System

Abstract

An object of a system according to an embodiment is to automate analysis of log data and creation of a report and to improve efficiency.SOLUTION: A system according to an embodiment includes a data analysis unit, a summary generation unit, a report creation unit, and an image analysis unit. The data analysis unit analyzes the log data. The summary generation unit creates a summary on the basis of the result analyzed by the data analysis unit. The report creation unit creates a report based on the summary created by the summary generation unit. The image analysis unit analyzes image data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have the drawback of requiring manual labor to analyze log data and create reports, resulting in low efficiency.

[0005] The system according to the embodiment aims to automate the analysis of log data and the creation of reports, thereby improving efficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes a data analysis unit, a summary generation unit, a report creation unit, and an image analysis unit. The data analysis unit analyzes log data. The summary creation unit creates a summary based on the results of the analysis by the data analysis unit. The report creation unit creates a report based on the summary created by the summary creation unit. The image analysis unit analyzes image data. [Effects of the Invention]

[0007] The system according to the embodiment can automate the analysis of log data and the creation of reports, thereby improving efficiency. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A monitoring system according to an embodiment of the present invention utilizes generative AI to automate the analysis of log data, the creation of summaries, and the output of reports, and also analyzes image data. This enables the monitoring system to automate processes from collecting log data to creating reports, and also analyze image data.

[0029] A monitoring system according to an embodiment includes a data analysis unit, a summary generation unit, a report creation unit, and an image analysis unit. The data analysis unit analyzes log data. For example, the data analysis unit analyzes system error logs and performance data to detect abnormal behavior and performance degradation. The data analysis unit can also automatically detect specific patterns and trends from the log data and build a predictive model. For example, the data analysis unit can automatically detect specific patterns and trends from the log data and predict future system performance using a generation AI. The data analysis unit can also predict future system performance and error occurrence based on the analyzed data. For example, the data analysis unit can predict future system performance and error occurrence based on the data analyzed by the generation AI. The summary generation unit creates a summary based on the results of the analysis by the data analysis unit. For example, the generation AI creates a data summary based on the analysis results and summarizes important points. The summary generation unit can also automatically analyze data correlations when creating summaries to provide deeper insights. For example, the generation AI can automatically analyze data correlations when creating summaries to identify areas for system improvement. Furthermore, the summary generation unit can automatically generate suggestions for system improvements and optimization based on the created summary. For example, the summary generation unit can automatically generate suggestions for system improvements and optimization based on the summary created by the generation AI. The report creation unit creates a report based on the summary created by the summary generation unit. For example, the generation AI creates a report based on the summary and the summarized content, automatically generating a report that includes the system's operating status, error details, and performance analysis results. The report creation unit can also automatically analyze data trends and patterns when creating a report, and include future predictions. For example, the generation AI can automatically analyze data trends and patterns when creating a report, and include future predictions. The report creation unit can also automatically generate suggestions for improving system performance and security based on the created report.For example, the system automatically generates system performance improvement proposals and security improvement proposals based on reports created by the generation AI. The image analysis unit analyzes image data. For example, the generation AI applies an anomaly detection algorithm to the image data to identify potential security risks. The image analysis unit can also build a feedback loop that optimizes system performance in real time based on the analyzed image data. For example, the image analysis unit can build a feedback loop that optimizes system performance in real time based on the image data analyzed by the generation AI. Furthermore, the image analysis unit can use an emotion estimation function to evaluate the emotional impact of the analyzed image data on a user and take measures to improve user satisfaction. For example, the emotion estimation function can evaluate the emotional impact of the analyzed image data on a user and take measures to improve user satisfaction. As a result, the monitoring system according to the embodiment automates processes from collecting log data to creating reports and can also analyze image data. For example, the system's operating status can be monitored in real time and an immediate response can be made if an abnormality occurs. Furthermore, the automation of periodic report creation reduces the burden on administrators. Furthermore, the image analysis function can be used to efficiently monitor surveillance camera footage and enhance security.

[0030] The data analysis unit can automatically detect specific patterns and trends from the log data and build a predictive model. The data analysis unit, for example, uses a generation AI to automatically detect specific patterns and trends from the log data. For example, it analyzes a system's error log and identifies errors that occur frequently during specific time periods or under specific conditions. The data analysis unit also uses a generation AI to detect trends from the log data and predict future system performance. For example, it predicts periods when system load will increase based on past data. The data analysis unit also uses a generation AI to detect specific patterns from the log data and build a predictive model. For example, it detects signs of a specific error pattern occurring and takes measures in advance. In this way, by detecting specific patterns and trends from the log data and building a predictive model, it is possible to predict the future operation of the system.

[0031] The data analysis unit can predict the future performance of the system and the occurrence of errors based on the analyzed data. For example, the data analysis unit predicts the future performance of the system based on data analyzed by the generation AI. For example, it predicts future increases in load based on past performance data. The data analysis unit also predicts the occurrence of errors based on data analyzed by the generation AI. For example, it calculates the probability of an error occurring under certain conditions and takes measures in advance. The data analysis unit also predicts the future performance of the system and the occurrence of errors based on data analyzed by the generation AI. For example, it monitors the system's operating status in real time and issues an alert before an abnormality occurs. This makes it possible to take measures in advance by predicting the future performance of the system and the occurrence of errors.

[0032] The data analysis unit can integrate log data between different systems and evaluate overall system performance. The data analysis unit, for example, uses generative AI to integrate log data between different systems and evaluate overall system performance. For example, log data from multiple servers is integrated to analyze overall performance. The data analysis unit also uses generative AI to integrate log data between different systems and analyze interactions between systems. For example, log data from network devices and servers is integrated to identify communication bottlenecks. The data analysis unit also uses generative AI to integrate log data between different systems and evaluate overall system performance. For example, log data from cloud services and on-premises systems is integrated to optimize overall performance. In this way, overall system performance can be evaluated by integrating log data between different systems.

[0033] The data analysis unit can combine the analysis results of the log data with other data sources (e.g., sensor data and user behavior data) to perform a comprehensive analysis. The data analysis unit, for example, uses a generation AI to combine the analysis results of the log data with sensor data to perform a comprehensive analysis. For example, it combines temperature sensor data with server logs to identify errors caused by overheating. The data analysis unit also uses a generation AI to combine the analysis results of the log data with user behavior data to perform a comprehensive analysis. For example, it combines user operation logs with system error logs to identify the cause of an error caused by a specific operation. The data analysis unit also uses a generation AI to combine the analysis results of the log data with other data sources to perform a comprehensive analysis. For example, it combines network traffic data with system logs to identify communication bottlenecks. This makes it possible to perform a comprehensive analysis by combining the analysis results of the log data with other data sources.

[0034] The data analysis unit can build a feedback loop that optimizes system performance in real time based on the extracted important information. The data analysis unit, for example, builds a feedback loop that optimizes system performance in real time based on the important information extracted by the generation AI. For example, it automatically adjusts load balancing. The data analysis unit also builds a feedback loop that optimizes system performance in real time based on the important information extracted by the generation AI. For example, it dynamically changes resource allocation. The data analysis unit also builds a feedback loop that optimizes system performance in real time based on the important information extracted by the generation AI. For example, it automatically recovers when an error occurs. In this way, by building a feedback loop that optimizes system performance in real time, system efficiency is improved.

[0035] The data analysis unit can unify different log data formats to improve analysis efficiency. The data analysis unit, for example, uses generation AI to unify different log data formats to improve analysis efficiency. For example, it converts text format logs and binary format logs into a unified format. The data analysis unit also uses generation AI to unify different log data formats to improve analysis efficiency. For example, it converts log data collected from different systems into a unified format. The data analysis unit also uses generation AI to unify different log data formats to improve analysis efficiency. For example, it automatically analyzes different log formats and converts them into a unified format. In this way, the efficiency of analysis is improved by unifying different log data formats.

[0036] The data analysis unit can link the results of log data analysis with other systems (e.g., CRM systems or ERP systems) to improve business processes. For example, the data analysis unit uses generation AI to link the results of log data analysis with a CRM system to improve customer service. For example, it links customer inquiry history with a system error log. The data analysis unit also uses generation AI to link the results of log data analysis with an ERP system to improve business processes. For example, it links log data from an inventory management system and a production management system. The data analysis unit also uses generation AI to link the results of log data analysis with other systems to improve business processes. For example, it links log data from a sales support system and a marketing system. In this way, business processes can be improved by linking the results of log data analysis with other systems.

[0037] The summary generation unit can automatically analyze data correlations when creating summaries, thereby providing deeper insights. The summary generation unit, for example, uses generation AI to automatically analyze data correlations when creating summaries. For example, it analyzes the correlation between error logs and performance data to identify the cause of the error. The summary generation unit also uses generation AI to analyze data correlations when creating summaries, thereby identifying areas for improvement in the system. For example, it analyzes the impact of specific operations on system performance. The summary generation unit also uses generation AI to automatically analyze data correlations when creating summaries, thereby providing deeper insights. For example, it analyzes the correlation between user behavior data and system logs to help improve the user experience. This allows deeper insights to be provided by analyzing data correlations.

[0038] The summary generation unit can automatically generate suggestions for system improvements and optimization based on the created summary. For example, the summary generation unit automatically generates suggestions for system improvements based on the summary created by the generation AI. For example, it proposes the cause of an error and countermeasures based on a summary of an error log. The summary generation unit also automatically generates suggestions for system optimization based on the summary created by the generation AI. For example, it proposes optimal resource allocation based on a summary of performance data. The summary generation unit also automatically generates suggestions for system improvements and optimization based on the summary created by the generation AI. For example, it proposes measures to improve the user experience based on a summary of user behavior data. In this way, system efficiency is improved by automatically generating suggestions for system improvements and optimization based on the summary.

[0039] The summary generation unit can integrate information from different data sources to create a comprehensive summary. The summary generation unit, for example, uses generation AI to integrate information from different data sources to create a comprehensive summary. For example, it integrates system logs and user behavior data to create a summary. The summary generation unit also uses generation AI to integrate information from different data sources to create a comprehensive summary. For example, it integrates sensor data and system logs to create a summary. The summary generation unit also uses generation AI to integrate information from different data sources to create a comprehensive summary. For example, it integrates network traffic data and system logs to create a summary. In this way, a comprehensive summary can be created by integrating information from different data sources.

[0040] The summary generation unit can provide the summary in different formats (for example, a visual report or an audio report) to improve user convenience. The summary generation unit, for example, uses generation AI to provide the summary as a visual report. For example, it visually displays the main points of data using graphs and charts. The summary generation unit also uses generation AI to provide the summary as an audio report. For example, it generates a report that explains important points audio-wise. The summary generation unit also uses generation AI to provide the summary in different formats to improve user convenience. For example, it provides a combination of a text report and a visual report. In this way, providing the summary in different formats improves user convenience.

[0041] The report creation unit can automatically analyze data trends and patterns when creating a report and include future predictions. The report creation unit, for example, uses a generation AI to automatically analyze data trends and include future predictions when creating a report. For example, predicting future load increases based on system performance data. The report creation unit also uses a generation AI to automatically analyze data patterns when creating a report and include future predictions. For example, analyzing error log patterns and predicting future error occurrences. The report creation unit also uses a generation AI to automatically analyze data trends and patterns when creating a report and include future predictions. For example, predicting future user behavior based on user behavior data. In this way, by analyzing data trends and patterns when creating a report and including future predictions, future trends of the system can be understood.

[0042] The report creation unit can automatically generate improvement proposals for system performance and security based on the created report. The report creation unit, for example, automatically generates improvement proposals for system performance based on the report created by the generation AI. For example, it may propose optimal resource allocation. The report creation unit also automatically generates improvement proposals for system security based on the report created by the generation AI. For example, it may propose measures to prevent unauthorized access. The report creation unit also automatically generates improvement proposals for system performance and security based on the report created by the generation AI. For example, it may propose the cause of an error and countermeasures. In this way, by automatically generating improvement proposals for system performance and security based on the report, the efficiency and safety of the system are improved.

[0043] The report creation unit can integrate information from different data sources and create a comprehensive report. The report creation unit, for example, uses a generation AI to integrate information from different data sources and create a comprehensive report. For example, the report creation unit integrates system logs and user behavior data to create a report. The report creation unit also uses a generation AI to integrate information from different data sources and create a comprehensive report. For example, the report creation unit integrates sensor data and system logs to create a report. The report creation unit also uses a generation AI to integrate information from different data sources and create a comprehensive report. For example, the report creation unit integrates network traffic data and system logs to create a report. This makes it possible to create a comprehensive report by integrating information from different data sources.

[0044] The report creation unit can provide reports in different formats (for example, visual reports or audio reports) to improve user convenience. The report creation unit, for example, uses generation AI to provide reports as visual reports. For example, it visually displays the main points of data using graphs and charts. The report creation unit also uses generation AI to provide reports as audio reports. For example, it generates reports that explain important points audio-wise. The report creation unit also uses generation AI to provide reports in different formats to improve user convenience. For example, it provides a combination of text reports and visual reports. In this way, user convenience is improved by providing reports in different formats.

[0045] The image analysis unit can apply an anomaly detection algorithm to the image data to identify potential security risks. The image analysis unit, for example, uses a generation AI to apply an anomaly detection algorithm to the image data to identify potential security risks. For example, it detects suspicious people from surveillance camera footage. The image analysis unit also uses a generation AI to apply an anomaly detection algorithm to the image data to identify potential security risks. For example, it detects abnormal behavior from surveillance footage inside a factory. The image analysis unit also uses a generation AI to apply an anomaly detection algorithm to the image data to identify potential security risks. For example, it detects suspicious vehicles from surveillance footage of a parking lot. In this way, the safety of the system is improved by applying an anomaly detection algorithm to the image data to identify potential security risks.

[0046] The image analysis unit can build a feedback loop that optimizes system performance in real time based on the analyzed image data. The image analysis unit builds a feedback loop that optimizes system performance in real time based on the image data analyzed by the generation AI. For example, it analyzes surveillance camera footage and issues an alert if an abnormality occurs. The image analysis unit also builds a feedback loop that optimizes system performance in real time based on the image data analyzed by the generation AI. For example, it analyzes surveillance footage within a factory and automatically takes measures if an abnormality occurs. The image analysis unit also builds a feedback loop that optimizes system performance in real time based on the image data analyzed by the generation AI. For example, it analyzes surveillance footage of a parking lot and automatically issues an alert if an abnormality occurs. In this way, by building a feedback loop that optimizes system performance in real time, system efficiency is improved.

[0047] The image analysis unit can link the results of image data analysis with other systems (e.g., CRM systems or ERP systems) to improve business processes. The image analysis unit, for example, uses generation AI to link the results of image data analysis with a CRM system to improve customer service. For example, it links customer facial recognition data with customer history. The image analysis unit also uses generation AI to link the results of image data analysis with an ERP system to improve business processes. For example, it links image data from an inventory management system and a production management system. The image analysis unit also uses generation AI to link the results of image data analysis with other systems to improve business processes. For example, it links image data from a sales support system and a marketing system. In this way, business processes can be improved by linking the results of image data analysis with other systems.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The surveillance system can further include an audio analysis unit. The audio analysis unit can analyze audio data and detect abnormal sounds or specific keywords. For example, the audio analysis unit can analyze audio data from a surveillance camera and detect abnormal sounds such as the sound of breaking glass or screams. The audio analysis unit can also detect specific keywords from the audio data and issue an alert. For example, it can detect highly urgent keywords such as "help" or "fire." Furthermore, the audio analysis unit can create a feedback loop that optimizes system performance in real time based on the analyzed audio data. For example, if an abnormal sound is detected, the viewpoint of the surveillance camera can be automatically adjusted. This makes it possible to improve the safety and efficiency of the system through the analysis of audio data.

[0050] The monitoring system may further include an environmental monitoring unit. The environmental monitoring unit may collect and analyze environmental data such as temperature, humidity, and illuminance. For example, the environmental monitoring unit may collect temperature data from a server room to detect the risk of overheating. The environmental monitoring unit may also analyze humidity data to predict the risk of condensation and mold. Furthermore, the environmental monitoring unit may optimize lighting based on illuminance data. For example, the illuminance data may be analyzed and the lighting may be automatically adjusted as needed. This allows the safety and efficiency of the system to be improved through the analysis of environmental data.

[0051] The monitoring system can further include a behavior analysis unit. The behavior analysis unit can analyze user behavior data and detect abnormal behavior or patterns. For example, the behavior analysis unit can analyze user access logs and detect access patterns that differ from normal. The behavior analysis unit can also analyze user operation logs and detect unauthorized or abnormal operations. Furthermore, the behavior analysis unit can build a feedback loop that optimizes system performance in real time based on the analyzed behavior data. For example, if abnormal behavior is detected, access restrictions can be automatically set. This makes it possible to improve the safety and efficiency of the system through the analysis of behavior data.

[0052] The monitoring system can further include a predictive maintenance unit. The predictive maintenance unit can analyze the system's operation data and predict future failures and the need for maintenance. For example, the predictive maintenance unit can analyze server operation data and predict the risk of hardware failure. The predictive maintenance unit can also analyze network device operation data and predict the need for maintenance. Furthermore, the predictive maintenance unit can automatically generate a maintenance schedule based on the analyzed data. For example, maintenance can be performed at the appropriate time before the risk of failure increases. This makes it possible to improve the system's operating efficiency and reliability through predictive maintenance.

[0053] The monitoring system can further include an energy management unit. The energy management unit can collect and analyze energy consumption data of the system. For example, the energy management unit can collect energy consumption data of servers and network devices and evaluate their energy efficiency. The energy management unit can also make suggestions for improving energy efficiency based on the energy consumption data. For example, it can make suggestions for optimizing the use of devices with high energy consumption. Furthermore, the energy management unit can create a feedback loop that optimizes energy consumption in real time based on the analyzed data. For example, it can automatically balance loads when energy consumption increases. This makes it possible to improve the energy efficiency and sustainability of the system through energy management.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The data analysis unit analyzes the log data. For example, it analyzes the system's error log and performance data to detect abnormal behavior or performance degradation. It can also automatically detect specific patterns and trends and build predictive models. It also uses the analyzed data to predict future system performance and error occurrences. Step 2: The summary generation unit creates a summary based on the results of the analysis by the data analysis unit. For example, it creates a data summary based on the analysis results and summarizes the important points. In addition, when creating the summary, it automatically analyzes data correlations and automatically generates suggestions for system improvements and optimization. Step 3: The report creation unit creates a report based on the summary created by the summary generation unit. For example, it creates a report based on the summary and the compiled content, and automatically generates a report that includes the system's operating status, error details, and performance analysis results. It can also automatically analyze data trends and patterns when creating a report, and include future predictions. Furthermore, it automatically generates improvement proposals for system performance and security based on the created report. Step 4: The image analysis unit analyzes the image data. For example, it applies anomaly detection algorithms to identify potential security risks within the image data. It can also build a feedback loop based on the analyzed image data to optimize system performance in real time. Furthermore, it uses emotion estimation to evaluate the emotional impact of the analyzed image data on the user and take measures to improve user satisfaction.

[0056] (Example 2) A monitoring system according to an embodiment of the present invention utilizes generative AI to automate the analysis of log data, the creation of summaries, and the output of reports, and also analyzes image data. This enables the monitoring system to automate processes from collecting log data to creating reports, and also analyze image data.

[0057] A monitoring system according to an embodiment includes a data analysis unit, a summary generation unit, a report creation unit, and an image analysis unit. The data analysis unit analyzes log data. For example, the data analysis unit analyzes system error logs and performance data to detect abnormal behavior and performance degradation. The data analysis unit can also automatically detect specific patterns and trends from the log data and build a predictive model. For example, the data analysis unit can automatically detect specific patterns and trends from the log data and predict future system performance using a generation AI. The data analysis unit can also predict future system performance and error occurrence based on the analyzed data. For example, the data analysis unit can predict future system performance and error occurrence based on the data analyzed by the generation AI. The summary generation unit creates a summary based on the results of the analysis by the data analysis unit. For example, the generation AI creates a data summary based on the analysis results and summarizes important points. The summary generation unit can also automatically analyze data correlations when creating summaries to provide deeper insights. For example, the generation AI can automatically analyze data correlations when creating summaries to identify areas for system improvement. Furthermore, the summary generation unit can automatically generate suggestions for system improvements and optimization based on the created summary. For example, the summary generation unit can automatically generate suggestions for system improvements and optimization based on the summary created by the generation AI. The report creation unit creates a report based on the summary created by the summary generation unit. For example, the generation AI creates a report based on the summary and the summarized content, automatically generating a report that includes the system's operating status, error details, and performance analysis results. The report creation unit can also automatically analyze data trends and patterns when creating a report, and include future predictions. For example, the generation AI can automatically analyze data trends and patterns when creating a report, and include future predictions. The report creation unit can also automatically generate suggestions for improving system performance and security based on the created report.For example, the system automatically generates system performance improvement proposals and security improvement proposals based on reports created by the generation AI. The image analysis unit analyzes image data. For example, the generation AI applies an anomaly detection algorithm to the image data to identify potential security risks. The image analysis unit can also build a feedback loop that optimizes system performance in real time based on the analyzed image data. For example, the image analysis unit can build a feedback loop that optimizes system performance in real time based on the image data analyzed by the generation AI. Furthermore, the image analysis unit can use an emotion estimation function to evaluate the emotional impact of the analyzed image data on a user and take measures to improve user satisfaction. For example, the emotion estimation function can evaluate the emotional impact of the analyzed image data on a user and take measures to improve user satisfaction. As a result, the monitoring system according to the embodiment automates processes from collecting log data to creating reports and can also analyze image data. For example, the system's operating status can be monitored in real time and an immediate response can be made if an abnormality occurs. Furthermore, the automation of periodic report creation reduces the burden on administrators. Furthermore, the image analysis function can be used to efficiently monitor surveillance camera footage and enhance security.

[0058] The data analysis unit can automatically detect specific patterns and trends from the log data and build a predictive model. The data analysis unit, for example, uses a generation AI to automatically detect specific patterns and trends from the log data. For example, it analyzes a system's error log and identifies errors that occur frequently during specific time periods or under specific conditions. The data analysis unit also uses a generation AI to detect trends from the log data and predict future system performance. For example, it predicts periods when system load will increase based on past data. The data analysis unit also uses a generation AI to detect specific patterns from the log data and build a predictive model. For example, it detects signs of a specific error pattern occurring and takes measures in advance. In this way, by detecting specific patterns and trends from the log data and building a predictive model, it is possible to predict the future operation of the system.

[0059] The data analysis unit can predict the future performance of the system and the occurrence of errors based on the analyzed data. For example, the data analysis unit predicts the future performance of the system based on data analyzed by the generation AI. For example, it predicts future increases in load based on past performance data. The data analysis unit also predicts the occurrence of errors based on data analyzed by the generation AI. For example, it calculates the probability of an error occurring under certain conditions and takes measures in advance. The data analysis unit also predicts the future performance of the system and the occurrence of errors based on data analyzed by the generation AI. For example, it monitors the system's operating status in real time and issues an alert before an abnormality occurs. This makes it possible to take measures in advance by predicting the future performance of the system and the occurrence of errors.

[0060] The data analysis unit uses the emotion estimation function to analyze how users feel about the log data, which can be useful in improving the user experience. For example, the data analysis unit uses the emotion estimation function to analyze how users of the system feel about the log data. For example, it analyzes the user's dissatisfaction and stress about the error log. The data analysis unit also uses the emotion estimation function to identify areas for improvement in the system based on the user's emotion data. For example, it takes measures to reduce the stress felt by the user when a specific error occurs. The data analysis unit also uses the emotion estimation function to analyze the user's emotion data, which can be useful in improving the user experience. For example, it makes suggestions for improving the system so that the user feels positive emotions. In this way, analyzing the user's emotions can be useful in improving the user experience.

[0061] The data analysis unit can integrate log data between different systems and evaluate overall system performance. The data analysis unit, for example, uses generative AI to integrate log data between different systems and evaluate overall system performance. For example, log data from multiple servers is integrated to analyze overall performance. The data analysis unit also uses generative AI to integrate log data between different systems and analyze interactions between systems. For example, log data from network devices and servers is integrated to identify communication bottlenecks. The data analysis unit also uses generative AI to integrate log data between different systems and evaluate overall system performance. For example, log data from cloud services and on-premises systems is integrated to optimize overall performance. In this way, overall system performance can be evaluated by integrating log data between different systems.

[0062] The data analysis unit can combine the analysis results of the log data with other data sources (e.g., sensor data and user behavior data) to perform a comprehensive analysis. The data analysis unit, for example, uses a generation AI to combine the analysis results of the log data with sensor data to perform a comprehensive analysis. For example, it combines temperature sensor data with server logs to identify errors caused by overheating. The data analysis unit also uses a generation AI to combine the analysis results of the log data with user behavior data to perform a comprehensive analysis. For example, it combines user operation logs with system error logs to identify the cause of an error caused by a specific operation. The data analysis unit also uses a generation AI to combine the analysis results of the log data with other data sources to perform a comprehensive analysis. For example, it combines network traffic data with system logs to identify communication bottlenecks. This makes it possible to perform a comprehensive analysis by combining the analysis results of the log data with other data sources.

[0063] The data analysis unit can use the emotion estimation function to predict user emotions based on the analysis results of the log data and propose improvements to the system. The data analysis unit, for example, uses the emotion estimation function to predict user emotions based on the analysis results of the log data. For example, it predicts the stress the user will feel when a specific error log occurs. The data analysis unit also uses the emotion estimation function to propose improvements to the system based on the user's emotion data. For example, it proposes system improvements that will make the user feel positive emotions. The data analysis unit also uses the emotion estimation function to predict user emotions based on the analysis results of the log data and propose improvements to the system. For example, it takes measures to reduce the stress the user feels. In this way, by predicting user emotions and proposing improvements to the system, it is possible to improve the user experience.

[0064] The data analysis unit can build a feedback loop that optimizes system performance in real time based on the extracted important information. The data analysis unit, for example, builds a feedback loop that optimizes system performance in real time based on the important information extracted by the generation AI. For example, it automatically adjusts load balancing. The data analysis unit also builds a feedback loop that optimizes system performance in real time based on the important information extracted by the generation AI. For example, it dynamically changes resource allocation. The data analysis unit also builds a feedback loop that optimizes system performance in real time based on the important information extracted by the generation AI. For example, it automatically recovers when an error occurs. In this way, by building a feedback loop that optimizes system performance in real time, system efficiency is improved.

[0065] The data analysis unit can use the emotion estimation function to evaluate the emotional impact of the extracted information on the user and take measures to improve user satisfaction. The data analysis unit, for example, uses the emotion estimation function to evaluate the emotional impact of the extracted information on the user. For example, it evaluates the stress that an error message causes to the user. The data analysis unit also uses the emotion estimation function to identify areas for improvement in the system based on the user's emotion data. For example, it proposes system improvements that will make the user feel positive emotions. The data analysis unit also uses the emotion estimation function to evaluate the emotional impact of the extracted information on the user and take measures to improve user satisfaction. For example, it takes measures to reduce the stress felt by the user. In this way, the user experience is improved by evaluating the emotional impact on the user and taking measures to improve user satisfaction.

[0066] The data analysis unit can unify different log data formats to improve analysis efficiency. The data analysis unit, for example, uses generation AI to unify different log data formats to improve analysis efficiency. For example, it converts text format logs and binary format logs into a unified format. The data analysis unit also uses generation AI to unify different log data formats to improve analysis efficiency. For example, it converts log data collected from different systems into a unified format. The data analysis unit also uses generation AI to unify different log data formats to improve analysis efficiency. For example, it automatically analyzes different log formats and converts them into a unified format. In this way, the efficiency of analysis is improved by unifying different log data formats.

[0067] The data analysis unit can link the results of log data analysis with other systems (e.g., CRM systems or ERP systems) to improve business processes. For example, the data analysis unit uses generation AI to link the results of log data analysis with a CRM system to improve customer service. For example, it links customer inquiry history with a system error log. The data analysis unit also uses generation AI to link the results of log data analysis with an ERP system to improve business processes. For example, it links log data from an inventory management system and a production management system. The data analysis unit also uses generation AI to link the results of log data analysis with other systems to improve business processes. For example, it links log data from a sales support system and a marketing system. In this way, business processes can be improved by linking the results of log data analysis with other systems.

[0068] The data analysis unit can use the emotion estimation function to predict the user's emotions based on the extracted information, thereby improving the quality of customer support. The data analysis unit, for example, uses the emotion estimation function to predict the user's emotions based on the extracted information. For example, it predicts the emotional impact that a specific error message will have on the user. The data analysis unit also uses the emotion estimation function to improve the quality of customer support based on the user's emotion data. For example, it takes measures to reduce the stress felt by the user. The data analysis unit also uses the emotion estimation function to predict the user's emotions based on the extracted information, thereby improving the quality of customer support. For example, it makes support suggestions that will make the user feel positive emotions. In this way, it is possible to predict the user's emotions and improve the quality of customer support, thereby improving user satisfaction.

[0069] The summary generation unit can automatically analyze data correlations when creating summaries, thereby providing deeper insights. The summary generation unit, for example, uses generation AI to automatically analyze data correlations when creating summaries. For example, it analyzes the correlation between error logs and performance data to identify the cause of the error. The summary generation unit also uses generation AI to analyze data correlations when creating summaries, thereby identifying areas for improvement in the system. For example, it analyzes the impact of specific operations on system performance. The summary generation unit also uses generation AI to automatically analyze data correlations when creating summaries, thereby providing deeper insights. For example, it analyzes the correlation between user behavior data and system logs to help improve the user experience. This allows deeper insights to be provided by analyzing data correlations.

[0070] The summary generation unit can automatically generate suggestions for system improvements and optimization based on the created summary. For example, the summary generation unit automatically generates suggestions for system improvements based on the summary created by the generation AI. For example, it proposes the cause of an error and countermeasures based on a summary of an error log. The summary generation unit also automatically generates suggestions for system optimization based on the summary created by the generation AI. For example, it proposes optimal resource allocation based on a summary of performance data. The summary generation unit also automatically generates suggestions for system improvements and optimization based on the summary created by the generation AI. For example, it proposes measures to improve the user experience based on a summary of user behavior data. In this way, system efficiency is improved by automatically generating suggestions for system improvements and optimization based on the summary.

[0071] The summary generation unit can use the emotion estimation function to evaluate the emotional impact of the summary content on the user and adjust it to elicit positive emotions. The summary generation unit, for example, uses the emotion estimation function to evaluate the emotional impact of the summary content on the user. For example, it evaluates the stress that an error log summary causes to the user. The summary generation unit also uses the emotion estimation function to adjust the summary content to elicit positive emotions. For example, it changes the error log summary to positive expressions. The summary generation unit also uses the emotion estimation function to evaluate the emotional impact of the summary content on the user and adjusts it to elicit positive emotions. For example, it creates a summary that makes the user feel positive emotions. In this way, the user experience is improved by evaluating the emotional impact of the summary content on the user and adjusting it to elicit positive emotions.

[0072] The summary generation unit can integrate information from different data sources to create a comprehensive summary. The summary generation unit, for example, uses generation AI to integrate information from different data sources to create a comprehensive summary. For example, it integrates system logs and user behavior data to create a summary. The summary generation unit also uses generation AI to integrate information from different data sources to create a comprehensive summary. For example, it integrates sensor data and system logs to create a summary. The summary generation unit also uses generation AI to integrate information from different data sources to create a comprehensive summary. For example, it integrates network traffic data and system logs to create a summary. In this way, a comprehensive summary can be created by integrating information from different data sources.

[0073] The summary generation unit can provide the summary in different formats (for example, a visual report or an audio report) to improve user convenience. The summary generation unit, for example, uses generation AI to provide the summary as a visual report. For example, it visually displays the main points of data using graphs and charts. The summary generation unit also uses generation AI to provide the summary as an audio report. For example, it generates a report that explains important points audio-wise. The summary generation unit also uses generation AI to provide the summary in different formats to improve user convenience. For example, it provides a combination of a text report and a visual report. In this way, providing the summary in different formats improves user convenience.

[0074] The report creation unit can automatically analyze data trends and patterns when creating a report and include future predictions. The report creation unit, for example, uses a generation AI to automatically analyze data trends and include future predictions when creating a report. For example, predicting future load increases based on system performance data. The report creation unit also uses a generation AI to automatically analyze data patterns when creating a report and include future predictions. For example, analyzing error log patterns and predicting future error occurrences. The report creation unit also uses a generation AI to automatically analyze data trends and patterns when creating a report and include future predictions. For example, predicting future user behavior based on user behavior data. In this way, by analyzing data trends and patterns when creating a report and including future predictions, future trends of the system can be understood.

[0075] The report creation unit can automatically generate improvement proposals for system performance and security based on the created report. The report creation unit, for example, automatically generates improvement proposals for system performance based on the report created by the generation AI. For example, it may propose optimal resource allocation. The report creation unit also automatically generates improvement proposals for system security based on the report created by the generation AI. For example, it may propose measures to prevent unauthorized access. The report creation unit also automatically generates improvement proposals for system performance and security based on the report created by the generation AI. For example, it may propose the cause of an error and countermeasures. In this way, by automatically generating improvement proposals for system performance and security based on the report, the efficiency and safety of the system are improved.

[0076] The report creation unit can use the emotion estimation function to evaluate the emotional impact of the report content on the user and adjust the report to elicit positive emotions. The report creation unit, for example, uses the emotion estimation function to evaluate the emotional impact of the report content on the user. For example, it evaluates the stress that an error report causes to the user. The report creation unit also uses the emotion estimation function to adjust the report content to elicit positive emotions. For example, it changes the error report to positive expressions. The report creation unit also uses the emotion estimation function to evaluate the emotional impact of the report content on the user and adjusts the report to elicit positive emotions. For example, it creates a report that makes the user feel positive emotions. In this way, the user experience is improved by evaluating the emotional impact of the report content on the user and adjusting the report to elicit positive emotions.

[0077] The report creation unit can integrate information from different data sources and create a comprehensive report. The report creation unit, for example, uses a generation AI to integrate information from different data sources and create a comprehensive report. For example, the report creation unit integrates system logs and user behavior data to create a report. The report creation unit also uses a generation AI to integrate information from different data sources and create a comprehensive report. For example, the report creation unit integrates sensor data and system logs to create a report. The report creation unit also uses a generation AI to integrate information from different data sources and create a comprehensive report. For example, the report creation unit integrates network traffic data and system logs to create a report. This makes it possible to create a comprehensive report by integrating information from different data sources.

[0078] The report creation unit can provide reports in different formats (for example, visual reports or audio reports) to improve user convenience. The report creation unit, for example, uses generation AI to provide reports as visual reports. For example, it visually displays the main points of data using graphs and charts. The report creation unit also uses generation AI to provide reports as audio reports. For example, it generates reports that explain important points audio-wise. The report creation unit also uses generation AI to provide reports in different formats to improve user convenience. For example, it provides a combination of text reports and visual reports. In this way, user convenience is improved by providing reports in different formats.

[0079] The report creation unit can use the emotion estimation function to predict the user's emotion based on the content of the report and provide personalized feedback. The report creation unit, for example, uses the emotion estimation function to predict the user's emotion based on the content of the report. For example, it predicts the emotional impact of an error report on the user. The report creation unit also uses the emotion estimation function to provide personalized feedback based on the user's emotion data. For example, it provides feedback that makes the user feel positive emotions. The report creation unit also uses the emotion estimation function to predict the user's emotion based on the content of the report and provide personalized feedback. For example, it provides feedback to reduce the stress felt by the user. In this way, the user experience is improved by predicting the user's emotion based on the content of the report and providing personalized feedback.

[0080] The image analysis unit can apply an anomaly detection algorithm to the image data to identify potential security risks. The image analysis unit, for example, uses a generation AI to apply an anomaly detection algorithm to the image data to identify potential security risks. For example, it detects suspicious people from surveillance camera footage. The image analysis unit also uses a generation AI to apply an anomaly detection algorithm to the image data to identify potential security risks. For example, it detects abnormal behavior from surveillance footage inside a factory. The image analysis unit also uses a generation AI to apply an anomaly detection algorithm to the image data to identify potential security risks. For example, it detects suspicious vehicles from surveillance footage of a parking lot. In this way, the safety of the system is improved by applying an anomaly detection algorithm to the image data to identify potential security risks.

[0081] The image analysis unit can build a feedback loop that optimizes system performance in real time based on the analyzed image data. The image analysis unit builds a feedback loop that optimizes system performance in real time based on the image data analyzed by the generation AI. For example, it analyzes surveillance camera footage and issues an alert if an abnormality occurs. The image analysis unit also builds a feedback loop that optimizes system performance in real time based on the image data analyzed by the generation AI. For example, it analyzes surveillance footage within a factory and automatically takes measures if an abnormality occurs. The image analysis unit also builds a feedback loop that optimizes system performance in real time based on the image data analyzed by the generation AI. For example, it analyzes surveillance footage of a parking lot and automatically issues an alert if an abnormality occurs. In this way, by building a feedback loop that optimizes system performance in real time, system efficiency is improved.

[0082] The image analysis unit can use the emotion estimation function to evaluate the emotional impact of the analyzed image data on the user and take measures to improve user satisfaction. The image analysis unit, for example, uses the emotion estimation function to evaluate the emotional impact of the analyzed image data on the user. For example, it evaluates the sense of security that surveillance camera footage gives to the user. The image analysis unit also uses the emotion estimation function to identify areas for improvement in the system based on the user's emotion data. For example, it proposes improvements to the surveillance system that will make the user feel positive emotions. The image analysis unit also uses the emotion estimation function to evaluate the emotional impact of the analyzed image data on the user and take measures to improve user satisfaction. For example, it takes measures to reduce the anxiety felt by the user. In this way, the user experience is improved by evaluating the emotional impact of the analyzed image data on the user and taking measures to improve user satisfaction.

[0083] The image analysis unit can link the results of image data analysis with other systems (e.g., CRM systems or ERP systems) to improve business processes. The image analysis unit, for example, uses generation AI to link the results of image data analysis with a CRM system to improve customer service. For example, it links customer facial recognition data with customer history. The image analysis unit also uses generation AI to link the results of image data analysis with an ERP system to improve business processes. For example, it links image data from an inventory management system and a production management system. The image analysis unit also uses generation AI to link the results of image data analysis with other systems to improve business processes. For example, it links image data from a sales support system and a marketing system. In this way, business processes can be improved by linking the results of image data analysis with other systems.

[0084] The image analysis unit uses the emotion estimation function to predict a user's emotion based on the analyzed image data, thereby improving the quality of customer support. The image analysis unit, for example, uses the emotion estimation function to predict a user's emotion based on the analyzed image data. For example, it predicts the emotional impact that surveillance camera footage has on a user. The image analysis unit also uses the emotion estimation function to improve the quality of customer support based on the user's emotion data. For example, it takes measures to reduce the anxiety felt by the user. The image analysis unit also uses the emotion estimation function to predict a user's emotion based on the analyzed image data, thereby improving the quality of customer support. For example, it makes support suggestions that will make the user feel positive emotions. In this way, predicting a user's emotion based on the analyzed image data and improving the quality of customer support improves user satisfaction.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The surveillance system can further include an audio analysis unit. The audio analysis unit can analyze audio data and detect abnormal sounds or specific keywords. For example, the audio analysis unit can analyze audio data from a surveillance camera and detect abnormal sounds such as the sound of breaking glass or screams. The audio analysis unit can also detect specific keywords from the audio data and issue an alert. For example, it can detect highly urgent keywords such as "help" or "fire." Furthermore, the audio analysis unit can create a feedback loop that optimizes system performance in real time based on the analyzed audio data. For example, if an abnormal sound is detected, the viewpoint of the surveillance camera can be automatically adjusted. This makes it possible to improve the safety and efficiency of the system through the analysis of audio data.

[0087] The monitoring system may further include an environmental monitoring unit. The environmental monitoring unit may collect and analyze environmental data such as temperature, humidity, and illuminance. For example, the environmental monitoring unit may collect temperature data from a server room to detect the risk of overheating. The environmental monitoring unit may also analyze humidity data to predict the risk of condensation and mold. Furthermore, the environmental monitoring unit may optimize lighting based on illuminance data. For example, the illuminance data may be analyzed and the lighting may be automatically adjusted as needed. This allows the safety and efficiency of the system to be improved through the analysis of environmental data.

[0088] The monitoring system can further include a behavior analysis unit. The behavior analysis unit can analyze user behavior data and detect abnormal behavior or patterns. For example, the behavior analysis unit can analyze user access logs and detect access patterns that differ from normal. The behavior analysis unit can also analyze user operation logs and detect unauthorized or abnormal operations. Furthermore, the behavior analysis unit can build a feedback loop that optimizes system performance in real time based on the analyzed behavior data. For example, if abnormal behavior is detected, access restrictions can be automatically set. This makes it possible to improve the safety and efficiency of the system through the analysis of behavior data.

[0089] The monitoring system can further include a predictive maintenance unit. The predictive maintenance unit can analyze the system's operation data and predict future failures and the need for maintenance. For example, the predictive maintenance unit can analyze server operation data and predict the risk of hardware failure. The predictive maintenance unit can also analyze network device operation data and predict the need for maintenance. Furthermore, the predictive maintenance unit can automatically generate a maintenance schedule based on the analyzed data. For example, maintenance can be performed at the appropriate time before the risk of failure increases. This makes it possible to improve the system's operating efficiency and reliability through predictive maintenance.

[0090] The monitoring system can further include an energy management unit. The energy management unit can collect and analyze energy consumption data of the system. For example, the energy management unit can collect energy consumption data of servers and network devices and evaluate their energy efficiency. The energy management unit can also make suggestions for improving energy efficiency based on the energy consumption data. For example, it can make suggestions for optimizing the use of devices with high energy consumption. Furthermore, the energy management unit can create a feedback loop that optimizes energy consumption in real time based on the analyzed data. For example, it can automatically balance loads when energy consumption increases. This makes it possible to improve the energy efficiency and sustainability of the system through energy management.

[0091] The monitoring system can further use the emotion estimation function to customize the interface based on the user's emotions. For example, if the user is feeling stressed, the color tone and layout of the interface can be changed to enhance relaxation. Also, if the user is feeling positive emotions, congratulatory messages and positive feedback can be displayed on the interface. Furthermore, the emotion estimation function can be used to set the priority of notifications based on the user's emotions. For example, if the user is feeling stressed, only important notifications can be displayed, and other notifications can be postponed. This makes it possible to improve the user experience through interface customization based on the user's emotions.

[0092] The monitoring system can further use the emotion estimation function to customize alerts based on the user's emotions. For example, if the user is feeling stressed, the volume and tone of the alert can be changed to reduce stress. Also, if the user is feeling positive emotions, a positive message can be added to the alert. Furthermore, the emotion estimation function can also be used to adjust the frequency of alerts based on the user's emotions. For example, if the user is feeling stressed, the frequency of alerts can be reduced and only important alerts can be displayed. This allows for an improved user experience through customization of alerts based on the user's emotions.

[0093] The monitoring system can further use the emotion estimation function to customize reports based on the user's emotions. For example, if the user is feeling stressed, the report content can be summarized concisely and only the important points can be highlighted. Also, if the user is feeling positive, detailed analysis and positive feedback can be added to the report. Furthermore, the emotion estimation function can be used to adjust the timing of report delivery based on the user's emotions. For example, if the user is feeling stressed, the delivery of the report can be delayed and delivered when the user is relaxed. This allows for an improved user experience through customization of reports based on the user's emotions.

[0094] The monitoring system can further use the emotion estimation function to provide feedback based on the user's emotions. For example, if the user is feeling stressed, the feedback can be changed to something gentle and encouraging. Also, if the user is feeling positive emotions, a message of gratitude or a positive rating can be added to the feedback. Furthermore, the emotion estimation function can be used to adjust the frequency of feedback based on the user's emotions. For example, if the user is feeling stressed, the frequency of feedback can be reduced and only important feedback can be provided. This makes it possible to improve the user experience by providing feedback based on the user's emotions.

[0095] The monitoring system can further use the emotion estimation function to provide a training program based on the user's emotions. For example, if the user is feeling stressed, a training program with a relaxing effect can be provided. Alternatively, if the user is feeling positive emotions, a challenging training program can be provided. Furthermore, the emotion estimation function can be used to monitor the progress of the training program based on the user's emotions and provide appropriate feedback. For example, if the user is feeling stressed, the progress of the training can be slowed down to enhance the relaxation effect. This makes it possible to improve the user experience by providing a training program based on the user's emotions.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The data analysis unit analyzes the log data. For example, it analyzes the system's error log and performance data to detect abnormal behavior or performance degradation. It can also automatically detect specific patterns and trends and build predictive models. It also uses the analyzed data to predict future system performance and error occurrences. Step 2: The summary generation unit creates a summary based on the results of the analysis by the data analysis unit. For example, it creates a data summary based on the analysis results and summarizes the important points. In addition, when creating the summary, it automatically analyzes data correlations and automatically generates suggestions for system improvements and optimization. Step 3: The report creation unit creates a report based on the summary created by the summary generation unit. For example, it creates a report based on the summary and the compiled content, and automatically generates a report that includes the system's operating status, error details, and performance analysis results. It can also automatically analyze data trends and patterns when creating a report, and include future predictions. Furthermore, it automatically generates improvement proposals for system performance and security based on the created report. Step 4: The image analysis unit analyzes the image data. For example, it applies anomaly detection algorithms to identify potential security risks within the image data. It can also build a feedback loop based on the analyzed image data to optimize system performance in real time. Furthermore, it uses emotion estimation to evaluate the emotional impact of the analyzed image data on the user and take measures to improve user satisfaction.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 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 system equipped with a generative AI, a data analysis unit that analyzes the log data; a summary generation unit that generates a summary based on the results of the analysis by the data analysis unit; a report creation unit that creates a report based on the summary created by the summary creation unit; an image analysis unit that analyzes image data; A system characterized by:

2. The data analysis unit Automatically detect specific patterns and trends from the log data and build predictive models 2. The system of claim 1.

3. The data analysis unit Based on the analyzed data, predict the future performance and occurrence of errors of the system.

2. The system of claim 1.

4. The data analysis unit Analyzing how users feel about the log data and using it to improve the user experience 2. The system of claim 1.

5. The data analysis unit Integrate the log data from different systems to assess overall system performance 2. The system of claim 1.

6. The data analysis unit The results of the analysis of the log data are combined with other data sources to perform a comprehensive analysis.

2. The system of claim 1.

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