System

Abstract

An object of a system according to an embodiment is to efficiently monitor data of the entire system and detect an abnormality at an early stage.SOLUTION: A system includes a data collection unit, an abnormality detection unit, and a warning unit. The data collection unit collects data of the entire system. The abnormality detection unit analyzes the data collected by the data collection unit to detect an abnormality. The warning unit issues a warning when an abnormality is detected by the abnormality detection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently monitor data from the entire system and detect abnormalities early.

[0005] The system according to the embodiment aims to efficiently monitor data of the entire system and detect abnormalities at an early stage. [Means for solving the problem]

[0006] A system according to an embodiment includes a data collection unit, an anomaly detection unit, and a warning unit. The data collection unit collects data for the entire system. The anomaly detection unit analyzes the data collected by the data collection unit to detect anomalies. The warning unit issues a warning when an anomaly is detected by the anomaly detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently monitor data of the entire system and detect abnormalities at an early stage. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 system monitoring system according to an embodiment of the present invention analyzes data from the entire system, automatically detects abnormalities and performance degradation, and issues a warning. This allows the system monitoring system to monitor the system efficiently and reduce the burden on the administrator.

[0029] A system monitoring system according to an embodiment includes a data collection unit, an anomaly detection unit, and a warning unit. The data collection unit collects data from the entire system, such as server CPU usage, memory usage, and network traffic. The data collection unit can also collect log data and user data. The anomaly detection unit analyzes the data collected by the data collection unit to detect anomalies. For example, the generation AI analyzes data from the entire system and detects abnormal patterns. The generation AI can also use an anomaly detection algorithm to detect performance degradation. The warning unit issues a warning when the anomaly detection unit detects an anomaly. For example, the generation AI can automatically generate a warning message when it detects an anomaly and send it to an administrator via email or a messaging app. The generation AI can also set a warning level according to the severity of the anomaly and prompt the administrator to take appropriate action. This allows the system monitoring system according to an embodiment to efficiently monitor the system and reduce the administrator's burden. For example, when an anomaly is detected, the generation AI can identify the cause of the anomaly and propose measures to prevent recurrence. The generation AI can also identify the scope of the anomaly's impact and propose measures to minimize the impact. Furthermore, generative AI can analyze the user's emotional response when an anomaly is detected and provide appropriate countermeasures.

[0030] The data collection unit can scan handwritten answers and convert them into digital data. For example, the data collection unit may use a scanner to read handwritten answers and save them as image data. Then, the image data may be converted into text data using OCR technology. The data collection unit may also use a smartphone camera to take a photo of the handwritten answer and convert the image data into text data using a dedicated app. For example, the app may automatically correct the image and perform character recognition. The data collection unit may also use a dedicated digital pen to write handwritten answers, which then converts the data into digital data in real time. For example, a sensor may detect the movement of the pen and save it as text data. In this way, converting handwritten answers into digital data makes it easier for generative AI to analyze.

[0031] When detecting an anomaly, the anomaly detection unit can perform causal analysis to identify the cause of the anomaly. For example, when the generation AI detects an anomaly, the anomaly detection unit performs causal analysis to identify the cause. For example, it analyzes whether a sudden increase in CPU usage is caused by the operation of a specific application. The generation AI can also use Granger causality testing or Bayesian networks to identify the cause of the anomaly. By identifying the cause of the anomaly, measures can be taken to prevent recurrence.

[0032] When an abnormality is detected, the warning unit can evaluate the severity of the abnormality and set a warning level according to the level of urgency. For example, when the generation AI detects an abnormality, the warning unit can evaluate the severity of the abnormality and set a warning level according to the level of urgency. For example, it can issue a low-level warning for minor abnormalities and a high-level warning for serious abnormalities. The generation AI can also evaluate the severity based on the scope and degree of impact of the abnormality, and the urgency based on the need for a response and time constraints. This enables a prompt response by issuing an appropriate warning according to the severity of the abnormality.

[0033] When an anomaly is detected, the warning unit can identify the cause of the anomaly and propose measures to prevent recurrence. For example, when the generation AI detects an anomaly, the warning unit can identify the cause of the anomaly and propose measures to prevent recurrence. For example, if the cause is a bug in a specific application, it can suggest applying a patch to fix it. The generation AI can also identify the cause of the anomaly using log analysis and system diagnosis and propose system updates or process improvements. This improves system stability by identifying the cause of the anomaly and proposing measures to prevent recurrence.

[0034] The data collection unit can introduce multimodal data analysis, which includes audio and image data in the data it collects. For example, the data collection unit can include audio data in the data collected by the generation AI and perform audio analysis. For example, it can record system error messages as audio and analyze their content to detect abnormalities. The generation AI can also collect image data and detect abnormalities using image recognition technology. For example, it can analyze surveillance camera footage to detect abnormal behavior. The generation AI can also perform integrated analysis of audio and image data and perform correlation analysis of heterogeneous data. This improves the accuracy of anomaly detection by performing multimodal data analysis, which includes audio and image data.

[0035] The data collection unit can integrate system monitoring data from different industries and develop cross-industry anomaly detection algorithms. For example, the data collection unit can integrate system monitoring data from different industries and develop a common anomaly detection algorithm. For example, data from the financial industry and the manufacturing industry can be integrated to improve the accuracy of anomaly detection. In addition, the generative AI can share data between different industries and apply algorithms across different industries. This improves the accuracy of anomaly detection by integrating data from different industries.

[0036] When an anomaly is detected, the anomaly detection unit can identify the scope of the anomaly's impact and propose measures to minimize the impact. For example, when the generation AI detects an anomaly, the anomaly detection unit can identify the scope of the anomaly's impact and propose measures to minimize the impact. For example, it can identify the scope of impact on a server where an anomaly has occurred and prevent the impact on other servers. The generation AI can also identify the affected system parts and the extent of the impact, and propose resetting the system or restoring a backup. This improves system stability by identifying the scope of impact of the anomaly and proposing measures to minimize the impact.

[0037] When an abnormality is detected, the warning unit can customize the content of the warning message to provide the recipient with the most appropriate information. For example, when the generation AI issues a warning, the warning unit can customize the content of the warning message to provide the recipient with the most appropriate information. For example, it can switch between messages that include technical details and simple messages. The generation AI can also adjust the content of the warning message depending on the recipient's role and skill level. In this way, customizing the content of the warning message provides the recipient with the most appropriate information and encourages a quick response.

[0038] When an abnormality is detected, the warning unit can set the priority of the warning and encourage a response according to the importance. For example, when the generation AI issues a warning, the warning unit can set the priority of the warning and encourage a response according to the importance. For example, it can issue a low-priority warning for minor abnormalities and a high-priority warning for serious abnormalities. The generation AI can also set priorities based on the impact and urgency, and evaluate the impact on the entire system and the business. In this way, setting the priority of warnings makes it possible to respond quickly according to the importance.

[0039] The warning unit can send a warning message through multiple channels when it detects an abnormality. For example, when the generation AI issues a warning, the warning unit sends the warning message through multiple channels. For example, the warning can be issued via email, SMS, or app notification. The generation AI can also select the method of sending the warning message according to the recipient's preferences. This allows warnings to be communicated quickly and reliably by sending the warning message through multiple channels.

[0040] The data collection unit can monitor the general-purpose AI's operation logs and execution commands to detect abnormal behavior. For example, the generation AI monitors the general-purpose AI's operation logs to detect abnormal behavior. For example, it detects an abnormality when an unexpected command is executed. The generation AI can also monitor the general-purpose AI's execution commands to detect abnormal behavior. For example, it monitors command line inputs and API calls to detect abnormal behavior. In this way, by monitoring the general-purpose AI's operation logs and execution commands and detecting abnormal behavior, it is possible to prevent the AI ​​from running wild.

[0041] When the data collection unit detects abnormal behavior, it can identify the scope of the abnormal behavior's impact and propose measures to minimize the impact. For example, when the generation AI detects abnormal behavior, the data collection unit can identify the scope of the abnormal behavior's impact and propose measures to minimize the impact. For example, isolating the system component where the abnormal behavior occurred. The generation AI can also identify the affected system parts and the extent of the impact, and propose resetting the system or restoring a backup. This improves system stability by identifying the scope of the abnormal behavior's impact and proposing measures to minimize the impact.

[0042] When the data collection unit detects abnormal behavior, it can identify the cause of the abnormal behavior and propose measures to prevent recurrence. For example, when the generation AI detects abnormal behavior, the data collection unit can identify the cause of the abnormal behavior and propose measures to prevent recurrence. For example, if a specific command is the cause, it can restrict the use of that command. The generation AI can also identify the cause using log analysis and system diagnosis and propose system updates or process improvements. This improves system stability by identifying the cause of abnormal behavior and proposing measures to prevent recurrence.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The system monitoring system can further include a predictive analysis unit. The predictive analysis unit predicts future abnormalities based on collected data. For example, it analyzes past data and predicts the possibility of an abnormality occurring if a specific pattern is repeated. The predictive analysis unit can also take into account performance fluctuations due to seasons and time of day to assess the risk of an abnormality occurring. This allows system administrators to take measures in advance and improve system stability.

[0045] A system monitoring system can also be equipped with a user interface unit. The user interface unit visually displays collected data and anomaly detection results. For example, it can display the real-time system status in dashboard format and provide detailed information in graphs and charts when an anomaly occurs. The user interface unit can also display tooltips and pop-ups that clearly explain the cause of the anomaly and possible countermeasures. This allows system administrators to respond to anomalies quickly and accurately.

[0046] The system monitoring system can also be equipped with an automatic repair unit. The automatic repair unit automatically performs repair work when an abnormality is detected. For example, if a specific service stops, it will automatically attempt to restart it. It can also isolate the component in which the abnormality occurred, minimizing the impact on the entire system. Furthermore, the automatic repair unit can change settings or apply patches to prevent the abnormality from recurring. This reduces system downtime and improves stability.

[0047] The system monitoring system may further include an energy management unit. The energy management unit monitors the energy consumption of the entire system and promotes efficient energy use. For example, it schedules non-critical tasks for off-peak hours to reduce energy consumption during peak hours. The energy management unit can also detect abnormal energy consumption and suggest measures to improve energy efficiency. This reduces the operating costs of the system and alleviates the environmental impact.

[0048] The system monitoring system can further include a security monitoring unit. The security monitoring unit monitors the security status of the entire system and detects abnormal access or attacks. For example, it monitors unauthorized access attempts and malware activity in real time and immediately issues an alert when an abnormality is detected. The security monitoring unit can also identify the cause of the abnormality and propose appropriate countermeasures. This strengthens the security of the system and ensures data protection.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The data collection unit collects data from the entire system. For example, it collects data such as the server's CPU usage, memory usage, and network traffic. It can also collect log data and user data. Step 2: The anomaly detection unit analyzes the data collected by the data collection unit to detect anomalies. For example, the generative AI analyzes data from the entire system to detect abnormal patterns. Anomaly detection algorithms can also be used to detect performance degradation. Step 3: The warning unit issues a warning when an abnormality is detected by the anomaly detection unit. For example, when an abnormality is detected, the generation AI can automatically generate a warning message and send it to the administrator via email or messaging app. It can also set a warning level depending on the severity of the abnormality, prompting appropriate action.

[0051] (Example 2) A system monitoring system according to an embodiment of the present invention analyzes data from the entire system, automatically detects abnormalities and performance degradation, and issues a warning. This allows the system monitoring system to monitor the system efficiently and reduce the burden on the administrator.

[0052] A system monitoring system according to an embodiment includes a data collection unit, an anomaly detection unit, and a warning unit. The data collection unit collects data from the entire system, such as server CPU usage, memory usage, and network traffic. The data collection unit can also collect log data and user data. The anomaly detection unit analyzes the data collected by the data collection unit to detect anomalies. For example, the generation AI analyzes data from the entire system and detects abnormal patterns. The generation AI can also use an anomaly detection algorithm to detect performance degradation. The warning unit issues a warning when the anomaly detection unit detects an anomaly. For example, the generation AI can automatically generate a warning message when it detects an anomaly and send it to an administrator via email or a messaging app. The generation AI can also set a warning level according to the severity of the anomaly and prompt the administrator to take appropriate action. This allows the system monitoring system according to an embodiment to efficiently monitor the system and reduce the administrator's burden. For example, when an anomaly is detected, the generation AI can identify the cause of the anomaly and propose measures to prevent recurrence. The generation AI can also identify the scope of the anomaly's impact and propose measures to minimize the impact. Furthermore, generative AI can analyze the user's emotional response when an anomaly is detected and provide appropriate countermeasures.

[0053] The data collection unit can scan handwritten answers and convert them into digital data. For example, the data collection unit may use a scanner to read handwritten answers and save them as image data. Then, the image data may be converted into text data using OCR technology. The data collection unit may also use a smartphone camera to take a photo of the handwritten answer and convert the image data into text data using a dedicated app. For example, the app may automatically correct the image and perform character recognition. The data collection unit may also use a dedicated digital pen to write handwritten answers, which then converts the data into digital data in real time. For example, a sensor may detect the movement of the pen and save it as text data. In this way, converting handwritten answers into digital data makes it easier for generative AI to analyze.

[0054] When detecting an anomaly, the anomaly detection unit can perform causal analysis to identify the cause of the anomaly. For example, when the generation AI detects an anomaly, the anomaly detection unit performs causal analysis to identify the cause. For example, it analyzes whether a sudden increase in CPU usage is caused by the operation of a specific application. The generation AI can also use Granger causality testing or Bayesian networks to identify the cause of the anomaly. By identifying the cause of the anomaly, measures can be taken to prevent recurrence.

[0055] When an abnormality is detected, the warning unit can evaluate the severity of the abnormality and set a warning level according to the level of urgency. For example, when the generation AI detects an abnormality, the warning unit can evaluate the severity of the abnormality and set a warning level according to the level of urgency. For example, it can issue a low-level warning for minor abnormalities and a high-level warning for serious abnormalities. The generation AI can also evaluate the severity based on the scope and degree of impact of the abnormality, and the urgency based on the need for a response and time constraints. This enables a prompt response by issuing an appropriate warning according to the severity of the abnormality.

[0056] When an anomaly is detected, the warning unit can identify the cause of the anomaly and propose measures to prevent recurrence. For example, when the generation AI detects an anomaly, the warning unit can identify the cause of the anomaly and propose measures to prevent recurrence. For example, if the cause is a bug in a specific application, it can suggest applying a patch to fix it. The generation AI can also identify the cause of the anomaly using log analysis and system diagnosis and propose system updates or process improvements. This improves system stability by identifying the cause of the anomaly and proposing measures to prevent recurrence.

[0057] The data collection unit can introduce multimodal data analysis, which includes audio and image data in the data it collects. For example, the data collection unit can include audio data in the data collected by the generation AI and perform audio analysis. For example, it can record system error messages as audio and analyze their content to detect abnormalities. The generation AI can also collect image data and detect abnormalities using image recognition technology. For example, it can analyze surveillance camera footage to detect abnormal behavior. The generation AI can also perform integrated analysis of audio and image data and perform correlation analysis of heterogeneous data. This improves the accuracy of anomaly detection by performing multimodal data analysis, which includes audio and image data.

[0058] The data collection unit can integrate system monitoring data from different industries and develop cross-industry anomaly detection algorithms. For example, the data collection unit can integrate system monitoring data from different industries and develop a common anomaly detection algorithm. For example, data from the financial industry and the manufacturing industry can be integrated to improve the accuracy of anomaly detection. In addition, the generative AI can share data between different industries and apply algorithms across different industries. This improves the accuracy of anomaly detection by integrating data from different industries.

[0059] The data collection unit can monitor the emotional state of the system administrator and automatically provide support when stress levels rise. The data collection unit, for example, uses an emotion estimation function to monitor the emotional state of the system administrator in real time. For example, it can analyze the administrator's facial expressions and voice to measure their stress level. The generation AI can also assess stress based on heart rate and self-reporting and provide appropriate support. For example, it can provide mental health support or technical support. This reduces the burden on the administrator by monitoring the emotional state of the system administrator and providing appropriate support when stress levels rise.

[0060] When an anomaly is detected, the anomaly detection unit can identify the scope of the anomaly's impact and propose measures to minimize the impact. For example, when the generation AI detects an anomaly, the anomaly detection unit can identify the scope of the anomaly's impact and propose measures to minimize the impact. For example, it can identify the scope of impact on a server where an anomaly has occurred and prevent the impact on other servers. The generation AI can also identify the affected system parts and the extent of the impact, and propose resetting the system or restoring a backup. This improves system stability by identifying the scope of impact of the anomaly and proposing measures to minimize the impact.

[0061] The data collection unit can analyze the user's emotional response when an anomaly is detected and propose measures to minimize the impact of the anomaly. The data collection unit, for example, uses an emotion estimation function to analyze the user's emotional response in real time when an anomaly is detected. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The generation AI can also propose measures to minimize the impact of the anomaly based on the user's emotional response. For example, it can provide countermeasures to reduce the user's stress level. This optimizes the user experience by analyzing the user's emotional response and proposing measures to minimize the impact of the anomaly.

[0062] When an abnormality is detected, the warning unit can customize the content of the warning message to provide the recipient with the most appropriate information. For example, when the generation AI issues a warning, the warning unit can customize the content of the warning message to provide the recipient with the most appropriate information. For example, it can switch between messages that include technical details and simple messages. The generation AI can also adjust the content of the warning message depending on the recipient's role and skill level. In this way, customizing the content of the warning message provides the recipient with the most appropriate information and encourages a quick response.

[0063] When an abnormality is detected, the warning unit can set the priority of the warning and encourage a response according to the importance. For example, when the generation AI issues a warning, the warning unit can set the priority of the warning and encourage a response according to the importance. For example, it can issue a low-priority warning for minor abnormalities and a high-priority warning for serious abnormalities. The generation AI can also set priorities based on the impact and urgency, and evaluate the impact on the entire system and the business. In this way, setting the priority of warnings makes it possible to respond quickly according to the importance.

[0064] The warning unit can send a warning message through multiple channels when it detects an abnormality. For example, when the generation AI issues a warning, the warning unit sends the warning message through multiple channels. For example, the warning can be issued via email, SMS, or app notification. The generation AI can also select the method of sending the warning message according to the recipient's preferences. This allows warnings to be communicated quickly and reliably by sending the warning message through multiple channels.

[0065] The data collection unit can monitor the general-purpose AI's operation logs and execution commands to detect abnormal behavior. For example, the generation AI monitors the general-purpose AI's operation logs to detect abnormal behavior. For example, it detects an abnormality when an unexpected command is executed. The generation AI can also monitor the general-purpose AI's execution commands to detect abnormal behavior. For example, it monitors command line inputs and API calls to detect abnormal behavior. In this way, by monitoring the general-purpose AI's operation logs and execution commands and detecting abnormal behavior, it is possible to prevent the AI ​​from running wild.

[0066] When the data collection unit detects abnormal behavior, it can identify the scope of the abnormal behavior's impact and propose measures to minimize the impact. For example, when the generation AI detects abnormal behavior, the data collection unit can identify the scope of the abnormal behavior's impact and propose measures to minimize the impact. For example, isolating the system component where the abnormal behavior occurred. The generation AI can also identify the affected system parts and the extent of the impact, and propose resetting the system or restoring a backup. This improves system stability by identifying the scope of the abnormal behavior's impact and proposing measures to minimize the impact.

[0067] When the data collection unit detects abnormal behavior, it can identify the cause of the abnormal behavior and propose measures to prevent recurrence. For example, when the generation AI detects abnormal behavior, the data collection unit can identify the cause of the abnormal behavior and propose measures to prevent recurrence. For example, if a specific command is the cause, it can restrict the use of that command. The generation AI can also identify the cause using log analysis and system diagnosis and propose system updates or process improvements. This improves system stability by identifying the cause of abnormal behavior and proposing measures to prevent recurrence.

[0068] When the data collection unit detects abnormal behavior, it can analyze the user's emotional response and provide appropriate countermeasures. For example, the data collection unit uses an emotion estimation function to evaluate the emotional impact that the general-purpose AI's abnormal behavior has on the user. For example, it analyzes whether the abnormal behavior increases the user's stress level. The generation AI can also provide appropriate countermeasures based on the user's emotional response. For example, it can suggest resetting the system or restoring a backup. This optimizes the user experience by analyzing the user's emotional response and providing appropriate countermeasures.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The system monitoring system can further include a predictive analysis unit. The predictive analysis unit predicts future abnormalities based on collected data. For example, it analyzes past data and predicts the possibility of an abnormality occurring if a specific pattern is repeated. The predictive analysis unit can also take into account performance fluctuations due to seasons and time of day to assess the risk of an abnormality occurring. This allows system administrators to take measures in advance and improve system stability.

[0071] A system monitoring system can also be equipped with a user interface unit. The user interface unit visually displays collected data and anomaly detection results. For example, it can display the real-time system status in dashboard format and provide detailed information in graphs and charts when an anomaly occurs. The user interface unit can also display tooltips and pop-ups that clearly explain the cause of the anomaly and possible countermeasures. This allows system administrators to respond to anomalies quickly and accurately.

[0072] The system monitoring system can also be equipped with an automatic repair unit. The automatic repair unit automatically performs repair work when an abnormality is detected. For example, if a specific service stops, it will automatically attempt to restart it. It can also isolate the component in which the abnormality occurred, minimizing the impact on the entire system. Furthermore, the automatic repair unit can change settings or apply patches to prevent the abnormality from recurring. This reduces system downtime and improves stability.

[0073] The system monitoring system may further include an energy management unit. The energy management unit monitors the energy consumption of the entire system and promotes efficient energy use. For example, it schedules non-critical tasks for off-peak hours to reduce energy consumption during peak hours. The energy management unit can also detect abnormal energy consumption and suggest measures to improve energy efficiency. This reduces the operating costs of the system and alleviates the environmental impact.

[0074] The system monitoring system can further include a security monitoring unit. The security monitoring unit monitors the security status of the entire system and detects abnormal access or attacks. For example, it monitors unauthorized access attempts and malware activity in real time and immediately issues an alert when an abnormality is detected. The security monitoring unit can also identify the cause of the abnormality and propose appropriate countermeasures. This strengthens the security of the system and ensures data protection.

[0075] The system monitoring system can further use emotion estimation capabilities to provide interactions based on the user's emotions. For example, if the user is feeling stressed, the system can simplify the interface and display only the necessary information. On the other hand, if the user is relaxed, the system can provide more detailed information and additional options. This allows the system to provide optimal interactions according to the user's emotional state and improve the user experience.

[0076] The system monitoring system can further use emotion estimation to analyze the user's emotional response when an anomaly is detected and provide appropriate countermeasures. For example, if the user feels anxious when an anomaly is detected, the system can provide a detailed explanation and support options. Alternatively, if the user remains calm, the system can send only a brief notification. This allows the system to provide appropriate countermeasures according to the user's emotional state and improve the efficiency of anomaly response.

[0077] The system monitoring system can also use emotion estimation functions to monitor the emotional state of system administrators and automatically provide support when stress levels rise. For example, it can analyze the administrator's facial expressions and voice to measure their stress levels. It can also assess stress levels based on heart rate and self-reporting and provide appropriate support, for example, by providing mental health support or technical support. This reduces the burden on administrators by monitoring their emotional state and providing appropriate support when stress levels rise.

[0078] The system monitoring system can further use its emotion estimation function to analyze the user's emotional response when an anomaly is detected and propose measures to minimize the impact of the anomaly. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. It can also propose measures to minimize the impact of the anomaly based on the user's emotional response. For example, it can provide countermeasures to reduce the user's stress level. This optimizes the user experience by analyzing the user's emotional response and proposing measures to minimize the impact of the anomaly.

[0079] The system monitoring system also uses an emotion estimation function to analyze the user's emotional response in real time when an anomaly is detected. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. It can also suggest measures to minimize the impact of the anomaly based on the user's emotional response. For example, it can provide countermeasures to reduce the user's stress level. This optimizes the user experience by analyzing the user's emotional response and suggesting measures to minimize the impact of the anomaly.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The data collection unit collects data from the entire system. For example, it collects data such as the server's CPU usage, memory usage, and network traffic. It can also collect log data and user data. Step 2: The anomaly detection unit analyzes the data collected by the data collection unit to detect anomalies. For example, the generative AI analyzes data from the entire system to detect abnormal patterns. Anomaly detection algorithms can also be used to detect performance degradation. Step 3: The warning unit issues a warning when an abnormality is detected by the anomaly detection unit. For example, when an abnormality is detected, the generation AI can automatically generate a warning message and send it to the administrator via email or messaging app. It can also set a warning level depending on the severity of the abnormality, prompting appropriate action.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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 data collection unit that collects data on the entire system; an anomaly detection unit that analyzes the data collected by the data collection unit and detects an anomaly; a warning unit that issues a warning when an abnormality is detected by the abnormality detection unit. A system characterized by:

2. The data collection unit Implement streaming data processing technology to process data collected from the system in real time.

2. The system of claim 1.

3. The abnormality detection unit When detecting the anomaly, a causal analysis is performed to identify the cause of the anomaly.

2. The system of claim 1.

4. The warning unit When the abnormality is detected, the severity of the abnormality is evaluated and a warning level is set according to the urgency.

2. The system of claim 1.

5. The data collection unit Introduce multimodal data analysis to collect data including audio and image data.

2. The system of claim 1.

6. The abnormality detection unit When detecting the anomaly, identify the extent of the anomaly's impact and propose measures to minimize the impact.

2. The system of claim 1.

7. The warning unit When the above abnormality is detected, the content of the warning message is customized to provide the recipient with the most appropriate information.

2. The system of claim 1.

8. The data collection unit Monitors the emotional state of system administrators and automatically provides support when stress levels rise 2. The system of claim 1.

Images