-check unit can use AI to refer to past pre-check history and select the most suitable check items. For example, the pre-check unit can adjust the progress of the check in real time based on past pre-check history. For example, the pre-check unit can analyze past pre-check history and determine the priority of checks. This improves the efficiency of the check by allowing the pre-check unit to optimize check items by referring to past pre-check history. Some or all of the above processes in the pre-check unit may be performed using AI, for example, or without AI. For example, the pre-check unit can input past pre-check history into a generating AI and have the generating AI perform the optimization of check items.

[0059] The pre-check unit can improve the accuracy of the check by considering the interrelationships of different parameters during the pre-check. For example, the pre-check unit can use AI to consider the interrelationships of different parameters and improve the accuracy of the check. For example, the pre-check unit can analyze the interrelationships of different parameters and select the optimal check method. For example, the pre-check unit can adjust the progress of the check in real time based on the interrelationships of different parameters. As a result, the accuracy of the check is improved by the pre-check unit considering the interrelationships of different parameters. Some or all of the above processing in the pre-check unit may be performed using AI, for example, or without AI. For example, the pre-check unit can input the interrelationships of different parameters into a generating AI and have the generating AI perform the check accuracy improvement.

[0060] The pre-check unit can integrate data from different devices during the pre-check process. For example, the pre-check unit can use AI to integrate data from different devices and improve the accuracy of the check. For example, the pre-check unit can analyze data from different devices and select the optimal checking method. For example, the pre-check unit can adjust the progress of the check in real time based on data from different devices. This improves the accuracy of the check by integrating data from different devices. Some or all of the above-described processes in the pre-check unit may be performed using AI, or without AI. For example, the pre-check unit can input data from different devices into a generating AI and have the generating AI perform data integration.

[0061] The pre-check unit can perform checks by referring to check standards from different industries during the pre-check process. For example, the pre-check unit can use AI to refer to check standards from different industries and improve the accuracy of the checks. For example, the pre-check unit can analyze check standards from different industries and select the optimal check method. For example, the pre-check unit can adjust the progress of the checks in real time based on check standards from different industries. As a result, the accuracy of the checks is improved by the pre-check unit referring to check standards from different industries. Some or all of the above processes in the pre-check unit may be performed using AI, for example, or without AI. For example, the pre-check unit can input check standards from different industries into a generating AI and have the generating AI perform the checks.

[0062] The proposal department can provide the optimal proposal by referring to past proposal history when making a proposal. For example, the proposal department can use AI to refer to past proposal history and select the best proposal. For example, the proposal department can adjust the progress of proposals in real time based on past proposal history. For example, the proposal department can analyze past proposal history and determine the priority of proposals. In this way, the proposal department can provide the optimal proposal by referring to past proposal history. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input past proposal history into a generation AI and have the generation AI execute the optimal proposal.

[0063] The proposal unit can simulate different parameter combinations during the proposal process and make the optimal proposal. For example, the proposal unit can use AI to simulate different parameter combinations and make the optimal proposal. For example, the proposal unit can propose the optimal parameter combination based on the simulation results. For example, the proposal unit can analyze the simulation results and select the optimal proposal. In this way, the proposal unit makes the optimal proposal by simulating different parameter combinations. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input different parameter combinations into a generating AI and have the generating AI perform a simulation.

[0064] The proposal unit can integrate data from different devices to make proposals. For example, the proposal unit can use AI to integrate data from different devices and improve the accuracy of the proposals. For example, the proposal unit can analyze data from different devices and select the optimal proposal method. For example, the proposal unit can adjust the progress of the proposal in real time based on data from different devices. As a result, the accuracy of the proposals is improved by the proposal unit integrating data from different devices. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data from different devices into a generating AI and have the generating AI perform data integration.

[0065] The proposal department can make proposals by referring to examples of proposals from different industries. For example, the proposal department can use AI to refer to examples of proposals from different industries and provide the optimal proposal. For example, the proposal department can analyze examples of proposals from different industries and select the optimal proposal method. For example, the proposal department can adjust the progress of the proposal in real time based on examples of proposals from different industries. In this way, the proposal department can provide the optimal proposal by referring to examples of proposals from different industries. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input examples of proposals from different industries into a generation AI and have the generation AI execute the proposal.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The database creation unit can automatically convert and unify different data formats. For example, it can use AI to automatically convert different data formats and maintain database integrity. It analyzes different data formats and selects the optimal conversion method. Based on the different data formats, it unifies the database. In this way, the database creation unit automatically converts different data formats, maintaining database integrity.

[0068] The customer support department can automatically handle inquiries in different languages. For example, it can use AI to automatically respond to inquiries in different languages, eliminating language barriers. It analyzes different languages ​​and selects the optimal response method. Based on the different languages, it adjusts the progress of the inquiry response in real time. In this way, the customer support department can automatically handle inquiries in different languages, eliminating language barriers.

[0069] The learning unit can learn by referencing parameter settings from different industries. For example, it can use AI to improve the accuracy of learning by referencing parameter settings from different industries. It analyzes parameter settings from different industries and selects the optimal learning method. Based on the parameter settings from different industries, it adjusts the progress of learning in real time. As a result, the learning unit improves the accuracy of learning by referencing parameter settings from different industries.

[0070] The checking unit can integrate data from different devices to perform checks. For example, it can use AI to integrate data from different devices and improve the accuracy of the checks. It analyzes data from different devices and selects the optimal checking method. Based on the data from different devices, it adjusts the progress of the checks in real time. As a result, the accuracy of the checks is improved by the checking unit integrating data from different devices.

[0071] The proposal department can simulate different parameter combinations and make optimal suggestions. For example, it can use AI to simulate different parameter combinations and make optimal suggestions. Based on the simulation results, it proposes the optimal parameter combination. It analyzes the simulation results and selects the best suggestion. In this way, the proposal department makes optimal suggestions by simulating different parameter combinations.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The learning unit learns master parameters, configuration patterns, and station information. The learning unit learns a large amount of data, for example using AI, while maintaining relationships between them, and understands each combination of parameters. This allows it to efficiently extract the information needed during setup. Step 2: The database creation unit creates a database of stations where the relevant parameter has been entered. The database creation unit can, for example, use AI to automatically acquire data from all stations, check the settings of each station by comparing them with master parameters and configuration patterns, and report an alert if there are any misconfigurations. Step 3: The checking unit periodically verifies that there are no omissions or errors in the parameters of existing stations based on the database created by the database creation unit. For example, the checking unit can periodically use AI to automatically acquire data from all stations, check the settings of each station by comparing them with master parameters and configuration patterns, and report an alert if there are any misconfigurations. Step 4: The inquiry response department uses a chatbot to handle inquiries about various parameters. For example, when a worker asks a question via chat about a parameter they want to confirm, the inquiry response department can use AI to extract the relevant parameter and provide an answer including related parameters. Step 5: The pre-check unit performs pre-checks to ensure that incorrect parameters are not entered when a new station is installed or the configuration is changed. For example, by using AI to check the configuration data before the setup work is completed, the pre-check unit can instantly detect misconfigurations or unexpected omissions. Step 6: The proposal unit suggests appropriate parameters for special situations such as events or disasters. For example, the proposal unit can use AI to learn special patterns and suggest parameter settings tailored to the situation.

[0074] (Example of form 2) The parameter management system according to an embodiment of the present invention is a system that uses an AI agent to streamline the parameter management of base stations. This parameter management system first trains the AI ​​agent on master parameters, configuration patterns, and station information. Next, it creates a database of stations where the relevant parameters are entered. This allows the AI ​​agent to understand the parameters of each station and periodically check for any omissions or gaps in the parameters of existing stations. It can also respond to inquiries about various parameters via a chatbot. Furthermore, it can perform pre-checks to prevent incorrect parameters from being entered when new stations are introduced or configurations are changed. Moreover, it can suggest appropriate parameters even in special situations such as events or disasters. For example, the AI ​​agent learns from a large amount of data while maintaining relationships and understands the combinations of each parameter. For example, base station parameters have many items, and are often designed by combining multiple parameters, so by understanding these relationships, the AI ​​agent can efficiently retrieve the necessary information during configuration. Next, it creates a database of stations where the relevant parameters are entered. This allows the AI ​​agent to understand the parameters of each station and periodically check for any omissions or gaps in the parameters of existing stations. For example, the AI ​​agent can periodically and automatically acquire data from all stations, check the settings of each station by comparing them with master parameters and configuration patterns, and report alerts if there are any misconfigurations. Furthermore, it can respond to inquiries about various parameters via a chatbot. For example, if an operator asks a question about a parameter they want to check via chat, the AI ​​agent can extract the relevant parameter and answer the question, including related parameters. In addition, it can perform pre-checks to prevent incorrect parameters from being entered when a new station is installed or a configuration is changed. For example, by using the AI ​​agent to check the configuration data before setting work, misconfigurations and unexpected omissions can be detected instantly. Furthermore, it can suggest appropriate parameters even in special situations such as events or disasters.For example, an AI agent can learn special patterns and suggest parameter settings tailored to the situation. This allows the parameter management system to streamline base station parameter management, preventing misconfigurations and reducing workload.

[0075] The parameter management system according to this embodiment comprises a learning unit, a database creation unit, a checking unit, an inquiry handling unit, a pre-check unit, and a proposal unit. The learning unit learns master parameters, configuration patterns, and station information. The learning unit learns a large amount of data while maintaining relationships between them, for example, using AI, and understands the combinations of each parameter. For example, the learning unit understands that base station parameters have many items and are often designed by combining multiple parameters, so by understanding these relationships, it can efficiently extract the information necessary during configuration. The database creation unit creates a database of stations in which the relevant parameters are entered. The database creation unit can automatically acquire data from all stations using AI, for example, check the configuration status of each station by comparing it with master parameters and configuration patterns, and report an alert if there is a misconfiguration. The checking unit periodically checks whether there are any omissions or errors in the parameters of existing stations based on the database created by the database creation unit. The checking unit can periodically automatically acquire data from all stations using AI, check the configuration status of each station by comparing it with master parameters and configuration patterns, and report an alert if there is a misconfiguration. The inquiry handling unit responds to inquiries regarding various parameters using a chatbot. The inquiry response unit can, for example, when a worker asks a question about a parameter they want to confirm via chat, use AI to extract the relevant parameter and provide an answer including related parameters. The pre-check unit performs pre-checks to prevent incorrect parameters from being entered when a new station is installed or a configuration change is made. For example, the pre-check unit can instantly detect misconfigurations or unexpected omissions by checking the configuration data using AI before the configuration work is performed. The proposal unit proposes appropriate parameters in special situations such as events or disasters. For example, the proposal unit can learn special patterns using AI and propose parameter settings that are appropriate for the situation. As a result, the parameter management system according to this embodiment can streamline the parameter management of base stations, prevent misconfigurations, and reduce man-hours.

[0076] The learning unit learns master parameters, configuration patterns, and station information. For example, the learning unit uses AI to learn large amounts of data while maintaining relationships between them, and understands the combinations of each parameter. Specifically, the AI ​​uses machine learning algorithms to analyze past configuration and operational data, revealing the interrelationships and dependencies between parameters. For example, base station parameters include many items such as frequency, transmit power, antenna direction, and coverage area. By appropriately combining these parameters, optimal communication quality can be achieved. The AI ​​learns these parameter combination patterns and builds a knowledge base to derive optimal settings. Furthermore, the learning unit continuously learns whenever new data is added, maintaining a knowledge base that reflects the latest information. This allows the learning unit to always provide the most up-to-date parameter setting information, improving the operational efficiency of base stations.

[0077] The database creation unit creates a database of stations where the relevant parameters have been entered. For example, the database creation unit can use AI to automatically acquire data from all stations, check the settings of each station by comparing them with master parameters and configuration patterns, and report alerts if there are any misconfigurations. Specifically, the database creation unit collects data from each base station in real time and integrates it into a central database. The AI ​​analyzes the collected data and compares the settings of each station with master parameters and configuration patterns. For example, if the transmission power of a base station exceeds the setting standard, the AI ​​will detect this and issue an alert. The database creation unit also saves the setting history of each station, allowing users to refer to past setting changes and troubleshooting history. This enables the database creation unit to centrally manage the setting status of base stations and support rapid problem solving.

[0078] The checking unit periodically verifies that there are no omissions or errors in the parameters of existing stations based on the database created by the database creation unit. For example, the checking unit can periodically use AI to automatically acquire data from all stations, check the settings of each station by comparing them with master parameters and configuration patterns, and report alerts if there are any misconfigurations. Specifically, the checking unit collects data from all stations based on a regular schedule and performs analysis using AI. The AI ​​compares the setting data of each station with the master parameters and detects any omissions or inconsistencies in the settings. For example, if the direction of an antenna at a station is set incorrectly, the AI ​​will detect this and issue an alert. In addition, the checking unit can save past check results and use them for trend analysis and anomaly detection. This allows the checking unit to continuously monitor the setting status of base stations and support rapid problem detection and response.

[0079] The inquiry support department uses a chatbot to handle inquiries about various parameters. For example, when a worker asks a question about a parameter they want to confirm via chat, the inquiry support department can use AI to extract the relevant parameter and provide an answer including related parameters. Specifically, the inquiry support department is equipped with a chatbot that uses natural language processing technology to understand the worker's question and generate an appropriate answer. For example, if a worker asks, "What is the setting value for the base station's transmission power?", the chatbot will extract the relevant setting value from the database and provide the answer. It also provides background information on related parameters and settings to help workers understand more deeply. Furthermore, the inquiry support department can save past inquiry history and analyze frequently asked questions to automatically generate FAQs. This allows the inquiry support department to provide information quickly and accurately, improving worker efficiency.

[0080] The pre-check unit performs checks to prevent incorrect parameters from being entered when setting up a new station or making configuration changes. For example, by using AI to check the configuration data before the setup work is performed, the pre-check unit can instantly detect misconfigurations or unexpected omissions. Specifically, before the setup work is performed, the pre-check unit inputs the configuration data into the AI, which analyzes the configuration content. The AI ​​compares it with master parameters and configuration patterns to confirm that there are no errors or inconsistencies in the configuration content. For example, if the frequency setting of a new base station may interfere with an existing station, the AI ​​will detect this and issue a warning. The pre-check unit also provides the operator with procedures and points to note for the setup work, offering guidelines to prevent misconfigurations. In this way, the pre-check unit can minimize the risks during new setups and configuration changes and support the stable operation of base stations.

[0081] The proposal unit proposes appropriate parameters in special situations such as events and disasters. For example, the proposal unit can use AI to learn special patterns and propose parameter settings tailored to the situation. Specifically, the proposal unit learns data from past events and disasters and derives optimal parameter settings for similar situations. For example, when a large-scale event is held, the AI ​​predicts an increase in communication traffic based on past event data and proposes appropriate parameter settings. In the event of a disaster, the AI ​​analyzes past disaster data and proposes parameter settings necessary for restoring communication infrastructure and ensuring emergency communications. Furthermore, the proposal unit can monitor the situation in real time and dynamically adjust parameter settings as needed. This allows the proposal unit to maintain an optimal communication environment even in special situations and support a rapid response.

[0082] The learning unit can learn master parameters, configuration patterns, and station information. For example, the learning unit can learn a large amount of data while maintaining relationships between them using AI, and understand the combinations of each parameter. For example, since base station parameters have many items and are often designed by combining multiple parameters, the learning unit can efficiently extract the information necessary during configuration by understanding these relationships. As a result, by the learning unit learning master parameters, configuration patterns, and station information, the AI ​​agent can understand the combinations of each parameter and efficiently extract the information necessary during configuration. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input master parameters, configuration patterns, and station information into a generating AI and allow the generating AI to learn.

[0083] The database creation unit can create a database of stations where the relevant parameter is entered. The database creation unit can, for example, automatically acquire data from all stations using AI, check the settings of each station by comparing them with master parameters and configuration patterns, and report alerts if there are any misconfigurations. As a result, by creating a database of stations where the relevant parameter is entered, the AI ​​agent can understand the parameters of each station and periodically check for any omissions or errors in the parameters of existing stations. Some or all of the above processing in the database creation unit may be performed using AI, or not using AI. For example, the database creation unit can input data of stations where the relevant parameter is entered into a generating AI, and have the generating AI create the database.

[0084] The checking unit can periodically verify that there are no omissions or errors in the parameters of existing stations based on the database created by the database creation unit. For example, the checking unit can periodically use AI to automatically acquire data from all stations, check the settings of each station by comparing them with master parameters and configuration patterns, and report an alert if there are any misconfigurations. This prevents misconfigurations by allowing the checking unit to periodically verify that there are no omissions or errors in the parameters of existing stations. Some or all of the above processing in the checking unit may be performed using AI, for example, or without using AI. For example, the checking unit can input the database created by the database creation unit into a generating AI and have the generating AI perform the checks.

[0085] The inquiry response department can handle inquiries regarding various parameters using a chatbot. For example, if a worker asks a question via chat about a parameter they want to confirm, the inquiry response department can use AI to extract the relevant parameter and provide an answer including related parameters. This allows the inquiry response department to respond quickly to inquiries regarding various parameters by using a chatbot. Some or all of the above processing in the inquiry response department may be performed using AI, or not. For example, the inquiry response department can input the question entered into the chatbot into a generating AI and have the generating AI generate an answer.

[0086] The pre-check unit can perform pre-checks to prevent incorrect parameters from being entered when a new station is installed or a configuration is changed. For example, by using AI to check the configuration data before the setup work is performed, the pre-check unit can instantly detect misconfigurations or unexpected omissions. In this way, the pre-check unit can prevent incorrect parameters from being entered when a new station is installed or a configuration is changed. Some or all of the above-described processes in the pre-check unit may be performed using AI, or they may not. For example, the pre-check unit can input configuration data into a generating AI and have the generating AI perform the pre-check.

[0087] The proposal unit can suggest appropriate parameters in special situations such as events or disasters. For example, the proposal unit can learn special patterns using AI and suggest parameter settings tailored to the situation. This allows for appropriate parameter settings according to the situation, such as events or disasters, by having the proposal unit suggest appropriate parameters in special situations. Some or all of the above processing in the proposal unit may be performed using AI, or not using AI. For example, the proposal unit can input parameter settings for special situations into a generating AI and have the generating AI generate suggestions.

[0088] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the AI ​​will start learning from simple parameters and gradually move to more complex ones. If the user is relaxed, the AI ​​will learn detailed parameters all at once. If the user is in a hurry, the AI ​​will prioritize learning important parameters. This allows the learning unit to select training data based on the user's emotions, enabling optimal learning tailored to the user's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using an AI, or not. For example, the learning unit can input user emotion data into a generative AI and have the generative AI select training data.

[0089] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can use AI to analyze past learning data and select the most effective learning algorithm. For example, the learning unit can adjust the learning progress in real time based on past learning data. For example, the learning unit can refer to past learning data to determine learning priorities. This improves learning efficiency by allowing the learning unit to optimize the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0090] The learning unit can simulate different parameter combinations during training to find the optimal configuration pattern. For example, the learning unit can use AI to simulate different parameter combinations and find the optimal configuration pattern. For example, the learning unit can propose the optimal parameter combination based on the simulation results. For example, the learning unit can analyze the simulation results and select the optimal configuration pattern. In this way, the learning unit can find the optimal configuration pattern by simulating different parameter combinations. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input different parameter combinations into a generating AI and have the generating AI perform a simulation.

[0091] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the AI ​​will reduce the learning frequency to alleviate the user's burden. For example, if the user is relaxed, the AI ​​will increase the learning frequency to learn more efficiently. For example, if the user is in a hurry, the AI ​​will adjust the learning frequency to prioritize learning important parameters. In this way, by adjusting the learning frequency based on the user's emotions, the learning unit achieves an optimal learning frequency according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI adjust the learning frequency.

[0092] The learning unit can integrate information from different data sources during the learning process. For example, the learning unit can use AI to integrate information from different data sources and improve the accuracy of learning. For example, the learning unit can analyze information from different data sources and select the optimal learning method. For example, the learning unit can adjust the progress of learning in real time based on information from different data sources. As a result, the learning unit improves the accuracy of learning by integrating information from different data sources. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input information from different data sources into a generating AI and have the generating AI perform the information integration.

[0093] The learning unit can perform learning by referencing parameter settings from different industries. For example, the learning unit can use AI to reference parameter settings from different industries and improve the accuracy of learning. For example, the learning unit can analyze parameter settings from different industries and select the optimal learning method. For example, the learning unit can adjust the progress of learning in real time based on parameter settings from different industries. This improves the accuracy of learning by allowing the learning unit to reference parameter settings from different industries. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input parameter settings from different industries into a generating AI and have the generating AI perform learning.

[0094] The database creation unit can estimate the user's emotions and adjust the database update frequency based on the estimated emotions. For example, if the user is stressed, the AI ​​in the database creation unit will reduce the database update frequency to alleviate the user's burden. For example, if the user is relaxed, the AI ​​in the database creation unit will increase the database update frequency to manage data efficiently. For example, if the user is in a hurry, the AI ​​in the database creation unit will adjust the database update frequency to prioritize updating important data. This allows the database creation unit to adjust the database update frequency based on the user's emotions, enabling optimal database management tailored to the user's situation. 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 database creation unit may be performed using AI or not using AI. For example, the database creation unit can input user emotion data into a generative AI and have the generative AI adjust the database update frequency.

[0095] The database creation unit can automatically convert and unify different data formats during database creation. For example, the database creation unit can use AI to automatically convert different data formats and maintain database integrity. For example, the database creation unit can analyze different data formats and select the optimal conversion method. For example, the database creation unit can unify the database based on the different data formats. This ensures database integrity by automatically converting different data formats. Some or all of the above processes in the database creation unit may be performed using AI, or without AI. For example, the database creation unit can input different data formats into a generating AI and have the generating AI perform the data format conversion.

[0096] The database creation unit can check data integrity and automatically correct outliers during database creation. For example, the database creation unit can use AI to check data integrity and automatically correct outliers during database creation. For example, the database creation unit can detect outliers and select the optimal correction method. For example, the database creation unit maintains data integrity based on the outliers. This improves the reliability of the database by allowing the database creation unit to check data integrity and automatically correct outliers. Some or all of the above processes in the database creation unit may be performed using AI, or without AI. For example, the database creation unit can input data integrity checks into a generation AI and have the generation AI perform the correction of outliers.

[0097] The database creation unit can estimate the user's emotions and adjust database access permissions based on the estimated emotions. For example, if the user is stressed, the AI ​​in the database creation unit will restrict database access permissions to reduce the user's burden. For example, if the user is relaxed, the AI ​​in the database creation unit will expand database access permissions to manage data efficiently. For example, if the user is in a hurry, the AI ​​in the database creation unit will adjust database access permissions to allow quick access to important data. This enables optimal access management tailored to the user's situation by allowing the database creation unit to adjust database access permissions 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 database creation unit may be performed using AI or not. For example, the database creation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of database access permissions.

[0098] The database creation unit can integrate with different database systems during database creation. For example, the database creation unit can use AI to integrate with different database systems and maintain data consistency. For example, the database creation unit can analyze different database systems and select the optimal integration method. For example, the database creation unit can integrate data based on different database systems. This ensures data consistency through the database creation unit's integration with different database systems. Some or all of the above processes in the database creation unit may be performed using AI, or without AI. For example, the database creation unit can input the integration with different database systems into a generating AI and have the generating AI execute the integration.

[0099] The database creation unit can back up data using cloud storage when creating a database. The database creation unit can, for example, use AI to back up data using cloud storage and ensure data security. The database creation unit can, for example, analyze cloud storage and select the optimal backup method. The database creation unit can, for example, back up data based on cloud storage. In this way, data security is ensured by the database creation unit backing up data using cloud storage. Some or all of the above processes in the database creation unit may be performed using AI, for example, or without AI. For example, the database creation unit can input cloud storage into a generation AI and have the generation AI perform the data backup.

[0100] The checking unit can estimate the user's emotions and determine the priority of checks based on the estimated emotions. For example, if the user is stressed, the AI ​​will prioritize checking important items. If the user is relaxed, the AI ​​will check detailed items. If the user is in a hurry, the AI ​​will perform the checks quickly and prioritize important items. This allows the checking unit to determine the priority of checks based on the user's emotions, enabling optimal checking tailored to the user's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input user emotion data into a generative AI and have the generative AI determine the priority of checks.

[0101] The checking unit can optimize check items by referring to past check history during the check process. For example, the checking unit can use AI to refer to past check history and select the most suitable check items. For example, the checking unit can adjust the progress of the check in real time based on past check history. For example, the checking unit can analyze past check history and determine the priority of checks. As a result, the efficiency of the check is improved by the checking unit optimizing check items by referring to past check history. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input past check history into a generating AI and have the generating AI perform the optimization of check items.

[0102] The checking unit can improve the accuracy of its checks by considering the interrelationships of different parameters during the check process. For example, the checking unit can use AI to consider the interrelationships of different parameters and improve the accuracy of its checks. For example, the checking unit can analyze the interrelationships of different parameters and select the optimal checking method. For example, the checking unit can adjust the progress of the check in real time based on the interrelationships of different parameters. As a result, the accuracy of the check is improved by the checking unit considering the interrelationships of different parameters. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the interrelationships of different parameters into a generating AI and have the generating AI perform the check accuracy improvement.

[0103] The checking unit can estimate the user's emotions and adjust the display method of the check results based on the estimated user emotions. For example, if the user is stressed, the AI ​​provides a simple and highly visible display method. If the user is relaxed, the AI ​​provides a display method that includes detailed information. If the user is in a hurry, the AI ​​provides a display method that gets straight to the point. By adjusting the display method of the check results based on the user's emotions, the checking unit enables optimal display according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the checking unit may be performed using AI, or not using AI. For example, the checking unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the check results.

[0104] The checking unit can integrate data from different devices during the checking process. For example, the checking unit can use AI to integrate data from different devices and improve the accuracy of the check. For example, the checking unit can analyze data from different devices and select the optimal checking method. For example, the checking unit can adjust the progress of the check in real time based on data from different devices. This improves the accuracy of the check by integrating data from different devices. Some or all of the above-described processes in the checking unit may be performed using AI, or without AI. For example, the checking unit can input data from different devices into a generating AI and have the generating AI perform data integration.

[0105] The checking unit can perform checks by referring to checking standards from different industries. For example, the checking unit can use AI to refer to checking standards from different industries and improve the accuracy of the checks. For example, the checking unit can analyze checking standards from different industries and select the optimal checking method. For example, the checking unit can adjust the progress of the checks in real time based on checking standards from different industries. As a result, the accuracy of the checks is improved by the checking unit referring to checking standards from different industries. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input checking standards from different industries into a generating AI and have the generating AI perform the checks.

[0106] The inquiry response unit can estimate the user's emotions and adjust the way it expresses its response based on those emotions. For example, if the user is stressed, the AI ​​will provide a concise and clear response. If the user is relaxed, the AI ​​will provide a response that includes detailed explanations. If the user is in a hurry, the AI ​​will provide a quick and to-the-point response. This allows the inquiry response unit to provide the most appropriate response for the user's situation by adjusting the way it expresses its response based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the inquiry response unit may be performed using AI or not. For example, the inquiry response unit can input user emotion data into a generative AI and have the generative AI adjust the way it expresses its response.

[0107] The inquiry response unit can provide the best possible answer by referring to past inquiry history when responding to an inquiry. For example, the inquiry response unit may use AI to refer to past inquiry history and select the best answer. For example, the inquiry response unit may adjust the progress of the answer in real time based on past inquiry history. For example, the inquiry response unit may analyze past inquiry history and determine the priority of the answers. As a result, the inquiry response unit can provide the best possible answer by referring to past inquiry history. Some or all of the above processes in the inquiry response unit may be performed using AI, for example, or without AI. For example, the inquiry response unit may input past inquiry history into a generating AI and have the generating AI execute the best possible answer.

[0108] The inquiry handling unit can automatically handle inquiries in different languages. For example, the inquiry handling unit can use AI to automatically handle inquiries in different languages, thereby eliminating language barriers. For example, the inquiry handling unit can analyze different languages ​​and select the optimal response method. For example, the inquiry handling unit can adjust the progress of the inquiry handling in real time based on the different languages. In this way, the inquiry handling unit can eliminate language barriers by automatically handling inquiries in different languages. Some or all of the above processes in the inquiry handling unit may be performed using AI, for example, or without AI. For example, the inquiry handling unit can input inquiries in different languages ​​into a generating AI and have the generating AI execute the response.

[0109] The inquiry response unit can estimate the user's emotions and adjust the level of detail in its response based on the estimated emotions. For example, if the user is stressed, the AI ​​will provide a concise and clear response. If the user is relaxed, the AI ​​will provide a response that includes detailed explanations. If the user is in a hurry, the AI ​​will provide a quick and to-the-point response. By adjusting the level of detail in the response based on the user's emotions, the inquiry response unit can provide the most appropriate response for the user's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the inquiry response unit may be performed using AI or not. For example, the inquiry response unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail in its response.

[0110] The inquiry handling department can integrate responses from different channels (email, chat, telephone) when handling inquiries. For example, the inquiry handling department can use AI to handle inquiries from different channels in an integrated manner, improving user convenience. For example, the inquiry handling department can analyze different channels and select the optimal response method. For example, the inquiry handling department can adjust the progress of inquiry handling in real time based on different channels. As a result, user convenience is improved by the inquiry handling department integrating responses from different channels. Some or all of the above processes in the inquiry handling department may be performed using AI, for example, or without AI. For example, the inquiry handling department can input inquiries from different channels into a generating AI and have the generating AI execute the response.

[0111] The inquiry handling department can provide answers by referring to inquiry handling examples from different industries when handling inquiries. For example, the inquiry handling department can use AI to refer to inquiry handling examples from different industries and provide the optimal answer. For example, the inquiry handling department can analyze inquiry handling examples from different industries and select the optimal response method. For example, the inquiry handling department can adjust the progress of inquiry handling in real time based on inquiry handling examples from different industries. As a result, the inquiry handling department can provide the optimal answer by referring to inquiry handling examples from different industries. Some or all of the above processes in the inquiry handling department may be performed using AI, for example, or without AI. For example, the inquiry handling department can input inquiry handling examples from different industries into a generating AI and have the generating AI execute the answer.

[0112] The pre-check unit can estimate the user's emotions and adjust the pre-check items based on the estimated emotions. For example, if the user is stressed, the AI ​​will prioritize checking important items. If the user is relaxed, the AI ​​will check detailed items. If the user is in a hurry, the AI ​​will perform the check quickly and prioritize important items. This allows the pre-check unit to adjust the pre-check items based on the user's emotions, enabling optimal checking according to the user's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the pre-check unit may be performed using AI or not. For example, the pre-check unit can input user emotion data into a generative AI and have the generative AI adjust the pre-check items.

[0113] The pre-check unit can optimize check items by referring to past pre-check history during the pre-check process. For example, the pre-check unit can use AI to refer to past pre-check history and select the most suitable check items. For example, the pre-check unit can adjust the progress of the check in real time based on past pre-check history. For example, the pre-check unit can analyze past pre-check history and determine the priority of checks. This improves the efficiency of the check by allowing the pre-check unit to optimize check items by referring to past pre-check history. Some or all of the above processes in the pre-check unit may be performed using AI, for example, or without AI. For example, the pre-check unit can input past pre-check history into a generating AI and have the generating AI perform the optimization of check items.

[0114] The pre-check unit can improve the accuracy of the check by considering the interrelationships of different parameters during the pre-check. For example, the pre-check unit can use AI to consider the interrelationships of different parameters and improve the accuracy of the check. For example, the pre-check unit can analyze the interrelationships of different parameters and select the optimal check method. For example, the pre-check unit can adjust the progress of the check in real time based on the interrelationships of different parameters. As a result, the accuracy of the check is improved by the pre-check unit considering the interrelationships of different parameters. Some or all of the above processing in the pre-check unit may be performed using AI, for example, or without AI. For example, the pre-check unit can input the interrelationships of different parameters into a generating AI and have the generating AI perform the check accuracy improvement.

[0115] The pre-check unit can estimate the user's emotions and adjust the display method of the pre-check results based on the estimated emotions. For example, if the user is stressed, the AI ​​provides a simple and highly visible display method. If the user is relaxed, the AI ​​provides a display method that includes detailed information. If the user is in a hurry, the AI ​​provides a display method that gets straight to the point. By adjusting the display method of the pre-check results based on the user's emotions, the pre-check unit enables optimal display according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the pre-check unit may be performed using AI, or not using AI. For example, the pre-check unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the pre-check results.

[0116] The pre-check unit can integrate data from different devices during the pre-check process. For example, the pre-check unit can use AI to integrate data from different devices and improve the accuracy of the check. For example, the pre-check unit can analyze data from different devices and select the optimal checking method. For example, the pre-check unit can adjust the progress of the check in real time based on data from different devices. This improves the accuracy of the check by integrating data from different devices. Some or all of the above-described processes in the pre-check unit may be performed using AI, or without AI. For example, the pre-check unit can input data from different devices into a generating AI and have the generating AI perform data integration.

[0117] The pre-check unit can perform checks by referring to check standards from different industries during the pre-check process. For example, the pre-check unit can use AI to refer to check standards from different industries and improve the accuracy of the checks. For example, the pre-check unit can analyze check standards from different industries and select the optimal check method. For example, the pre-check unit can adjust the progress of the checks in real time based on check standards from different industries. As a result, the accuracy of the checks is improved by the pre-check unit referring to check standards from different industries. Some or all of the above processes in the pre-check unit may be performed using AI, for example, or without AI. For example, the pre-check unit can input check standards from different industries into a generating AI and have the generating AI perform the checks.

[0118] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the AI ​​will provide concise and clear suggestions. If the user is relaxed, the AI ​​will provide suggestions with detailed explanations. If the user is in a hurry, the AI ​​will provide quick and to-the-point suggestions. This allows the suggestion unit to provide optimal suggestions tailored to the user's situation by adjusting the way suggestions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0119] The proposal department can provide the optimal proposal by referring to past proposal history when making a proposal. For example, the proposal department can use AI to refer to past proposal history and select the best proposal. For example, the proposal department can adjust the progress of proposals in real time based on past proposal history. For example, the proposal department can analyze past proposal history and determine the priority of proposals. In this way, the proposal department can provide the optimal proposal by referring to past proposal history. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input past proposal history into a generation AI and have the generation AI execute the optimal proposal.

[0120] The proposal unit can simulate different parameter combinations during the proposal process and make the optimal proposal. For example, the proposal unit can use AI to simulate different parameter combinations and make the optimal proposal. For example, the proposal unit can propose the optimal parameter combination based on the simulation results. For example, the proposal unit can analyze the simulation results and select the optimal proposal. In this way, the proposal unit makes the optimal proposal by simulating different parameter combinations. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input different parameter combinations into a generating AI and have the generating AI perform a simulation.

[0121] The suggestion unit can estimate the user's emotions and adjust the level of detail of its suggestions based on the estimated emotions. For example, if the user is stressed, the AI ​​will provide concise and clear suggestions. If the user is relaxed, the AI ​​will provide suggestions with detailed explanations. If the user is in a hurry, the AI ​​will provide quick and to-the-point suggestions. By adjusting the level of detail of suggestions based on the user's emotions, the suggestion unit can provide optimal suggestions tailored to the user's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail of its suggestions.

[0122] The proposal unit can integrate data from different devices to make proposals. For example, the proposal unit can use AI to integrate data from different devices and improve the accuracy of the proposals. For example, the proposal unit can analyze data from different devices and select the optimal proposal method. For example, the proposal unit can adjust the progress of the proposal in real time based on data from different devices. As a result, the accuracy of the proposals is improved by the proposal unit integrating data from different devices. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data from different devices into a generating AI and have the generating AI perform data integration.

[0123] The proposal department can make proposals by referring to examples of proposals from different industries. For example, the proposal department can use AI to refer to examples of proposals from different industries and provide the optimal proposal. For example, the proposal department can analyze examples of proposals from different industries and select the optimal proposal method. For example, the proposal department can adjust the progress of the proposal in real time based on examples of proposals from different industries. In this way, the proposal department can provide the optimal proposal by referring to examples of proposals from different industries. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input examples of proposals from different industries into a generation AI and have the generation AI execute the proposal.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The learning unit can estimate the user's emotions and select training data based on those estimated emotions. For example, if the user is stressed, the AI ​​will start learning with simple parameters and gradually move on to more complex ones. If the user is relaxed, the AI ​​will learn detailed parameters all at once. If the user is in a hurry, the AI ​​will prioritize learning important parameters. This allows the learning unit to select training data based on the user's emotions, enabling optimal learning tailored to the user's situation.

[0126] The database creation unit can automatically convert and unify different data formats. For example, it can use AI to automatically convert different data formats and maintain database integrity. It analyzes different data formats and selects the optimal conversion method. Based on the different data formats, it unifies the database. In this way, the database creation unit automatically converts different data formats, maintaining database integrity.

[0127] The checking unit can estimate the user's emotions and determine the priority of checks based on those emotions. For example, if the user is stressed, the AI ​​will prioritize checking important items. If the user is relaxed, the AI ​​will check detailed items. If the user is in a hurry, the AI ​​will perform the checks quickly and prioritize important items. In this way, the checking unit determines the priority of checks based on the user's emotions, enabling optimal checks tailored to the user's situation.

[0128] The customer support department can automatically handle inquiries in different languages. For example, it can use AI to automatically respond to inquiries in different languages, eliminating language barriers. It analyzes different languages ​​and selects the optimal response method. Based on the different languages, it adjusts the progress of the inquiry response in real time. In this way, the customer support department can automatically handle inquiries in different languages, eliminating language barriers.

[0129] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the AI ​​will provide concise and clear suggestions. If the user is relaxed, the AI ​​will provide suggestions that include detailed explanations. If the user is in a hurry, the AI ​​will provide quick and to-the-point suggestions. This allows the suggestion function to adjust the way suggestions are presented based on the user's emotions, enabling it to provide optimal suggestions tailored to the user's situation.

[0130] The learning unit can learn by referencing parameter settings from different industries. For example, it can use AI to improve the accuracy of learning by referencing parameter settings from different industries. It analyzes parameter settings from different industries and selects the optimal learning method. Based on the parameter settings from different industries, it adjusts the progress of learning in real time. As a result, the learning unit improves the accuracy of learning by referencing parameter settings from different industries.

[0131] The database creation unit can estimate the user's emotions and adjust the database update frequency based on that estimation. For example, if the user is stressed, the AI ​​reduces the database update frequency to alleviate the user's burden. If the user is relaxed, the AI ​​increases the database update frequency to manage data efficiently. If the user is in a hurry, the AI ​​adjusts the database update frequency, prioritizing the updating of important data. This allows the database creation unit to adjust the database update frequency based on the user's emotions, enabling optimal database management tailored to the user's situation.

[0132] The checking unit can integrate data from different devices to perform checks. For example, it can use AI to integrate data from different devices and improve the accuracy of the checks. It analyzes data from different devices and selects the optimal checking method. Based on the data from different devices, it adjusts the progress of the checks in real time. As a result, the accuracy of the checks is improved by the checking unit integrating data from different devices.

[0133] The pre-check unit can estimate the user's emotions and adjust the pre-check items based on those emotions. For example, if the user is stressed, the AI ​​will prioritize checking important items. If the user is relaxed, the AI ​​will check detailed items. If the user is in a hurry, the AI ​​will perform the check quickly and prioritize important items. In this way, the pre-check unit adjusts the pre-check items based on the user's emotions, enabling an optimal check tailored to the user's situation.

[0134] The proposal department can simulate different parameter combinations and make optimal suggestions. For example, it can use AI to simulate different parameter combinations and make optimal suggestions. Based on the simulation results, it proposes the optimal parameter combination. It analyzes the simulation results and selects the best suggestion. In this way, the proposal department makes optimal suggestions by simulating different parameter combinations.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The learning unit learns master parameters, configuration patterns, and station information. The learning unit learns a large amount of data, for example using AI, while maintaining relationships between them, and understands each combination of parameters. This allows it to efficiently extract the information needed during setup. Step 2: The database creation unit creates a database of stations where the relevant parameter has been entered. The database creation unit can, for example, use AI to automatically acquire data from all stations, check the settings of each station by comparing them with master parameters and configuration patterns, and report an alert if there are any misconfigurations. Step 3: The checking unit periodically verifies that there are no omissions or errors in the parameters of existing stations based on the database created by the database creation unit. For example, the checking unit can periodically use AI to automatically acquire data from all stations, check the settings of each station by comparing them with master parameters and configuration patterns, and report an alert if there are any misconfigurations. Step 4: The inquiry response department uses a chatbot to handle inquiries about various parameters. For example, when a worker asks a question via chat about a parameter they want to confirm, the inquiry response department can use AI to extract the relevant parameter and provide an answer including related parameters. Step 5: The pre-check unit performs pre-checks to ensure that incorrect parameters are not entered when a new station is installed or the configuration is changed. For example, by using AI to check the configuration data before the setup work is completed, the pre-check unit can instantly detect misconfigurations or unexpected omissions. Step 6: The proposal unit suggests appropriate parameters for special situations such as events or disasters. For example, the proposal unit can use AI to learn special patterns and suggest parameter settings tailored to the situation.

[0137] 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.

[0138] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0139] 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.

[0140] Each of the multiple elements described above, including the learning unit, database creation unit, checking unit, query handling unit, pre-check unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The database creation unit is implemented by the specific processing unit 290 of the data processing unit 12. The checking unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The query handling unit is implemented by the control unit 46A of the smart device 14. The pre-check unit is implemented by the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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.

[0156] Each of the multiple elements described above, including the learning unit, database creation unit, checking unit, query handling unit, pre-check unit, and proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The database creation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The checking unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The query handling unit is implemented, for example, by the control unit 46A of the smart glasses 214. The pre-check unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] 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.

[0172] Each of the multiple elements described above, including the learning unit, database creation unit, checking unit, query handling unit, pre-check unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The database creation unit is implemented by the specific processing unit 290 of the data processing unit 12. The checking unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The query handling unit is implemented by the control unit 46A of the headset terminal 314. The pre-check unit is implemented by the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0188] 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.

[0189] Each of the multiple elements described above, including the learning unit, database creation unit, checking unit, query handling unit, pre-check unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The database creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The checking unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The query handling unit is implemented by, for example, the control unit 46A of the robot 414. The pre-check unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) A learning unit that learns master parameters, configuration patterns, and station information, A database creation unit that creates a database of stations where the relevant parameter is entered, A checking unit periodically checks whether there are any omissions or errors in the parameters of existing stations based on the database created by the aforementioned database creation unit, The inquiry handling department uses a chatbot to respond to inquiries regarding various parameters, A pre-check unit performs pre-checks to prevent incorrect parameters from being entered when a new station or configuration is changed, It includes a proposal unit that suggests appropriate parameters in special situations such as events or disasters. A system characterized by the following features. (Note 2) The aforementioned learning unit, Learn master parameters, configuration patterns, and station information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned database creation unit, Create a database of stations where the relevant parameter is entered. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned checking unit is The database created by the aforementioned database creation unit is periodically checked to ensure that there are no omissions or errors in the parameters of existing stations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned inquiry handling department, We handle inquiries about various parameters via chatbot. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned pre-check unit is: Pre-checks are performed to prevent incorrect parameters from being entered when new stations are introduced or configurations are changed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, We propose appropriate parameters for special situations such as events and disasters. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, During training, different parameter combinations are simulated to find the optimal configuration pattern. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, During training, information from different data sources is integrated for learning. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, During training, the system learns by referencing parameter settings from different industries. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned database creation unit, The system estimates user sentiment and adjusts the database update frequency based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned database creation unit, When creating a database, automatically convert and unify different data formats. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned database creation unit, When creating a database, the system checks for data integrity and automatically corrects outliers. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned database creation unit, It estimates user sentiment and adjusts database access permissions based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned database creation unit, When creating a database, it is necessary to integrate it with a different database system. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned database creation unit, When creating a database, use cloud storage to back up the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned checking unit is The system estimates the user's emotions and prioritizes checks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned checking unit is During the check, the check items are optimized by referring to past check history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned checking unit is During the check, the interrelationships of different parameters are taken into consideration to improve the accuracy of the check. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned checking unit is The system estimates the user's emotions and adjusts how the check results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned checking unit is During the check, data from different devices is integrated and checked. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned checking unit is During the inspection process, we refer to inspection standards from different industries. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned inquiry handling department, It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned inquiry handling department, When responding to inquiries, we refer to past inquiry history to provide the most appropriate answer. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned inquiry handling department, The system automatically handles inquiries in different languages. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned inquiry handling department, The system estimates the user's emotions and adjusts the level of detail in the responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned inquiry handling department, Integrate responses from different channels when handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned inquiry handling department, When responding to inquiries, refer to examples of inquiry handling from different industries to provide answers. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned pre-check unit is: The system estimates the user's emotions and adjusts the pre-check items based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned pre-check unit is: During the pre-check, the check items are optimized by referring to past pre-check history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned pre-check unit is: During the pre-check, we improve the accuracy of the check by considering the interrelationships of different parameters. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned pre-check unit is: The system estimates the user's emotions and adjusts how the pre-check results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned pre-check unit is: During the pre-check, data from different devices is integrated and checked. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned pre-check unit is: During the pre-check, we refer to check standards from different industries. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned proposal section is, When making a proposal, we refer to past proposal history to provide the most suitable proposal. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned proposal section is, When making a proposal, we simulate different parameter combinations to provide the optimal solution. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned proposal section is, It estimates the user's emotions and adjusts the level of detail of suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned proposal section is, When making a proposal, we integrate data from different devices to create a proposal. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned proposal section is, When making a proposal, refer to examples of proposals from different industries. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0209] 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 learning unit that learns master parameters, configuration patterns, and station information, A database creation unit that creates a database of stations where the relevant parameter is entered, A checking unit periodically checks whether there are any omissions or errors in the parameters of existing stations based on the database created by the aforementioned database creation unit, The inquiry handling department uses a chatbot to respond to inquiries regarding various parameters, A pre-check unit performs pre-checks to prevent incorrect parameters from being entered when a new station or configuration is changed, It includes a proposal unit that suggests appropriate parameters in special situations such as events or disasters. A system characterized by the following features.

2. The aforementioned learning unit, Learn master parameters, configuration patterns, and station information. The system according to feature 1.

3. The aforementioned database creation unit, Create a database of stations where the relevant parameter is entered. The system according to feature 1.

4. The aforementioned checking unit is The database created by the aforementioned database creation unit is periodically checked to ensure that there are no omissions or errors in the parameters of existing stations. The system according to feature 1.

5. The aforementioned inquiry handling department, We handle inquiries about various parameters via chatbot. The system according to feature 1.

6. The aforementioned pre-check unit is Pre-checks are performed to prevent incorrect parameters from being entered when new stations are introduced or configurations are changed. The system according to feature 1.

7. The aforementioned proposal section is, We propose appropriate parameters for special situations such as events and disasters. The system according to feature 1.

8. The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system according to feature 1.

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