System

Abstract

An object of a system according to an embodiment is to automatically detect a failure or a fault of a base station and generate an appropriate countermeasure or report.SOLUTION: A system includes a state collection part, an analysis part, a countermeasure generation part, and a report generation part. The state collecting unit collects a state and a log of the base station. The analysis unit analyzes the state of the base station and the log collected by the state collection unit. The countermeasure generation unit generates an optimum countermeasure based on the result analyzed by the analysis unit. The report generation part generates a report based on the countermeasure generated by the countermeasure generation part.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] With conventional technology, the process of detecting base station failures and faults and generating appropriate countermeasures and reports was done manually, which was a time-consuming and labor-intensive process.

[0005] The system according to the embodiment aims to automatically detect failures and faults in base stations and generate appropriate countermeasures and reports. [Means for solving the problem]

[0006] The system according to the embodiment includes a status collection unit, an analysis unit, a countermeasure generation unit, and a report generation unit. The status collection unit collects the status and logs of the base stations. The analysis unit analyzes the status and logs of the base stations collected by the status collection unit. The countermeasure generation unit generates an optimal countermeasure based on the results of the analysis by the analysis unit. The report generation unit generates a report based on the countermeasure generated by the countermeasure generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically detect failures or faults in base stations and generate appropriate countermeasures and reports. [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) The base station management system according to an embodiment of the present invention is a system that collects the status and logs of base stations, automatically detects faults and failures using a generation AI, and generates appropriate countermeasures and reports. As a result, the base station management system can reduce the human burden and time required for the operation and management of base stations.

[0029] A base station management system according to an embodiment includes a status collection unit, an analysis unit, a countermeasure generation unit, and a report generation unit. The status collection unit collects the status and logs of base stations. For example, it collects base station operation status, error logs, communication quality data, etc. The status collection unit can also collect base station status and logs in real time. For example, it collects operation status and error logs in real time and provides them to the generation AI sequentially. The status collection unit can also collect surrounding environment data (weather information, earthquake information, etc.) and provide them to the generation AI. For example, it collects weather information and earthquake information and analyzes factors affecting the operation of the base station. The analysis unit analyzes the status and logs of the base station collected by the status collection unit. For example, the generation AI detects not only abnormal patterns learned from past data but also newly emerging unknown abnormal patterns. The analysis unit also analyzes abnormalities in the physical vibrations and sounds of the base station. For example, it detects abnormalities using a vibration sensor or microphone. The countermeasure generation unit generates optimal countermeasures based on the results of the analysis by the analysis unit. For example, it proposes specific countermeasures such as replacing specific parts, restarting the system, or changing settings. The countermeasure generation unit also considers the success rate and cost of past countermeasures to propose the most effective and economical countermeasure. Furthermore, the countermeasure generation unit also considers the surrounding environment of the base station and the characteristics of users to propose countermeasures specific to the region. The report generation unit generates a report based on the countermeasures generated by the countermeasure generation unit. For example, the report may include details of the failure or fault, the proposed countermeasure, and the results of the implemented countermeasure. The report generation unit also considers evaluation data from past reports to automatically select the format and content with the highest evaluation. Furthermore, the report generation unit also considers the operation history and trend data of the base station to generate a report that includes future predictions. This allows the base station management system according to the embodiment to reduce the human burden and time required for the operation and management of base stations. For example, by automating the process from detecting failures or faults to proposing countermeasures and generating reports, the burden on operators can be reduced. Furthermore, a quick response is possible, thereby improving the availability rate of base stations.

[0030] The status collection unit can collect surrounding environment data in addition to the base station status and logs and provide it to the generation AI. For example, the status collection unit collects meteorological information in real time in addition to the base station status and logs and provides it to the generation AI. For example, it collects data such as temperature, humidity, and wind speed and analyzes factors that affect the operation of the base station. The status collection unit also collects earthquake information and integrates it with the base station status and logs for analysis. For example, it analyzes the operation status of the base station when an earthquake occurs and evaluates the impact of the earthquake. The status collection unit also collects surrounding traffic information and integrates it with the base station status and logs for analysis. For example, it evaluates the impact that increases or decreases in traffic volume have on the communication quality of the base station. This enables more accurate analysis by taking surrounding environment data into account.

[0031] The status collection unit can collect the status and logs of base stations in real time and provide them to the generation AI sequentially. The status collection unit, for example, monitors the operating status of base stations in real time and provides the data sequentially to the generation AI. For example, it collects availability rates and error logs in real time and performs immediate analysis. The status collection unit also collects communication quality data in real time and provides it to the generation AI. For example, it sequentially collects data on communication speed and connection stability and generates immediate countermeasures. The status collection unit also collects power consumption data of base stations in real time and provides it to the generation AI. For example, it detects abnormalities in power consumption and proposes immediate countermeasures. This makes it possible to collect data in real time and take immediate action.

[0032] The status collection unit can also collect physical inspection data using drones or robots and provide it to the generation AI. For example, the status collection unit uses drones to perform physical inspections of base stations and provides the collected data to the generation AI. For example, it takes photos of the base station's exterior and the condition of the antenna and uses them for analysis. The status collection unit also uses robots to perform internal inspections of base stations and provides the collected data to the generation AI. For example, it checks the condition of wiring and equipment and detects abnormalities. The status collection unit also uses drones or robots to collect environmental data around the base station and provides it to the generation AI. For example, it uses data on surrounding obstacles and terrain for analysis. In this way, collecting physical inspection data enables more detailed analysis.

[0033] The status collection unit can integrate data from base stations from different manufacturers and provide it to the generation AI. The status collection unit, for example, builds a system that integrates data from base stations from different manufacturers and provides it to the generation AI. For example, it unifies and analyzes data in different formats. The status collection unit also develops a data conversion tool to integrate base station data from different manufacturers and provides it to the generation AI. For example, it automates the conversion and integration of data formats. The status collection unit also improves interoperability between different systems by integrating base station data from different manufacturers and providing it to the generation AI. For example, it centrally manages data from different systems. This can improve interoperability between different systems.

[0034] The analysis unit can detect not only abnormal patterns learned from past data, but also newly emerging unknown abnormal patterns. For example, the analysis unit not only teaches the generation AI past data, but also develops algorithms to detect newly emerging unknown abnormal patterns. For example, it adds a self-learning function for anomaly detection. In addition, to detect unknown abnormal patterns, the analysis unit provides data to the generation AI in real time and builds a system that performs sequential learning. For example, it learns immediately when a new abnormality occurs. In addition, the analysis unit integrates various data sources for anomaly detection so that the generation AI can detect unknown abnormal patterns. For example, it uses data from different sensors and devices for analysis. This enables the generation AI to detect unknown abnormal patterns, enabling faster responses.

[0035] The analysis unit can also analyze abnormalities in the physical vibrations and sounds of the base station. For example, the analysis unit collects physical vibration data from the base station and provides it to the generation AI. For example, a vibration sensor is installed to detect abnormal vibration patterns. The analysis unit also collects sound data from the base station and provides it to the generation AI. For example, a microphone is installed to detect abnormal sounds. The analysis unit also integrates the vibration and sound data and provides it to the generation AI to build a system that detects physical abnormalities. For example, it analyzes abnormalities in vibration and sound simultaneously. This allows physical abnormalities to be detected, enabling a more comprehensive analysis.

[0036] The analysis unit can also analyze data from other infrastructures and take mutual influences into account. For example, the analysis unit provides power grid data to the generation AI and uses it to detect base station failures and faults. For example, it analyzes the impact of abnormalities in the power supply on base stations. The analysis unit also provides transportation system data to the generation AI and uses it to detect base station failures and faults. For example, it analyzes the impact of increases or decreases in traffic volume on the communication quality of base stations. The analysis unit also integrates other infrastructure data and provides it to the generation AI to build a system that detects failures and faults while taking mutual influences into account. For example, it analyzes power grid and transportation system data simultaneously. This enables a more comprehensive analysis by taking into account the mutual influences with other infrastructures.

[0037] The analysis unit also analyzes feedback data from users of base stations and can detect abnormalities based on user reports. For example, the analysis unit builds a system that collects feedback data from users of base stations and provides it to the generation AI. For example, it detects abnormalities based on user reports. The analysis unit also analyzes user feedback data and develops an algorithm for the generation AI to detect abnormalities. For example, it analyzes the content of user reports and identifies abnormal patterns. The analysis unit also builds a system that collects user feedback data in real time and provides it to the generation AI to immediately detect abnormalities. For example, it detects an abnormality when there are many user reports. This makes it possible to detect abnormalities based on user feedback and respond more quickly.

[0038] The countermeasure generation unit can propose the most effective and economical countermeasure, taking into account the success rate and cost of past countermeasures. For example, the countermeasure generation unit provides the generation AI with success rate data of past countermeasures and builds a system that proposes optimal countermeasures. For example, it prioritizes proposing countermeasures with a high success rate. The countermeasure generation unit also provides the generation AI with cost data of countermeasures and develops an algorithm that proposes the most economical countermeasure. For example, it prioritizes proposing countermeasures with a low cost. The countermeasure generation unit also builds a system in which the generation AI proposes countermeasures that take both success rate and cost into consideration. For example, it proposes a countermeasure with a high success rate and a low cost as the optimal one. This makes it possible to reduce operating costs by proposing effective and economical countermeasures.

[0039] The countermeasure generation unit can propose region-specific countermeasures by taking into account the surrounding environment of the base station and the characteristics of the user. For example, the countermeasure generation unit provides the generation AI with data on the surrounding environment of the base station and builds a system that proposes region-specific countermeasures. For example, it proposes countermeasures that take into account weather conditions and topography. The countermeasure generation unit also provides the generation AI with data on the user's characteristics and develops an algorithm that proposes region-specific countermeasures. For example, it proposes countermeasures that take into account the user's age group and usage patterns. The countermeasure generation unit also builds a system in which the generation AI proposes countermeasures that take into account both the surrounding environment and user characteristics. For example, it proposes the optimal countermeasure that combines weather conditions and user characteristics. This enables more appropriate responses by proposing region-specific countermeasures.

[0040] The countermeasure generation unit can also take into account data on other infrastructure and propose countermeasures that minimize mutual impact. For example, the countermeasure generation unit provides power grid data to the generation AI and builds a system that proposes countermeasures that minimize mutual impact. For example, it proposes countermeasures that take into account the stability of the power supply. The countermeasure generation unit also provides transportation system data to the generation AI and develops an algorithm that proposes countermeasures that minimize mutual impact. For example, it proposes countermeasures that take fluctuations in traffic volume into account. The countermeasure generation unit also integrates other infrastructure data and provides it to the generation AI to build a system that proposes countermeasures that minimize mutual impact. For example, it analyzes data on the power grid and transportation system simultaneously. This enables more appropriate responses by minimizing mutual impact with other infrastructure.

[0041] The countermeasure generation unit can also take feedback data from users of base stations into consideration and propose countermeasures that improve user satisfaction. The countermeasure generation unit, for example, builds a system that collects feedback data from users of base stations and provides it to the generation AI. For example, it proposes countermeasures based on user opinions. The countermeasure generation unit also analyzes user feedback data and develops an algorithm that allows the generation AI to propose countermeasures that improve user satisfaction. For example, it proposes countermeasures that reflect user requests. The countermeasure generation unit also builds a system that proposes countermeasures that improve user satisfaction by collecting user feedback data in real time and providing it to the generation AI. For example, it reflects user opinions immediately. This makes it possible to provide better services by proposing countermeasures that improve user satisfaction.

[0042] The report generation unit can automatically select the most highly rated format and content by taking into account evaluation data of past reports. For example, the report generation unit provides evaluation data of past reports to the generation AI and builds a system that automatically selects the most highly rated format and content. For example, it generates a new report based on the format of the highly rated report. The report generation unit also analyzes the report evaluation data and develops an algorithm that allows the generation AI to automatically select the most highly rated content. For example, it generates a report based on the highly rated content. The report generation unit also collects evaluation data of past reports in real time and provides it to the generation AI to build a system that automatically selects the most highly rated format and content. For example, it immediately reflects the format of the highly rated report. This makes it possible to generate more effective reports by selecting the most rated format and content.

[0043] The report generation unit can also take into account data from other infrastructures to generate comprehensive reports. For example, the report generation unit provides power grid data to the generation AI to build a system that generates comprehensive reports. For example, it generates a report that takes into account the stability of the power supply. The report generation unit also provides transportation system data to the generation AI to develop an algorithm that generates comprehensive reports. For example, it generates a report that takes into account fluctuations in traffic volume. The report generation unit also builds a system that generates comprehensive reports by integrating other infrastructure data and providing it to the generation AI. For example, it analyzes power grid and transportation system data simultaneously. This makes it possible to generate more comprehensive reports by taking into account data from other infrastructures.

[0044] The report generation unit can also take feedback data from base station users into consideration and generate reports that reflect the user's perspective. The report generation unit, for example, builds a system that collects feedback data from base station users and provides it to the generation AI. For example, it generates reports based on the user's opinions. The report generation unit also analyzes the user's feedback data and develops an algorithm that enables the generation AI to generate reports that reflect the user's perspective. For example, it generates reports that reflect the user's requests. The report generation unit also builds a system that generates reports that reflect the user's perspective by collecting user feedback data in real time and providing it to the generation AI. For example, it reflects user opinions immediately. This allows reports that reflect the user's perspective to be generated, making it possible to provide reports that are more user-friendly.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The base station management system can further include an energy efficiency optimization unit. The energy efficiency optimization unit collects power consumption data from base stations and provides it to the generation AI. For example, it analyzes power consumption patterns and proposes an optimal operation schedule to reduce power consumption during peak hours. The energy efficiency optimization unit can also propose operation methods to maximize the use of renewable energy. For example, it analyzes the usage status of solar power generation and wind power generation and adjusts base station operation at the optimal timing. Furthermore, the energy efficiency optimization unit can consider the stability of the power supply and propose a plan to use backup power sources in emergencies. This can improve the energy efficiency of base stations and reduce operating costs.

[0047] The base station management system can further include a security monitoring unit. The security monitoring unit monitors the physical security status of the base station and provides the generated AI with this information. For example, it can use surveillance cameras and sensors to detect suspicious activity and immediately issue an alert. The security monitoring unit can also monitor the network security of the base station. For example, it can detect signs of unauthorized access or cyberattacks and propose appropriate countermeasures. Furthermore, the security monitoring unit can regularly evaluate the security status of the base station and propose areas for improvement. This can strengthen the security of the base station and improve operational safety.

[0048] The base station management system can further be equipped with a preventive maintenance unit. The preventive maintenance unit collects lifespan data for each base station component and provides it to the generation AI. For example, it analyzes the usage and deterioration status of components and proposes the optimal replacement time. The preventive maintenance unit can also detect signs of impending failure based on the base station's operational data. For example, it can detect abnormal vibrations or temperature increases and take measures in advance. Furthermore, the preventive maintenance unit can analyze past maintenance history and propose optimal maintenance plans. This can prevent base station failures before they occur and improve operational stability.

[0049] The base station management system can further include an environmental impact assessment unit. The environmental impact assessment unit assesses the impact of base station operation on the surrounding environment and provides the results to the generation AI. For example, it analyzes the impact of electromagnetic waves from the base station and proposes measures to minimize the impact on surrounding residents. The environmental impact assessment unit can also assess the impact of noise and vibration caused by base station operation. For example, it collects noise level and vibration data and proposes appropriate soundproofing measures and vibration control measures. Furthermore, the environmental impact assessment unit can assess the impact of base station operation on the ecosystem. For example, it analyzes the impact on flora and fauna and proposes measures for environmental protection. This makes it possible to minimize the impact of base station operation on the environment.

[0050] The base station management system can further include a user feedback collection unit. The user feedback collection unit collects feedback from users of the base station and provides it to the generation AI. For example, user satisfaction and dissatisfaction can be collected through surveys or apps and used for analysis. The user feedback collection unit can also collect user feedback in real time and immediately propose countermeasures. For example, if there are many complaints about communication quality, improvement measures can be taken immediately. Furthermore, the user feedback collection unit can also propose improvements to the base station operation based on user feedback. This can increase user satisfaction and provide better services.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The status collection unit collects the status and logs of the base station. For example, it collects the base station's operating status, error logs, communication quality data, etc. The status collection unit can also collect the status and logs of the base station in real time. It also collects surrounding environment data (weather information, earthquake information, etc.) and provides it to the generation AI. Step 2: The analysis unit analyzes the base station status and logs collected by the status collection unit. For example, the generation AI not only detects abnormal patterns learned from past data, but also detects new, unknown abnormal patterns that emerge. It also analyzes abnormalities in the physical vibrations and sounds of the base station. Step 3: The countermeasure generation unit generates optimal countermeasures based on the results of the analysis by the analysis unit. For example, it proposes specific countermeasures such as replacing specific parts, restarting the system, or changing settings. It also considers the success rate and cost of past countermeasures to propose the most effective and economical countermeasure. It also considers the surrounding environment of the base station and the characteristics of users to propose countermeasures specific to the region. Step 4: The report generator generates a report based on the countermeasures generated by the countermeasure generator. For example, the report may include details of the failure or fault, the proposed countermeasures, and the results of the implemented countermeasures. It also considers evaluation data from past reports and automatically selects the most highly rated format and content. It also considers the base station's operational history and trend data to generate a report that includes future predictions.

[0053] (Example 2) The base station management system according to an embodiment of the present invention is a system that collects the status and logs of base stations, automatically detects faults and failures using a generation AI, and generates appropriate countermeasures and reports. As a result, the base station management system can reduce the human burden and time required for the operation and management of base stations.

[0054] A base station management system according to an embodiment includes a status collection unit, an analysis unit, a countermeasure generation unit, and a report generation unit. The status collection unit collects the status and logs of base stations. For example, it collects base station operation status, error logs, communication quality data, etc. The status collection unit can also collect base station status and logs in real time. For example, it collects operation status and error logs in real time and provides them to the generation AI sequentially. The status collection unit can also collect surrounding environment data (weather information, earthquake information, etc.) and provide them to the generation AI. For example, it collects weather information and earthquake information and analyzes factors affecting the operation of the base station. The analysis unit analyzes the status and logs of the base station collected by the status collection unit. For example, the generation AI detects not only abnormal patterns learned from past data but also newly emerging unknown abnormal patterns. The analysis unit also analyzes abnormalities in the physical vibrations and sounds of the base station. For example, it detects abnormalities using a vibration sensor or microphone. The countermeasure generation unit generates optimal countermeasures based on the results of the analysis by the analysis unit. For example, it proposes specific countermeasures such as replacing specific parts, restarting the system, or changing settings. The countermeasure generation unit also considers the success rate and cost of past countermeasures to propose the most effective and economical countermeasure. Furthermore, the countermeasure generation unit also considers the surrounding environment of the base station and the characteristics of users to propose countermeasures specific to the region. The report generation unit generates a report based on the countermeasures generated by the countermeasure generation unit. For example, the report may include details of the failure or fault, the proposed countermeasure, and the results of the implemented countermeasure. The report generation unit also considers evaluation data from past reports to automatically select the format and content with the highest evaluation. Furthermore, the report generation unit also considers the operation history and trend data of the base station to generate a report that includes future predictions. This allows the base station management system according to the embodiment to reduce the human burden and time required for the operation and management of base stations. For example, by automating the process from detecting failures or faults to proposing countermeasures and generating reports, the burden on operators can be reduced. Furthermore, a quick response is possible, thereby improving the availability rate of base stations.

[0055] The status collection unit can collect surrounding environment data in addition to the base station status and logs and provide it to the generation AI. For example, the status collection unit collects meteorological information in real time in addition to the base station status and logs and provides it to the generation AI. For example, it collects data such as temperature, humidity, and wind speed and analyzes factors that affect the operation of the base station. The status collection unit also collects earthquake information and integrates it with the base station status and logs for analysis. For example, it analyzes the operation status of the base station when an earthquake occurs and evaluates the impact of the earthquake. The status collection unit also collects surrounding traffic information and integrates it with the base station status and logs for analysis. For example, it evaluates the impact that increases or decreases in traffic volume have on the communication quality of the base station. This enables more accurate analysis by taking surrounding environment data into account.

[0056] The status collection unit can collect the status and logs of base stations in real time and provide them to the generation AI sequentially. The status collection unit, for example, monitors the operating status of base stations in real time and provides the data sequentially to the generation AI. For example, it collects availability rates and error logs in real time and performs immediate analysis. The status collection unit also collects communication quality data in real time and provides it to the generation AI. For example, it sequentially collects data on communication speed and connection stability and generates immediate countermeasures. The status collection unit also collects power consumption data of base stations in real time and provides it to the generation AI. For example, it detects abnormalities in power consumption and proposes immediate countermeasures. This makes it possible to collect data in real time and take immediate action.

[0057] The state collection unit can use the emotion estimation function to monitor the stress level of the operations personnel and automatically provide support when stress increases. For example, the state collection unit uses the emotion estimation function to analyze facial expressions and voice to monitor the stress level of the operations personnel. For example, it automatically suggests taking a break when stress increases. The state collection unit also uses the emotion estimation function to monitor the stress level of the operations personnel in real time and automatically sends a support message when stress increases. The state collection unit also monitors the stress level of the operations personnel and builds a system that automatically distributes tasks when stress increases. For example, it assigns tasks to another person. This reduces the stress of the operations personnel and enables efficient operations.

[0058] The status collection unit can also collect physical inspection data using drones or robots and provide it to the generation AI. For example, the status collection unit uses drones to perform physical inspections of base stations and provides the collected data to the generation AI. For example, it takes photos of the base station's exterior and the condition of the antenna and uses them for analysis. The status collection unit also uses robots to perform internal inspections of base stations and provides the collected data to the generation AI. For example, it checks the condition of wiring and equipment and detects abnormalities. The status collection unit also uses drones or robots to collect environmental data around the base station and provides it to the generation AI. For example, it uses data on surrounding obstacles and terrain for analysis. In this way, collecting physical inspection data enables more detailed analysis.

[0059] The status collection unit can integrate data from base stations from different manufacturers and provide it to the generation AI. The status collection unit, for example, builds a system that integrates data from base stations from different manufacturers and provides it to the generation AI. For example, it unifies and analyzes data in different formats. The status collection unit also develops a data conversion tool to integrate base station data from different manufacturers and provides it to the generation AI. For example, it automates the conversion and integration of data formats. The status collection unit also improves interoperability between different systems by integrating base station data from different manufacturers and providing it to the generation AI. For example, it centrally manages data from different systems. This can improve interoperability between different systems.

[0060] The status collection unit can use the emotion estimation function to collect base station user satisfaction in real time and provide it to the generation AI. The status collection unit, for example, uses the emotion estimation function to build a system that collects base station user satisfaction in real time. For example, it analyzes the user's facial expressions and voice and evaluates satisfaction. The status collection unit also provides user satisfaction data to the generation AI and performs analysis for operational improvement. For example, it identifies the cause of low satisfaction and proposes improvement measures. The status collection unit also uses the emotion estimation function to monitor user satisfaction in real time and provides it to the generation AI, thereby improving operations based on the user's emotions. For example, it automatically proposes countermeasures when satisfaction is low. This makes it possible to improve operations based on the user's emotions.

[0061] The analysis unit can detect not only abnormal patterns learned from past data, but also newly emerging unknown abnormal patterns. For example, the analysis unit not only teaches the generation AI past data, but also develops algorithms to detect newly emerging unknown abnormal patterns. For example, it adds a self-learning function for anomaly detection. In addition, to detect unknown abnormal patterns, the analysis unit provides data to the generation AI in real time and builds a system that performs sequential learning. For example, it learns immediately when a new abnormality occurs. In addition, the analysis unit integrates various data sources for anomaly detection so that the generation AI can detect unknown abnormal patterns. For example, it uses data from different sensors and devices for analysis. This enables the generation AI to detect unknown abnormal patterns, enabling faster responses.

[0062] The analysis unit can also analyze abnormalities in the physical vibrations and sounds of the base station. For example, the analysis unit collects physical vibration data from the base station and provides it to the generation AI. For example, a vibration sensor is installed to detect abnormal vibration patterns. The analysis unit also collects sound data from the base station and provides it to the generation AI. For example, a microphone is installed to detect abnormal sounds. The analysis unit also integrates the vibration and sound data and provides it to the generation AI to build a system that detects physical abnormalities. For example, it analyzes abnormalities in vibration and sound simultaneously. This allows physical abnormalities to be detected, enabling a more comprehensive analysis.

[0063] The analysis unit can also analyze data from other infrastructures and take mutual influences into account. For example, the analysis unit provides power grid data to the generation AI and uses it to detect base station failures and faults. For example, it analyzes the impact of abnormalities in the power supply on base stations. The analysis unit also provides transportation system data to the generation AI and uses it to detect base station failures and faults. For example, it analyzes the impact of increases or decreases in traffic volume on the communication quality of base stations. The analysis unit also integrates other infrastructure data and provides it to the generation AI to build a system that detects failures and faults while taking mutual influences into account. For example, it analyzes power grid and transportation system data simultaneously. This enables a more comprehensive analysis by taking into account the mutual influences with other infrastructures.

[0064] The analysis unit also analyzes feedback data from users of base stations and can detect abnormalities based on user reports. For example, the analysis unit builds a system that collects feedback data from users of base stations and provides it to the generation AI. For example, it detects abnormalities based on user reports. The analysis unit also analyzes user feedback data and develops an algorithm for the generation AI to detect abnormalities. For example, it analyzes the content of user reports and identifies abnormal patterns. The analysis unit also builds a system that collects user feedback data in real time and provides it to the generation AI to immediately detect abnormalities. For example, it detects an abnormality when there are many user reports. This makes it possible to detect abnormalities based on user feedback and respond more quickly.

[0065] The analysis unit uses the emotion estimation function to detect the dissatisfaction and stress of base station users in real time and provides the data to the generation AI, thereby enabling fault detection based on the user's emotions. The analysis unit, for example, uses the emotion estimation function to build a system that detects the dissatisfaction and stress of base station users in real time. For example, it analyzes the user's facial expressions and voice to evaluate dissatisfaction and stress. The analysis unit also provides the user's dissatisfaction and stress data to the generation AI and performs analysis for fault detection. For example, it identifies the cause of high dissatisfaction or stress and detects abnormalities. The analysis unit also uses the emotion estimation function to monitor the user's dissatisfaction and stress in real time and provides the data to the generation AI, thereby enabling fault detection based on the user's emotions. For example, it automatically detects abnormalities when dissatisfaction or stress is high. This makes it possible to detect faults based on the user's emotions.

[0066] The countermeasure generation unit can propose the most effective and economical countermeasure, taking into account the success rate and cost of past countermeasures. For example, the countermeasure generation unit provides the generation AI with success rate data of past countermeasures and builds a system that proposes optimal countermeasures. For example, it prioritizes proposing countermeasures with a high success rate. The countermeasure generation unit also provides the generation AI with cost data of countermeasures and develops an algorithm that proposes the most economical countermeasure. For example, it prioritizes proposing countermeasures with a low cost. The countermeasure generation unit also builds a system in which the generation AI proposes countermeasures that take both success rate and cost into consideration. For example, it proposes a countermeasure with a high success rate and a low cost as the optimal one. This makes it possible to reduce operating costs by proposing effective and economical countermeasures.

[0067] The countermeasure generation unit can propose region-specific countermeasures by taking into account the surrounding environment of the base station and the characteristics of the user. For example, the countermeasure generation unit provides the generation AI with data on the surrounding environment of the base station and builds a system that proposes region-specific countermeasures. For example, it proposes countermeasures that take into account weather conditions and topography. The countermeasure generation unit also provides the generation AI with data on the user's characteristics and develops an algorithm that proposes region-specific countermeasures. For example, it proposes countermeasures that take into account the user's age group and usage patterns. The countermeasure generation unit also builds a system in which the generation AI proposes countermeasures that take into account both the surrounding environment and user characteristics. For example, it proposes the optimal countermeasure that combines weather conditions and user characteristics. This enables more appropriate responses by proposing region-specific countermeasures.

[0068] The countermeasure generation unit can use the emotion estimation function to consider the emotional state of the operations personnel and propose countermeasures that cause less stress. The countermeasure generation unit, for example, uses the emotion estimation function to build a system that analyzes the emotional state of the operations personnel in real time. For example, it analyzes facial expressions and voice to evaluate stress levels. The countermeasure generation unit also monitors the emotional state of the operations personnel and develops a system in which a generating AI proposes countermeasures that cause less stress. For example, it prioritizes the proposal of countermeasures that result in lower stress levels. The countermeasure generation unit also uses the emotion estimation function to develop an algorithm that analyzes the emotional state of the operations personnel and proposes countermeasures that cause less stress. For example, it automatically selects countermeasures that cause less stress. This reduces the stress of the operations personnel and enables efficient responses.

[0069] The countermeasure generation unit can also take into account data on other infrastructure and propose countermeasures that minimize mutual impact. For example, the countermeasure generation unit provides power grid data to the generation AI and builds a system that proposes countermeasures that minimize mutual impact. For example, it proposes countermeasures that take into account the stability of the power supply. The countermeasure generation unit also provides transportation system data to the generation AI and develops an algorithm that proposes countermeasures that minimize mutual impact. For example, it proposes countermeasures that take fluctuations in traffic volume into account. The countermeasure generation unit also integrates other infrastructure data and provides it to the generation AI to build a system that proposes countermeasures that minimize mutual impact. For example, it analyzes data on the power grid and transportation system simultaneously. This enables more appropriate responses by minimizing mutual impact with other infrastructure.

[0070] The countermeasure generation unit can also take feedback data from users of base stations into consideration and propose countermeasures that improve user satisfaction. The countermeasure generation unit, for example, builds a system that collects feedback data from users of base stations and provides it to the generation AI. For example, it proposes countermeasures based on user opinions. The countermeasure generation unit also analyzes user feedback data and develops an algorithm that allows the generation AI to propose countermeasures that improve user satisfaction. For example, it proposes countermeasures that reflect user requests. The countermeasure generation unit also builds a system that proposes countermeasures that improve user satisfaction by collecting user feedback data in real time and providing it to the generation AI. For example, it reflects user opinions immediately. This makes it possible to provide better services by proposing countermeasures that improve user satisfaction.

[0071] The countermeasure generation unit can use the emotion estimation function to consider the emotional state of the base station user and propose countermeasures that alleviate the user's dissatisfaction. The countermeasure generation unit, for example, uses the emotion estimation function to build a system that analyzes the emotional state of the base station user in real time. For example, it analyzes the user's facial expressions and voice and evaluates dissatisfaction. The countermeasure generation unit also monitors the user's emotional state and develops a system in which a generating AI proposes countermeasures that alleviate dissatisfaction. For example, it prioritizes proposing countermeasures that result in less dissatisfaction. The countermeasure generation unit also uses the emotion estimation function to develop an algorithm that analyzes the user's emotional state and proposes countermeasures that alleviate dissatisfaction. For example, it automatically selects countermeasures that result in less dissatisfaction. This allows for the provision of better services by proposing countermeasures that alleviate user dissatisfaction.

[0072] The report generation unit can automatically select the most highly rated format and content by taking into account evaluation data of past reports. For example, the report generation unit provides evaluation data of past reports to the generation AI and builds a system that automatically selects the most highly rated format and content. For example, it generates a new report based on the format of the highly rated report. The report generation unit also analyzes the report evaluation data and develops an algorithm that allows the generation AI to automatically select the most highly rated content. For example, it generates a report based on the highly rated content. The report generation unit also collects evaluation data of past reports in real time and provides it to the generation AI to build a system that automatically selects the most highly rated format and content. For example, it immediately reflects the format of the highly rated report. This makes it possible to generate more effective reports by selecting the most rated format and content.

[0073] The report generation unit can use the emotion estimation function to consider the emotional state of the report recipient and suggest an easy-to-understand format and content. For example, the report generation unit uses the emotion estimation function to build a system that analyzes the emotional state of the report recipient in real time. For example, it analyzes the recipient's facial expressions and voice and suggests an easy-to-understand format. The report generation unit also monitors the recipient's emotional state and develops a system in which AI suggests an easy-to-understand format and content. For example, it suggests a report format according to the emotional state. The report generation unit also uses the emotion estimation function to develop an algorithm that analyzes the emotional state of the report recipient and suggests an easy-to-understand format and content. For example, it automatically selects report content according to the emotional state. This makes it possible to generate reports that are easier to understand by taking the recipient's emotional state into consideration.

[0074] The report generation unit can also take into account data from other infrastructures to generate comprehensive reports. For example, the report generation unit provides power grid data to the generation AI to build a system that generates comprehensive reports. For example, it generates a report that takes into account the stability of the power supply. The report generation unit also provides transportation system data to the generation AI to develop an algorithm that generates comprehensive reports. For example, it generates a report that takes into account fluctuations in traffic volume. The report generation unit also builds a system that generates comprehensive reports by integrating other infrastructure data and providing it to the generation AI. For example, it analyzes power grid and transportation system data simultaneously. This makes it possible to generate more comprehensive reports by taking into account data from other infrastructures.

[0075] The report generation unit can also take feedback data from base station users into consideration and generate reports that reflect the user's perspective. The report generation unit, for example, builds a system that collects feedback data from base station users and provides it to the generation AI. For example, it generates reports based on the user's opinions. The report generation unit also analyzes the user's feedback data and develops an algorithm that enables the generation AI to generate reports that reflect the user's perspective. For example, it generates reports that reflect the user's requests. The report generation unit also builds a system that generates reports that reflect the user's perspective by collecting user feedback data in real time and providing it to the generation AI. For example, it reflects user opinions immediately. This allows reports that reflect the user's perspective to be generated, making it possible to provide reports that are more user-friendly.

[0076] The report generation unit can use the emotion estimation function to monitor the emotional state of the report recipient in real time and generate a report that corresponds to the recipient's emotions. For example, the report generation unit uses the emotion estimation function to build a system that monitors the emotional state of the report recipient in real time. For example, it analyzes the recipient's facial expressions and voice and generates a report that corresponds to their emotional state. The report generation unit also monitors the recipient's emotional state and develops a system in which AI suggests a report format that corresponds to their emotions. For example, it suggests a report format that corresponds to their emotional state. The report generation unit also uses the emotion estimation function to develop an algorithm that analyzes the emotional state of the report recipient and generates a report that corresponds to their emotions. For example, it automatically selects the content of the report that corresponds to their emotional state. This enables more effective communication by generating a report that corresponds to the recipient's emotions.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The base station management system can further include an energy efficiency optimization unit. The energy efficiency optimization unit collects power consumption data from base stations and provides it to the generation AI. For example, it analyzes power consumption patterns and proposes an optimal operation schedule to reduce power consumption during peak hours. The energy efficiency optimization unit can also propose operation methods to maximize the use of renewable energy. For example, it analyzes the usage status of solar power generation and wind power generation and adjusts base station operation at the optimal timing. Furthermore, the energy efficiency optimization unit can consider the stability of the power supply and propose a plan to use backup power sources in emergencies. This can improve the energy efficiency of base stations and reduce operating costs.

[0079] The base station management system can further include a security monitoring unit. The security monitoring unit monitors the physical security status of the base station and provides the generated AI with this information. For example, it can use surveillance cameras and sensors to detect suspicious activity and immediately issue an alert. The security monitoring unit can also monitor the network security of the base station. For example, it can detect signs of unauthorized access or cyberattacks and propose appropriate countermeasures. Furthermore, the security monitoring unit can regularly evaluate the security status of the base station and propose areas for improvement. This can strengthen the security of the base station and improve operational safety.

[0080] The base station management system can further be equipped with a preventive maintenance unit. The preventive maintenance unit collects lifespan data for each base station component and provides it to the generation AI. For example, it analyzes the usage and deterioration status of components and proposes the optimal replacement time. The preventive maintenance unit can also detect signs of impending failure based on the base station's operational data. For example, it can detect abnormal vibrations or temperature increases and take measures in advance. Furthermore, the preventive maintenance unit can analyze past maintenance history and propose optimal maintenance plans. This can prevent base station failures before they occur and improve operational stability.

[0081] The base station management system can further include an environmental impact assessment unit. The environmental impact assessment unit assesses the impact of base station operation on the surrounding environment and provides the results to the generation AI. For example, it analyzes the impact of electromagnetic waves from the base station and proposes measures to minimize the impact on surrounding residents. The environmental impact assessment unit can also assess the impact of noise and vibration caused by base station operation. For example, it collects noise level and vibration data and proposes appropriate soundproofing measures and vibration control measures. Furthermore, the environmental impact assessment unit can assess the impact of base station operation on the ecosystem. For example, it analyzes the impact on flora and fauna and proposes measures for environmental protection. This makes it possible to minimize the impact of base station operation on the environment.

[0082] The base station management system can further include a user feedback collection unit. The user feedback collection unit collects feedback from users of the base station and provides it to the generation AI. For example, user satisfaction and dissatisfaction can be collected through surveys or apps and used for analysis. The user feedback collection unit can also collect user feedback in real time and immediately propose countermeasures. For example, if there are many complaints about communication quality, improvement measures can be taken immediately. Furthermore, the user feedback collection unit can also propose improvements to the base station operation based on user feedback. This can increase user satisfaction and provide better services.

[0083] The base station management system can also use emotion estimation to improve the motivation of operators. For example, it can monitor the emotional state of operators and automatically send encouraging messages if their motivation is low. It can also use the emotion estimation function to evaluate the stress level of operators and suggest taking a break to refresh when stress levels rise. It can also use the emotion estimation function to assign tasks according to the emotional state of operators. For example, it can assign less burdensome tasks to operators who are highly stressed. This helps maintain the motivation of operators and enables efficient operations.

[0084] The base station management system can further use the emotion estimation function to provide services based on the user's emotions. For example, it can monitor the user's emotional state and automatically suggest improvements if satisfaction is low. The emotion estimation function can also be used to provide customized services according to the user's emotional state. For example, if the user is feeling stressed, it can provide relaxing content. Furthermore, the emotion estimation function can also be used to analyze the user's emotional state and collect data for future service improvements. This makes it possible to provide services based on the user's emotions and improve satisfaction.

[0085] The base station management system can further use the emotion estimation function to consider the emotional state of the report recipient and suggest an easy-to-understand format and content. For example, it can monitor the emotional state of the report recipient and generate a concise and easy-to-understand report if the recipient is under high stress. It can also use the emotion estimation function to suggest a report format according to the recipient's emotional state. For example, it can provide a detailed report if the recipient is relaxed. It can also use the emotion estimation function to analyze the recipient's emotional state and adjust the content of the report. This allows reports to be generated that take the recipient's emotional state into consideration, enabling more effective communication.

[0086] The base station management system can further use the emotion estimation function to propose countermeasures to reduce user dissatisfaction. For example, it can monitor the user's emotional state and automatically propose improvement measures if dissatisfaction is increasing. The emotion estimation function can also be used to provide customized countermeasures according to the user's emotional state. For example, if the user is feeling stressed, it can propose countermeasures to reduce stress. Furthermore, the emotion estimation function can be used to analyze the user's emotional state and collect data for future service improvements. This can reduce user dissatisfaction and increase satisfaction.

[0087] The base station management system can also use the emotion estimation function to suggest measures to reduce the stress of operators. For example, it can monitor the emotional state of operators and automatically suggest taking a break to refresh if stress levels are high. The emotion estimation function can also be used to assign tasks based on the emotional state of operators. For example, operators with high stress levels can be assigned less burdensome tasks. Furthermore, the emotion estimation function can be used to analyze the emotional state of operators and collect data for future stress reduction. This reduces the stress of operators and enables more efficient operations.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The status collection unit collects the status and logs of the base station. For example, it collects the base station's operating status, error logs, communication quality data, etc. The status collection unit can also collect the status and logs of the base station in real time. It also collects surrounding environment data (weather information, earthquake information, etc.) and provides it to the generation AI. Step 2: The analysis unit analyzes the base station status and logs collected by the status collection unit. For example, the generation AI not only detects abnormal patterns learned from past data, but also detects new, unknown abnormal patterns that emerge. It also analyzes abnormalities in the physical vibrations and sounds of the base station. Step 3: The countermeasure generation unit generates optimal countermeasures based on the results of the analysis by the analysis unit. For example, it proposes specific countermeasures such as replacing specific parts, restarting the system, or changing settings. It also considers the success rate and cost of past countermeasures to propose the most effective and economical countermeasure. It also considers the surrounding environment of the base station and the characteristics of users to propose countermeasures specific to the region. Step 4: The report generator generates a report based on the countermeasures generated by the countermeasure generator. For example, the report may include details of the failure or fault, the proposed countermeasures, and the results of the implemented countermeasures. It also considers evaluation data from past reports and automatically selects the most highly rated format and content. It also considers the base station's operational history and trend data to generate a report that includes future predictions.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

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

[0111] The 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.

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

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

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

[0115] Fig. 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.

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

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

[0118] In the 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.

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

[0120] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

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

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

[0134] In the 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.

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

[0136] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a status collection unit that collects the status and logs of the base station; an analysis unit that analyzes the status and logs of the base station collected by the status collection unit; a countermeasure generation unit that generates an optimal countermeasure based on the results of the analysis by the analysis unit; a report generation unit that generates a report based on the countermeasure generated by the countermeasure generation unit. A system characterized by:

2. The status collection unit In addition to the status and logs of the base station, surrounding environmental data is collected and provided to the generation AI.

2. The system of claim 1.

3. The status collection unit The status and logs of the base station are collected in real time and provided to the generation AI sequentially.

2. The system of claim 1.

4. The status collection unit Monitors stress levels of operators and automatically provides assistance when stress levels rise 2. The system of claim 1.

5. The status collection unit Drones and robots will also be used to collect physical inspection data, which will be provided to the generation AI.

2. The system of claim 1.

6. The status collection unit Data from base stations from different manufacturers is integrated and provided to the generation AI.

2. The system of claim 1.

7. The status collection unit The satisfaction of base station users is collected in real time and provided to the generation AI.

2. The system of claim 1.

8. The analysis unit Detects not only abnormal patterns learned from past data, but also new and unknown abnormal patterns that emerge 2. The system of claim 1.

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