System

Abstract

An object of a system according to an embodiment is to objectively evaluate the performance and quality of a base station and provide appropriate feedback.SOLUTION: A system includes an input unit, a data analysis unit, an evaluation unit, and a feedback providing unit. The input unit inputs the type and specifications of the base station. The data analysis unit evaluates the performance and quality of the base station based on the type and specifications of the base station input by the input unit. The evaluation unit generates an objective index based on the performance and quality of the base station evaluated by the data analysis unit. The feedback providing unit provides feedback based on the index generated by the evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to objectively evaluate the performance and quality of base stations and provide appropriate feedback.

[0005] The system according to the embodiment aims to objectively evaluate the performance and quality of a base station and provide appropriate feedback. [Means for solving the problem]

[0006] A system according to an embodiment includes an input unit, a data analysis unit, an evaluation unit, and a feedback providing unit. The input unit inputs the type and specifications of a base station. The data analysis unit evaluates the performance and quality of the base station based on the type and specifications of the base station input by the input unit. The evaluation unit generates objective indicators based on the performance and quality of the base station evaluated by the data analysis unit. The feedback providing unit provides feedback based on the indicators generated by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can objectively evaluate the performance and quality of a base station and provide appropriate feedback. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A base station evaluation system according to an embodiment of the present invention is a system that evaluates the performance and quality of a base station using a generative AI, thereby providing objective indicators and feedback regarding the performance and quality of the base station.

[0029] A base station evaluation system according to an embodiment includes an input unit, a data analysis unit, an evaluation unit, and a feedback provision unit. The input unit inputs the type and specifications of the base station. For example, the input unit inputs the model name, manufacturer, and technical specifications (such as frequency band, output, and coverage range) of the base station. The input unit also inputs operation data and performance data of the base station. For example, the input unit provides data such as the base station's operating time, traffic data, error log, and maintenance history. The data analysis unit evaluates the performance and quality of the base station based on the type and specifications of the base station input by the input unit. For example, the generation AI analyzes the base station's operating time and traffic data to evaluate the base station's stability and efficiency. The generation AI also analyzes the error log and maintenance history to evaluate the base station's reliability and the need for maintenance. The evaluation unit generates objective indicators based on the performance and quality of the base station evaluated by the data analysis unit. For example, the evaluation unit provides indicators such as the base station's operating rate, traffic processing capacity, error rate, and maintenance frequency. The feedback provision unit provides feedback based on the indicators generated by the evaluation unit. For example, the evaluation unit provides specific suggestions and advice for improving the performance and quality of the base station. As a result, the base station evaluation system according to the embodiment can efficiently and accurately evaluate the performance and quality of a base station and provide specific indicators and feedback for improvement.

[0030] The input section uses a generation AI to automatically suggest the optimal input format, preventing user input errors. For example, when a user inputs the type and specifications of a base station, the generation AI automatically suggests the optimal input format. For example, when a user inputs the model name of a base station, the generation AI automatically displays other necessary specifications related to that model. Furthermore, when a user inputs base station specifications, the generation AI checks the input in real time, detecting incorrect or incomplete information and prompting corrections. For example, a warning is displayed if the frequency band is incorrect. Furthermore, when a user inputs the type and specifications of a base station, the generation AI refers to past input data and suggests the most common input patterns to the user. For example, it automatically completes specifications commonly used for base stations from a specific manufacturer. This prevents user input errors and supports accurate data entry.

[0031] The input unit can automatically retrieve performance data of similar base stations from a past database and provide it to the user as reference information. For example, when the input unit inputs the type and specifications of a base station, the generation AI automatically retrieves performance data of similar base stations from a past database and provides it to the user as reference information. For example, it displays performance data of different models from the same manufacturer. Furthermore, when the input unit inputs the specifications of a base station, the generation AI searches the past database and displays operation data and performance data of similar base stations. This makes it easy for the user to compare it with other base stations. Furthermore, when the input unit inputs the type and specifications of a base station, the generation AI retrieves error logs and maintenance histories of similar base stations from a past database and provides them to the user as reference information. For example, it displays error logs of base stations using the same frequency band. This makes it easy for the user to compare it with other base stations.

[0032] The input unit can enable voice input using voice recognition technology. For example, when inputting the type and specifications of a base station, the input unit enables voice input using voice recognition technology. For example, the user can input the model name and technical specifications of the base station by voice. Furthermore, when the input unit uses voice recognition technology to input the base station specifications by voice, the generation AI converts the voice to text in real time and confirms the input content. For example, the frequency band input by voice is displayed as text. Furthermore, when inputting the type and specifications of a base station by voice, the input unit analyzes the user's voice command using voice recognition technology, and the generation AI automatically completes the appropriate input fields. For example, based on the manufacturer input by voice, the relevant technical specifications are automatically displayed. This simplifies the user's input work through voice input.

[0033] The input unit can provide an interactive dashboard for comparing the specifications of different base stations, allowing the user to make a visual comparison. For example, when inputting the type and specifications of a base station, the input unit provides an interactive dashboard for comparing the specifications of different base stations. For example, the technical specifications of multiple base stations can be visually compared using graphs and tables. The input unit also uses the interactive dashboard to allow the user to visually compare the specifications of different base stations. For example, the frequency bands and outputs of base stations can be displayed in graphs to make the comparison easier. The input unit also provides an interactive dashboard when inputting the type and specifications of a base station, allowing the user to visually compare performance data and operation data of different base stations. For example, uptime and traffic data can be displayed in graphs. This allows the user to visually compare the specifications of different base stations.

[0034] The data analysis unit can automatically detect outliers in the data and prompt the user to make corrections. For example, when inputting base station operation data, the generation AI automatically detects outliers in the data and prompts the user to make corrections. For example, if there is an abnormal peak in the traffic data, a warning is displayed. The data analysis unit also has the generation AI analyze base station performance data, detect outliers, and prompt the user to make corrections. For example, if the uptime is abnormally short, the data analysis unit identifies the cause and suggests corrections. The data analysis unit also has the generation AI detect outliers in the data in real time when inputting base station data and prompt the user to make corrections. For example, a warning is displayed if there is an abnormal entry in the error log. This automatically detects outliers in the data and prompts the user to make corrections, thereby maintaining data accuracy.

[0035] The data analysis unit displays trends by comparing with past data, making it easier for users to understand data fluctuations. For example, when base station operation data is input, the data analysis unit has the generation AI compare it with past data and display trends. For example, the data analysis unit displays fluctuations in traffic data over the past year in a graph, making it easier for users to understand data fluctuations. The data analysis unit also has the generation AI analyze base station performance data and display trends by comparing with past data. For example, the data analysis unit displays fluctuations in operating time in a graph, making it easier for users to visually grasp data fluctuations. The data analysis unit also has the generation AI compare it with past data when base station data is input and display trends by comparing with past data, making it easier for users to understand data fluctuations. For example, the data analysis unit displays fluctuations in the frequency of error log occurrences in a graph, making it easier for users to visually understand data fluctuations.

[0036] The data analysis unit allows data input via a smartphone app, facilitating on-site data collection. The data analysis unit, for example, allows base station data input via a smartphone app, facilitating on-site data collection. For example, an app is provided that allows base station operating time and traffic data to be directly input on-site. The data analysis unit also uses a smartphone app to collect base station operation data on-site and input it into the generation AI. For example, on-site maintenance history and error logs are input via the app. The data analysis unit also streamlines on-site data collection by inputting base station data via a smartphone app. For example, on-site traffic data and operating time are input via the app and sent to the generation AI in real time. This facilitates on-site data collection.

[0037] The data analysis unit can automatically unify data formats and maintain data consistency. For example, when data is input from a base station, the generation AI automatically unifies the data format, maintaining data consistency. For example, it converts traffic data input in different formats into a unified format. The data analysis unit also has the generation AI analyze the base station's operational data and automatically unifies the data format. For example, it unifies the format of operating time and error logs, maintaining data consistency. The data analysis unit also has the generation AI automatically unify the data format when data is input from a base station, maintaining data consistency. For example, it unifies the format of maintenance history, ensuring data consistency. This maintains data consistency.

[0038] The evaluation unit can evaluate the reliability of the analysis results and display the reliability to the user. For example, when the generation AI performs data analysis, the evaluation unit evaluates the reliability of the analysis results and displays the reliability to the user. For example, the reliability of the analysis results may be displayed as a percentage, allowing the user to understand the reliability of the results. The evaluation unit also evaluates the reliability of the analysis results and displays the reliability to the user during the process of the generation AI performing data analysis. For example, the reliability may be calculated based on the quality and quantity of the data used in the analysis. The evaluation unit also evaluates the reliability of the analysis results and displays the reliability to the user when the generation AI performs data analysis. For example, the reliability of the analysis results may be displayed in different colors, allowing the user to intuitively understand. This provides the user with the reliability of the analysis results.

[0039] The evaluation unit detects anomalies and can identify the cause if an abnormality occurs. The evaluation unit detects anomalies, for example, when the generation AI performs data analysis, and identifies the cause if an abnormality occurs. For example, if there is an abnormal peak in traffic data, it identifies the cause. The evaluation unit also detects anomalies during the process of the generation AI analyzing data, and identifies the cause if an abnormality occurs. For example, if the operating time is abnormally short, it identifies the cause. The evaluation unit also detects anomalies when the generation AI performs data analysis, and identifies the cause if an abnormality occurs. For example, if there is an abnormal entry in the error log, it identifies the cause. This makes it possible to quickly respond if an abnormality occurs by identifying the cause.

[0040] The evaluation unit can display the results using different visualization techniques to enable users to intuitively understand the data. For example, the generation AI performs data analysis in the evaluation unit, and then displays the analysis results using different visualization techniques, such as 3D graphs and heat maps. For example, the evaluation unit displays base station traffic data in a 3D graph to enable users to intuitively understand the data. The evaluation unit also displays the analysis results in a heat map to enable users to intuitively understand the data distribution and fluctuations. For example, the frequency of base station error logs is displayed in a heat map. The evaluation unit also performs data analysis in the generation AI, and then displays the analysis results using different visualization techniques. For example, the operation time of a base station is displayed in a 3D graph to enable users to intuitively understand the data. This allows users to intuitively understand the analysis results.

[0041] The evaluation unit can integrate the data with other related data to perform a comprehensive evaluation. For example, when the generation AI performs data analysis, the evaluation unit integrates the data with other related data (e.g., weather data and geographic information) to perform a more comprehensive evaluation. For example, the evaluation unit integrates base station traffic data and weather data for analysis. The evaluation unit also integrates the data with other related data (e.g., geographic information) to perform a more comprehensive evaluation of the performance and quality of the base station. For example, the evaluation unit integrates base station location information and traffic data for analysis. The evaluation unit also integrates the data with other related data (e.g., weather data) when the generation AI performs data analysis to perform a more comprehensive evaluation. For example, the evaluation unit integrates base station operating hours and weather data for analysis. In this way, by integrating related data, a more comprehensive evaluation is possible.

[0042] The feedback providing unit can add a user-customizable filtering function to display only the necessary information. For example, the feedback providing unit adds a user-customizable filtering function to the indices provided by the generation AI to display only the necessary information. For example, the user selects and displays specific indices (such as availability rate or error rate). The feedback providing unit also uses the filtering function to enable the user to display only the information they need. For example, data from a specific period or data from a specific base station is filtered and displayed. The feedback providing unit also adds a user-customizable filtering function to the indices provided by the generation AI to display only the necessary information. For example, the user selects and displays indices that interest them. This enables the user to display only the information they need.

[0043] The feedback providing unit allows the generation AI to personalize the feedback based on the user's past behavioral history and make more specific suggestions. For example, the generation AI personalizes the feedback based on the user's past behavioral history and makes more specific suggestions. For example, the generation AI makes optimal suggestions to the user based on past data input history and analysis results. The feedback providing unit also analyzes the user's past behavioral history and the generation AI makes personalized suggestions. For example, the generation AI suggests the next maintenance date based on past maintenance history. The feedback providing unit also personalizes the feedback based on the user's past behavioral history and makes more specific suggestions. For example, the generation AI suggests preventive measures for errors based on past error logs. This allows the generation AI to make more specific suggestions to the user.

[0044] The feedback providing unit can add a function that allows sharing with other users and promote community-based feedback. The feedback providing unit, for example, adds a function that allows indicators provided by the generation AI to be shared with other users and promotes community-based feedback. For example, data is shared with other users who operate the same base station and opinions are exchanged. The feedback providing unit also promotes community-based feedback using the function that allows sharing with other users. For example, a user makes their own data public and receives advice from other users. The feedback providing unit also adds a function that allows indicators provided by the generation AI to be shared with other users and promotes community-based feedback. For example, a user shares their own data and receives feedback from other users. This promotes community-based feedback.

[0045] The feedback providing unit can provide feedback in different formats to make it easier for the user to understand. For example, the feedback providing unit provides the feedback provided by the generation AI in different formats (for example, video explanations or infographics) to make it easier for the user to understand. For example, base station performance data is explained using video explanations. The feedback providing unit also provides feedback in different formats to make it easier for the user to understand. For example, base station operation data is displayed using infographics. The feedback providing unit also provides the feedback provided by the generation AI in different formats to make it easier for the user to understand. For example, base station error logs are explained using video explanations. In this way, feedback is provided in a format that is easier for the user to understand.

[0046] The feedback providing unit strengthens the anomaly detection algorithm and can immediately issue an alert if an anomaly occurs. For example, the generation AI continuously monitors the performance and quality of the base station and strengthens the anomaly detection algorithm. For example, an alert is issued immediately if there is an abnormal peak in the traffic data. The feedback providing unit also strengthens the anomaly detection algorithm and immediately issues an alert if an abnormality occurs in the performance or quality of the base station. For example, an alert is issued if the uptime is abnormally short. The feedback providing unit also strengthens the anomaly detection algorithm and strengthens the anomaly detection algorithm. For example, an alert is issued immediately if there is an abnormal entry in the error log. This allows an alert to be issued immediately if an abnormality occurs.

[0047] The feedback providing unit automatically backs up monitoring data to prevent data loss. For example, the generation AI continuously monitors the performance and quality of the base station and automatically backs up the monitoring data. For example, traffic data and uptime data are backed up regularly. The feedback providing unit also automatically backs up monitoring data to prevent data loss. For example, error log and maintenance history data are backed up regularly. The feedback providing unit also has the generation AI continuously monitor the performance and quality of the base station and automatically backs up monitoring data. For example, base station operation data is backed up regularly. This prevents data loss.

[0048] The feedback providing unit can add a function to send notifications to a smartwatch or smartphone, allowing the user to check the results anywhere. For example, the generation AI continuously monitors the performance and quality of a base station and adds a function to send notifications to a smartwatch or smartphone. For example, it notifies a smartphone of base station traffic data and operating time data. The feedback providing unit also adds a function to send notifications to a smartwatch or smartphone, allowing the user to check the results anywhere. For example, it notifies a smartwatch of base station error log and maintenance history data. The feedback providing unit also adds a function to send notifications to a smartwatch or smartphone, allowing the generation AI continuously monitors the performance and quality of a base station and adds a function to send notifications to a smartwatch or smartphone. For example, it notifies a smartphone of base station operation data. This allows the user to check the results anywhere.

[0049] The feedback providing unit can work in conjunction with other systems to enable integrated management. For example, the generation AI in the feedback providing unit continuously monitors the performance and quality of base stations and works in conjunction with other systems (e.g., a company's operations management system). For example, it integrates base station traffic data and uptime data into the company's operations management system. The feedback providing unit also works in conjunction with other systems to enable integrated management. For example, it integrates base station error log and maintenance history data into the company's operations management system. The feedback providing unit also works in conjunction with other systems to enable integrated management. For example, it integrates base station operation data into the company's operations management system. The generation AI in the feedback providing unit continuously monitors the performance and quality of base stations and works in conjunction with other systems. For example, it integrates base station operations data into the company's operations management system. This enables it to work in conjunction with other systems to enable integrated management.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The base station evaluation system can further include an anomaly detection unit. The anomaly detection unit can monitor the base station's operational data and performance data in real time and detect abnormal patterns. For example, it can issue a warning if an abnormal peak occurs in traffic data or if the uptime is abnormally short. The anomaly detection unit can also analyze error logs and maintenance history to identify the cause of the abnormality. For example, if a specific part is frequently failing, it can suggest replacing that part. This can improve the operational stability of the base station.

[0052] The base station evaluation system can further include a prediction unit. The prediction unit can predict future base station performance based on past data. For example, it can analyze past traffic data and predict future traffic increases. The prediction unit can also predict the next maintenance period based on maintenance history. For example, it can predict the lifespan of a specific part and suggest when it should be replaced. This allows for efficient operation planning of base stations.

[0053] The base station evaluation system can further include an energy management unit. The energy management unit can monitor the energy consumption of the base station and propose efficient energy usage. For example, it can identify peak energy consumption times based on the base station's operating hours and traffic data and propose optimization of energy usage. The energy management unit can also make proposals to promote the use of renewable energy. For example, it can propose the introduction of a solar power generation system. This can improve the energy efficiency of the base station.

[0054] The base station evaluation system can further include a security management unit. The security management unit can monitor the security status of the base station and detect potential threats. For example, it can detect unauthorized access attempts or abnormal data traffic and issue an alert. The security management unit can also monitor compliance with security policies and suggest necessary measures. For example, it can suggest the regular application of security patches. This can strengthen the security of the base station.

[0055] The base station evaluation system may further include a user feedback unit. The user feedback unit may collect feedback from users of the base station and reflect it in the evaluation. For example, users may provide opinions about the coverage and connection quality of the base station. The user feedback unit may also analyze the collected feedback and identify areas for improvement in the base station. For example, the user feedback unit may identify connection problems in a specific area and propose solutions to improve the base station. This may improve user satisfaction.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The input unit inputs the type and specifications of the base station. For example, the model name, manufacturer, and technical specifications (frequency band, output, coverage range, etc.) of the base station are input. The input unit also inputs operational and performance data of the base station. For example, data such as the base station's operating time, traffic data, error log, and maintenance history is provided. Step 2: The data analysis unit evaluates the performance and quality of the base station based on the type and specifications of the base station entered by the input unit. For example, the generation AI analyzes the base station's operating time and traffic data to evaluate the base station's stability and efficiency. The generation AI also analyzes error logs and maintenance history to evaluate the base station's reliability and the need for maintenance. Step 3: The evaluation unit generates objective indicators based on the performance and quality of the base station evaluated by the data analysis unit. For example, it provides indicators such as the base station's availability rate, traffic processing capacity, error rate, and maintenance frequency. Step 4: The feedback providing unit provides feedback based on the indicators generated by the evaluation unit, for example, providing specific suggestions or advice for improving the performance or quality of the base station.

[0058] (Example 2) A base station evaluation system according to an embodiment of the present invention is a system that evaluates the performance and quality of a base station using a generative AI, thereby providing objective indicators and feedback regarding the performance and quality of the base station.

[0059] A base station evaluation system according to an embodiment includes an input unit, a data analysis unit, an evaluation unit, and a feedback provision unit. The input unit inputs the type and specifications of the base station. For example, the input unit inputs the model name, manufacturer, and technical specifications (such as frequency band, output, and coverage range) of the base station. The input unit also inputs operation data and performance data of the base station. For example, the input unit provides data such as the base station's operating time, traffic data, error log, and maintenance history. The data analysis unit evaluates the performance and quality of the base station based on the type and specifications of the base station input by the input unit. For example, the generation AI analyzes the base station's operating time and traffic data to evaluate the base station's stability and efficiency. The generation AI also analyzes the error log and maintenance history to evaluate the base station's reliability and the need for maintenance. The evaluation unit generates objective indicators based on the performance and quality of the base station evaluated by the data analysis unit. For example, the evaluation unit provides indicators such as the base station's operating rate, traffic processing capacity, error rate, and maintenance frequency. The feedback provision unit provides feedback based on the indicators generated by the evaluation unit. For example, the evaluation unit provides specific suggestions and advice for improving the performance and quality of the base station. As a result, the base station evaluation system according to the embodiment can efficiently and accurately evaluate the performance and quality of a base station and provide specific indicators and feedback for improvement.

[0060] The input section uses a generation AI to automatically suggest the optimal input format, preventing user input errors. For example, when a user inputs the type and specifications of a base station, the generation AI automatically suggests the optimal input format. For example, when a user inputs the model name of a base station, the generation AI automatically displays other necessary specifications related to that model. Furthermore, when a user inputs base station specifications, the generation AI checks the input in real time, detecting incorrect or incomplete information and prompting corrections. For example, a warning is displayed if the frequency band is incorrect. Furthermore, when a user inputs the type and specifications of a base station, the generation AI refers to past input data and suggests the most common input patterns to the user. For example, it automatically completes specifications commonly used for base stations from a specific manufacturer. This prevents user input errors and supports accurate data entry.

[0061] The input unit can automatically retrieve performance data of similar base stations from a past database and provide it to the user as reference information. For example, when the input unit inputs the type and specifications of a base station, the generation AI automatically retrieves performance data of similar base stations from a past database and provides it to the user as reference information. For example, it displays performance data of different models from the same manufacturer. Furthermore, when the input unit inputs the specifications of a base station, the generation AI searches the past database and displays operation data and performance data of similar base stations. This makes it easy for the user to compare it with other base stations. Furthermore, when the input unit inputs the type and specifications of a base station, the generation AI retrieves error logs and maintenance histories of similar base stations from a past database and provides them to the user as reference information. For example, it displays error logs of base stations using the same frequency band. This makes it easy for the user to compare it with other base stations.

[0062] The input unit can use the emotion estimation function to detect the user's stress level when entering data and make suggestions to simplify the input process. For example, when entering the type and specifications of a base station, the input unit can use the emotion estimation function to detect the user's stress level and make suggestions to simplify the input process. For example, if the user is feeling stressed, the input unit can make a suggestion to reduce the number of input items. The input unit can also use the emotion estimation function to monitor the user's stress level when entering data in real time, and if the user's stress level is high, display a guide to simplify the input process. For example, it can provide hints to simplify the input procedure. The input unit can also use the emotion estimation function to detect the user's stress level when entering the type and specifications of a base station and provide an interface to simplify the input process. For example, if the user's stress level is high, the input unit can enable a function to automatically complete input items. This reduces the user's stress and makes the input process more efficient.

[0063] The input unit can enable voice input using voice recognition technology. For example, when inputting the type and specifications of a base station, the input unit enables voice input using voice recognition technology. For example, the user can input the model name and technical specifications of the base station by voice. Furthermore, when the input unit uses voice recognition technology to input the base station specifications by voice, the generation AI converts the voice to text in real time and confirms the input content. For example, the frequency band input by voice is displayed as text. Furthermore, when inputting the type and specifications of a base station by voice, the input unit analyzes the user's voice command using voice recognition technology, and the generation AI automatically completes the appropriate input fields. For example, based on the manufacturer input by voice, the relevant technical specifications are automatically displayed. This simplifies the user's input work through voice input.

[0064] The input unit can provide an interactive dashboard for comparing the specifications of different base stations, allowing the user to make a visual comparison. For example, when inputting the type and specifications of a base station, the input unit provides an interactive dashboard for comparing the specifications of different base stations. For example, the technical specifications of multiple base stations can be visually compared using graphs and tables. The input unit also uses the interactive dashboard to allow the user to visually compare the specifications of different base stations. For example, the frequency bands and outputs of base stations can be displayed in graphs to make the comparison easier. The input unit also provides an interactive dashboard when inputting the type and specifications of a base station, allowing the user to visually compare performance data and operation data of different base stations. For example, uptime and traffic data can be displayed in graphs. This allows the user to visually compare the specifications of different base stations.

[0065] The input unit uses the emotion estimation function to analyze the emotion of the user when entering information and provides positive feedback to promote the input work. For example, when entering the type and specifications of a base station, the input unit uses the emotion estimation function to analyze the user's emotion and provide positive feedback. For example, if the user shows positive emotion in response to the input, an encouraging message is displayed. The input unit also uses the emotion estimation function to analyze the emotion of the user when entering the specifications of the base station in real time and provide positive feedback. For example, a message praising the accuracy of the input is displayed. The input unit also uses the emotion estimation function to analyze the emotion of the user when entering the type and specifications of a base station and provides positive feedback to promote the input work. For example, a message that gives a sense of accomplishment is displayed when the input is completed. This promotes the user's input work in a positive manner.

[0066] The data analysis unit can automatically detect outliers in the data and prompt the user to make corrections. For example, when inputting base station operation data, the generation AI automatically detects outliers in the data and prompts the user to make corrections. For example, if there is an abnormal peak in the traffic data, a warning is displayed. The data analysis unit also has the generation AI analyze base station performance data, detect outliers, and prompt the user to make corrections. For example, if the uptime is abnormally short, the data analysis unit identifies the cause and suggests corrections. The data analysis unit also has the generation AI detect outliers in the data in real time when inputting base station data and prompt the user to make corrections. For example, a warning is displayed if there is an abnormal entry in the error log. This automatically detects outliers in the data and prompts the user to make corrections, thereby maintaining data accuracy.

[0067] The data analysis unit displays trends by comparing with past data, making it easier for users to understand data fluctuations. For example, when base station operation data is input, the data analysis unit has the generation AI compare it with past data and display trends. For example, the data analysis unit displays fluctuations in traffic data over the past year in a graph, making it easier for users to understand data fluctuations. The data analysis unit also has the generation AI analyze base station performance data and display trends by comparing with past data. For example, the data analysis unit displays fluctuations in operating time in a graph, making it easier for users to visually grasp data fluctuations. The data analysis unit also has the generation AI compare it with past data when base station data is input and display trends by comparing with past data, making it easier for users to understand data fluctuations. For example, the data analysis unit displays fluctuations in the frequency of error log occurrences in a graph, making it easier for users to visually understand data fluctuations.

[0068] The data analysis unit can use the emotion estimation function to detect in real time any anxiety or doubt the user may have while entering data and provide appropriate support. For example, when entering base station operation data, the data analysis unit uses the emotion estimation function to detect in real time any anxiety or doubt the user may have and provide appropriate support. For example, if the user is feeling anxious, an input guide is displayed. The data analysis unit also uses the emotion estimation function to detect in real time any anxiety or doubt the user may have while entering base station data and provide support. For example, if there is a question about the input content, an FAQ is displayed. The data analysis unit also uses the emotion estimation function to detect any anxiety or doubt the user may have while entering base station data and provide appropriate support. For example, if the user has difficulty entering data, a support chat is launched. In this way, the data analysis unit can detect in real time any anxiety or doubt the user may have while entering data and provide appropriate support.

[0069] The data analysis unit allows data input via a smartphone app, facilitating on-site data collection. The data analysis unit, for example, allows base station data input via a smartphone app, facilitating on-site data collection. For example, an app is provided that allows base station operating time and traffic data to be directly input on-site. The data analysis unit also uses a smartphone app to collect base station operation data on-site and input it into the generation AI. For example, on-site maintenance history and error logs are input via the app. The data analysis unit also streamlines on-site data collection by inputting base station data via a smartphone app. For example, on-site traffic data and operating time are input via the app and sent to the generation AI in real time. This facilitates on-site data collection.

[0070] The data analysis unit can automatically unify data formats and maintain data consistency. For example, when data is input from a base station, the generation AI automatically unifies the data format, maintaining data consistency. For example, it converts traffic data input in different formats into a unified format. The data analysis unit also has the generation AI analyze the base station's operational data and automatically unifies the data format. For example, it unifies the format of operating time and error logs, maintaining data consistency. The data analysis unit also has the generation AI automatically unify the data format when data is input from a base station, maintaining data consistency. For example, it unifies the format of maintenance history, ensuring data consistency. This maintains data consistency.

[0071] The data analysis unit can use the emotion estimation function to analyze the emotions felt by the user while entering data and make suggestions to improve the efficiency of the entry work. For example, when entering data for a base station, the data analysis unit uses the emotion estimation function to analyze the user's emotions and make suggestions to improve the efficiency of the entry work. For example, if the user is feeling stressed, the data analysis unit makes a suggestion to simplify the entry procedure. The data analysis unit also uses the emotion estimation function to analyze the emotions felt by the user while entering data in real time and make suggestions to improve the efficiency of the entry work. For example, if the user is feeling tired, the data analysis unit displays a message encouraging the user to take a break. The data analysis unit also uses the emotion estimation function to analyze the emotions felt by the user while entering data for the base station and make suggestions to improve the efficiency of the entry work. For example, if the user is lacking concentration, the data analysis unit enables a function to automatically complete input items. This improves the efficiency of the user's entry work.

[0072] The evaluation unit can evaluate the reliability of the analysis results and display the reliability to the user. For example, when the generation AI performs data analysis, the evaluation unit evaluates the reliability of the analysis results and displays the reliability to the user. For example, the reliability of the analysis results may be displayed as a percentage, allowing the user to understand the reliability of the results. The evaluation unit also evaluates the reliability of the analysis results and displays the reliability to the user during the process of the generation AI performing data analysis. For example, the reliability may be calculated based on the quality and quantity of the data used in the analysis. The evaluation unit also evaluates the reliability of the analysis results and displays the reliability to the user when the generation AI performs data analysis. For example, the reliability of the analysis results may be displayed in different colors, allowing the user to intuitively understand. This provides the user with the reliability of the analysis results.

[0073] The evaluation unit detects anomalies and can identify the cause if an abnormality occurs. The evaluation unit detects anomalies, for example, when the generation AI performs data analysis, and identifies the cause if an abnormality occurs. For example, if there is an abnormal peak in traffic data, it identifies the cause. The evaluation unit also detects anomalies during the process of the generation AI analyzing data, and identifies the cause if an abnormality occurs. For example, if the operating time is abnormally short, it identifies the cause. The evaluation unit also detects anomalies when the generation AI performs data analysis, and identifies the cause if an abnormality occurs. For example, if there is an abnormal entry in the error log, it identifies the cause. This makes it possible to quickly respond if an abnormality occurs by identifying the cause.

[0074] The evaluation unit can use the emotion estimation function to analyze the emotions the user feels when they receive the analysis results and provide additional information to help them understand the results. For example, the evaluation unit uses the emotion estimation function to analyze the emotions the user feels when they receive the analysis results and provide additional information to help them understand the results. For example, if the user is feeling anxious, the evaluation unit displays a detailed explanation. The evaluation unit also uses the emotion estimation function to analyze the emotions the user feels when they receive the analysis results in real time and provides additional information to help them understand the results. For example, if the user has questions, the evaluation unit displays an FAQ. The evaluation unit also uses the emotion estimation function to analyze the emotions the user feels when they receive the analysis results and provides additional information to help them understand the results. For example, if the user is confused, the evaluation unit displays visualized data. This makes it easier for the user to understand the analysis results.

[0075] The evaluation unit can display the results using different visualization techniques to enable users to intuitively understand the data. For example, the generation AI performs data analysis in the evaluation unit, and then displays the analysis results using different visualization techniques, such as 3D graphs and heat maps. For example, the evaluation unit displays base station traffic data in a 3D graph to enable users to intuitively understand the data. The evaluation unit also displays the analysis results in a heat map to enable users to intuitively understand the data distribution and fluctuations. For example, the frequency of base station error logs is displayed in a heat map. The evaluation unit also performs data analysis in the generation AI, and then displays the analysis results using different visualization techniques. For example, the operation time of a base station is displayed in a 3D graph to enable users to intuitively understand the data. This allows users to intuitively understand the analysis results.

[0076] The evaluation unit can integrate the data with other related data to perform a comprehensive evaluation. For example, when the generation AI performs data analysis, the evaluation unit integrates the data with other related data (e.g., weather data and geographic information) to perform a more comprehensive evaluation. For example, the evaluation unit integrates base station traffic data and weather data for analysis. The evaluation unit also integrates the data with other related data (e.g., geographic information) to perform a more comprehensive evaluation of the performance and quality of the base station. For example, the evaluation unit integrates base station location information and traffic data for analysis. The evaluation unit also integrates the data with other related data (e.g., weather data) when the generation AI performs data analysis to perform a more comprehensive evaluation. For example, the evaluation unit integrates base station operating hours and weather data for analysis. In this way, by integrating related data, a more comprehensive evaluation is possible.

[0077] The evaluation unit can use the emotion estimation function to analyze how the user feels about the analysis results and provide feedback to elicit positive emotions. For example, the evaluation unit uses the generation AI to perform data analysis, and the emotion estimation function to analyze how the user feels about the analysis results and provide feedback to elicit positive emotions. For example, if the user feels anxious, the evaluation unit displays an encouraging message. The evaluation unit also uses the emotion estimation function to analyze in real time how the user feels about the analysis results and provide feedback to elicit positive emotions. For example, if the user feels confused, the evaluation unit displays a detailed explanation. The evaluation unit also uses the emotion estimation function to analyze how the user feels about the analysis results and provide feedback to elicit positive emotions. For example, if the user feels confused, the evaluation unit displays visualized data. This provides feedback to elicit positive emotions from the user.

[0078] The feedback providing unit can add a user-customizable filtering function to display only the necessary information. For example, the feedback providing unit adds a user-customizable filtering function to the indices provided by the generation AI to display only the necessary information. For example, the user selects and displays specific indices (such as availability rate or error rate). The feedback providing unit also uses the filtering function to enable the user to display only the information they need. For example, data from a specific period or data from a specific base station is filtered and displayed. The feedback providing unit also adds a user-customizable filtering function to the indices provided by the generation AI to display only the necessary information. For example, the user selects and displays indices that interest them. This enables the user to display only the information they need.

[0079] The feedback providing unit allows the generation AI to personalize the feedback based on the user's past behavioral history and make more specific suggestions. For example, the generation AI personalizes the feedback based on the user's past behavioral history and makes more specific suggestions. For example, the generation AI makes optimal suggestions to the user based on past data input history and analysis results. The feedback providing unit also analyzes the user's past behavioral history and the generation AI makes personalized suggestions. For example, the generation AI suggests the next maintenance date based on past maintenance history. The feedback providing unit also personalizes the feedback based on the user's past behavioral history and makes more specific suggestions. For example, the generation AI suggests preventive measures for errors based on past error logs. This allows the generation AI to make more specific suggestions to the user.

[0080] The feedback providing unit can use the emotion estimation function to analyze the emotion of the user when receiving feedback and adjust the content of the feedback. For example, the feedback providing unit uses the emotion estimation function to analyze the user's emotion when receiving feedback provided by the generation AI and adjust the content of the feedback. For example, if the user is feeling anxious, a detailed explanation is added. The feedback providing unit also uses the emotion estimation function to analyze the emotion of the user when receiving feedback in real time and adjust the content of the feedback. For example, if the user is feeling confused, an FAQ is displayed. The feedback providing unit also uses the emotion estimation function to analyze the emotion of the user when receiving feedback and adjust the content of the feedback. For example, if the user is confused, visualized data is displayed. In this way, the content of the feedback is adjusted according to the user's emotion.

[0081] The feedback providing unit can add a function that allows sharing with other users and promote community-based feedback. The feedback providing unit, for example, adds a function that allows indicators provided by the generation AI to be shared with other users and promotes community-based feedback. For example, data is shared with other users who operate the same base station and opinions are exchanged. The feedback providing unit also promotes community-based feedback using the function that allows sharing with other users. For example, a user makes their own data public and receives advice from other users. The feedback providing unit also adds a function that allows indicators provided by the generation AI to be shared with other users and promotes community-based feedback. For example, a user shares their own data and receives feedback from other users. This promotes community-based feedback.

[0082] The feedback providing unit can provide feedback in different formats to make it easier for the user to understand. For example, the feedback providing unit provides the feedback provided by the generation AI in different formats (for example, video explanations or infographics) to make it easier for the user to understand. For example, base station performance data is explained using video explanations. The feedback providing unit also provides feedback in different formats to make it easier for the user to understand. For example, base station operation data is displayed using infographics. The feedback providing unit also provides the feedback provided by the generation AI in different formats to make it easier for the user to understand. For example, base station error logs are explained using video explanations. In this way, feedback is provided in a format that is easier for the user to understand.

[0083] The feedback providing unit can use the emotion estimation function to monitor the user's emotions in real time when receiving feedback, and provide feedback at optimal timing. For example, the feedback providing unit uses the emotion estimation function to monitor the user's emotions in real time when receiving feedback provided by the generation AI, and provide feedback at optimal timing. For example, the feedback providing unit displays feedback when the user is relaxed. The feedback providing unit also uses the emotion estimation function to monitor the user's emotions in real time when receiving feedback, and provides feedback at optimal timing. For example, the feedback providing unit displays feedback when the user is concentrating. The feedback providing unit also uses the emotion estimation function to monitor the user's emotions in real time when receiving feedback, and provides feedback at optimal timing. For example, the feedback is displayed when the user is feeling positive. In this way, feedback is provided at optimal timing according to the user's emotions.

[0084] The feedback providing unit strengthens the anomaly detection algorithm and can immediately issue an alert if an anomaly occurs. For example, the generation AI continuously monitors the performance and quality of the base station and strengthens the anomaly detection algorithm. For example, an alert is issued immediately if there is an abnormal peak in the traffic data. The feedback providing unit also strengthens the anomaly detection algorithm and immediately issues an alert if an abnormality occurs in the performance or quality of the base station. For example, an alert is issued if the uptime is abnormally short. The feedback providing unit also strengthens the anomaly detection algorithm and strengthens the anomaly detection algorithm. For example, an alert is issued immediately if there is an abnormal entry in the error log. This allows an alert to be issued immediately if an abnormality occurs.

[0085] The feedback providing unit automatically backs up monitoring data to prevent data loss. For example, the generation AI continuously monitors the performance and quality of the base station and automatically backs up the monitoring data. For example, traffic data and uptime data are backed up regularly. The feedback providing unit also automatically backs up monitoring data to prevent data loss. For example, error log and maintenance history data are backed up regularly. The feedback providing unit also has the generation AI continuously monitor the performance and quality of the base station and automatically backs up monitoring data. For example, base station operation data is backed up regularly. This prevents data loss.

[0086] The feedback providing unit can use the emotion estimation function to analyze the emotions of the user when they receive the monitoring results and provide support as needed. For example, the feedback providing unit continuously monitors the performance and quality of the base station using the generation AI, analyzes the emotions of the user when they receive the monitoring results using the emotion estimation function, and provides support as needed. For example, if the user is feeling anxious, the feedback providing unit displays a detailed explanation. The feedback providing unit also uses the emotion estimation function to analyze the emotions of the user when they receive the monitoring results in real time and provides support as needed. For example, if the user has questions, the feedback providing unit displays an FAQ. The feedback providing unit also uses the emotion estimation function to analyze the emotions of the user when they receive the monitoring results and provides support as needed. For example, if the user is confused, the feedback providing unit displays visualized data. This allows support to be provided according to the user's emotions.

[0087] The feedback providing unit can add a function to send notifications to a smartwatch or smartphone, allowing the user to check the results anywhere. For example, the generation AI continuously monitors the performance and quality of a base station and adds a function to send notifications to a smartwatch or smartphone. For example, it notifies a smartphone of base station traffic data and operating time data. The feedback providing unit also adds a function to send notifications to a smartwatch or smartphone, allowing the user to check the results anywhere. For example, it notifies a smartwatch of base station error log and maintenance history data. The feedback providing unit also adds a function to send notifications to a smartwatch or smartphone, allowing the generation AI continuously monitors the performance and quality of a base station and adds a function to send notifications to a smartwatch or smartphone. For example, it notifies a smartphone of base station operation data. This allows the user to check the results anywhere.

[0088] The feedback providing unit can work in conjunction with other systems to enable integrated management. For example, the generation AI in the feedback providing unit continuously monitors the performance and quality of base stations and works in conjunction with other systems (e.g., a company's operations management system). For example, it integrates base station traffic data and uptime data into the company's operations management system. The feedback providing unit also works in conjunction with other systems to enable integrated management. For example, it integrates base station error log and maintenance history data into the company's operations management system. The feedback providing unit also works in conjunction with other systems to enable integrated management. For example, it integrates base station operation data into the company's operations management system. The generation AI in the feedback providing unit continuously monitors the performance and quality of base stations and works in conjunction with other systems. For example, it integrates base station operations data into the company's operations management system. This enables it to work in conjunction with other systems to enable integrated management.

[0089] The feedback providing unit can use the emotion estimation function to analyze how the user feels about the monitoring results and make suggestions to elicit positive emotions. For example, the feedback providing unit continuously monitors the performance and quality of the base station using the generation AI, analyzes how the user feels about the monitoring results using the emotion estimation function, and makes suggestions to elicit positive emotions. For example, if the user is feeling anxious, the feedback providing unit displays an encouraging message. The feedback providing unit also uses the emotion estimation function to analyze in real time how the user feels about the monitoring results and makes suggestions to elicit positive emotions. For example, if the user has doubts, the feedback providing unit displays a detailed explanation. The feedback providing unit also uses the emotion estimation function to analyze how the user feels about the monitoring results and makes suggestions to elicit positive emotions. For example, if the user is confused, the feedback providing unit displays visualized data. This makes suggestions to elicit positive emotions from the user.

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

[0091] The base station evaluation system can further include an anomaly detection unit. The anomaly detection unit can monitor the base station's operational data and performance data in real time and detect abnormal patterns. For example, it can issue a warning if an abnormal peak occurs in traffic data or if the uptime is abnormally short. The anomaly detection unit can also analyze error logs and maintenance history to identify the cause of the abnormality. For example, if a specific part is frequently failing, it can suggest replacing that part. This can improve the operational stability of the base station.

[0092] The base station evaluation system can further include a prediction unit. The prediction unit can predict future base station performance based on past data. For example, it can analyze past traffic data and predict future traffic increases. The prediction unit can also predict the next maintenance period based on maintenance history. For example, it can predict the lifespan of a specific part and suggest when it should be replaced. This allows for efficient operation planning of base stations.

[0093] The base station evaluation system can further include an energy management unit. The energy management unit can monitor the energy consumption of the base station and propose efficient energy usage. For example, it can identify peak energy consumption times based on the base station's operating hours and traffic data and propose optimization of energy usage. The energy management unit can also make proposals to promote the use of renewable energy. For example, it can propose the introduction of a solar power generation system. This can improve the energy efficiency of the base station.

[0094] The base station evaluation system can further include a security management unit. The security management unit can monitor the security status of the base station and detect potential threats. For example, it can detect unauthorized access attempts or abnormal data traffic and issue an alert. The security management unit can also monitor compliance with security policies and suggest necessary measures. For example, it can suggest the regular application of security patches. This can strengthen the security of the base station.

[0095] The base station evaluation system may further include a user feedback unit. The user feedback unit may collect feedback from users of the base station and reflect it in the evaluation. For example, users may provide opinions about the coverage and connection quality of the base station. The user feedback unit may also analyze the collected feedback and identify areas for improvement in the base station. For example, the user feedback unit may identify connection problems in a specific area and propose solutions to improve the base station. This may improve user satisfaction.

[0096] The base station evaluation system can also use its emotion estimation function to detect the user's stress level and make suggestions to simplify the input process. For example, if the user is feeling stressed, it can make suggestions to reduce the number of input items. It can also use the emotion estimation function to monitor the user's stress level in real time while inputting data, and if stress is high, it can display guidance to simplify the input process. For example, it can provide hints to simplify the input procedure. This reduces the user's stress and makes input work more efficient.

[0097] The base station evaluation system can further use an emotion estimation function to analyze the user's emotions and provide positive feedback. For example, if the user shows positive emotions in response to their input, an encouraging message can be displayed. The emotion estimation function can also be used to analyze the emotions of the user when they are inputting base station specifications in real time and provide positive feedback. For example, a message praising the user for accurate input can be displayed. This positively encourages the user's input work.

[0098] The base station evaluation system can also use an emotion estimation function to detect in real time any anxiety or doubts the user may have while entering data and provide appropriate support. For example, if the user is feeling anxious, an input guide can be displayed. The emotion estimation function can also be used to detect in real time any anxiety or doubt the user may have while entering base station data and provide support. For example, if there is a question about the input content, an FAQ can be displayed. This allows the system to detect in real time any anxiety or doubt the user may have while entering data and provide appropriate support.

[0099] The base station evaluation system can further use an emotion estimation function to analyze the emotions expressed by the user when receiving the analysis results and provide additional information to help the user understand the results. For example, if the user is feeling anxious, a detailed explanation can be displayed. The emotion estimation function can also be used to analyze the emotions expressed by the user when receiving the analysis results in real time and provide additional information to help the user understand the results. For example, if the user has questions, an FAQ can be displayed. This makes it easier for the user to understand the analysis results.

[0100] The base station evaluation system further uses an emotion estimation function to monitor in real time the emotions of the user when receiving feedback and provide feedback at the optimal timing. For example, feedback may be displayed when the user is relaxed. The base station evaluation system also uses an emotion estimation function to monitor in real time the emotions of the user when receiving feedback and provide feedback at the optimal timing. For example, feedback may be displayed when the user is concentrating. In this way, feedback may be provided at the optimal timing according to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The input unit inputs the type and specifications of the base station. For example, the model name, manufacturer, and technical specifications (frequency band, output, coverage range, etc.) of the base station are input. The input unit also inputs operational and performance data of the base station. For example, data such as the base station's operating time, traffic data, error log, and maintenance history is provided. Step 2: The data analysis unit evaluates the performance and quality of the base station based on the type and specifications of the base station entered by the input unit. For example, the generation AI analyzes the base station's operating time and traffic data to evaluate the base station's stability and efficiency. The generation AI also analyzes error logs and maintenance history to evaluate the base station's reliability and the need for maintenance. Step 3: The evaluation unit generates objective indicators based on the performance and quality of the base station evaluated by the data analysis unit. For example, it provides indicators such as the base station's availability rate, traffic processing capacity, error rate, and maintenance frequency. Step 4: The feedback providing unit provides feedback based on the indicators generated by the evaluation unit, for example, providing specific suggestions or advice for improving the performance or quality of the base station.

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

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting the type and specifications of the base station; a data analysis unit that evaluates the performance and quality of the base station based on the type and specifications of the base station input by the input unit; an evaluation unit that generates an objective index based on the performance and quality of the base station evaluated by the data analysis unit; a feedback providing unit that provides feedback based on the index generated by the evaluation unit. A system characterized by:

2. The input unit The generative AI automatically suggests the optimal input format, preventing user input errors.

2. The system of claim 1.

3. The input unit Automatically extract performance data of similar base stations from a historical database and provide it to users as reference information 2. The system of claim 1.

4. The input unit Detects the user's stress level when typing and provides suggestions to simplify the input process 2. The system of claim 1.

5. The input unit Enables voice input using voice recognition technology 2. The system of claim 1.

6. The input unit Providing an interactive dashboard for comparing the specifications of different base stations, allowing users to make visual comparisons 2. The system of claim 1.

7. The input unit Analyzes user emotions while typing and provides positive feedback to encourage typing 2. The system of claim 1.

8. The data analysis unit Automatically detects data anomalies and prompts users to correct them 2. The system of claim 1.

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