System

Abstract

An object of a system according to an embodiment is to efficiently perform detection of a system error, cause analysis, and countermeasure planning.SOLUTION: A system includes a collection unit, an analysis unit, a reference unit, and a proposal unit. The collection unit collects log data and performance data of the system. The analysis unit analyzes the data collected by the collection unit to detect an error. The reference unit specifies a cause of the error detected by the analysis unit. The proposal unit proposes an appropriate countermeasure based on the cause specified by the reference 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] With conventional technology, the process from detecting a system error to analyzing the cause and planning countermeasures required time and effort, making it difficult to carry out efficiently.

[0005] The system according to the embodiment aims to efficiently perform the process from detecting a system error to analyzing the cause and planning countermeasures. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a reference unit, and a proposal unit. The collection unit collects system log data and performance data. The analysis unit analyzes the data collected by the collection unit to detect errors. The reference unit identifies the cause of the error detected by the analysis unit. The proposal unit proposes appropriate measures based on the cause identified by the reference unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently detect system errors, analyze the causes, and develop countermeasures. [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 system according to an embodiment of the present invention automates the process from detecting system errors to analyzing their causes and developing temporary and permanent solutions. This system detects system errors, analyzes the causes of the detected errors, and develops temporary and permanent solutions based on the analysis results. Automating this process significantly reduces the workload of system personnel and improves error recovery speed. For example, the system collects system log data and performance data and uses AI to analyze them to detect errors. Next, the AI ​​identifies the cause of the error based on the collected data. Furthermore, the AI ​​proposes optimal solutions for the identified cause. This significantly reduces the workload of system personnel and improves error recovery speed. This system automates the process from detecting system errors to analyzing their causes and proposing solutions, thereby reducing workload and improving error recovery speed. For example, identifying the cause of an error and developing solutions, which previously took several hours, can now be completed in just a few minutes. This system also contributes to preventing error recurrence.

[0029] A system according to an embodiment includes a collection unit, an analysis unit, a reference unit, and a proposal unit. The collection unit collects system log data and performance data. For example, the collection unit can collect data such as system error logs, access logs, CPU usage, and memory usage. The collection unit can also adjust the timing of data collection using AI. The analysis unit analyzes the data collected by the collection unit to detect errors. For example, the analysis unit uses AI to analyze the collected data and detect errors by detecting specific error messages or abnormal performance indicators. The analysis unit can also dynamically adjust the error detection algorithm. The reference unit identifies the cause of the error detected by the analysis unit. For example, the reference unit uses AI to refer to past error data and similar cases to identify that a specific code section or configuration error is the cause. The reference unit can also prioritize cause identification based on the frequency and impact of the error. The proposal unit proposes optimal countermeasures based on the cause identified by the reference unit. For example, the proposal unit uses AI to propose countermeasures such as modifying settings, modifying code, or restarting the system. The proposing unit can also propose effective countermeasures by referring to past countermeasure data and best practices. As a result, the system according to the embodiment automates the process from detecting system errors to analyzing their causes and proposing countermeasures, thereby reducing the number of steps and improving the speed of error recovery.

[0030] The collection unit can dynamically change the type and amount of data to be collected based on the operating status of the system. For example, when the system is under high load, the collection unit collects only important log data and suspends the collection of performance data. In addition, when the system is under low load, the collection unit can also collect detailed log data and performance data. The collection unit can also dynamically change the type of data to be collected based on the operating status of the system and efficiently collect necessary information. This makes it possible to optimize data collection according to the operating status of the system and achieve efficient error detection.

[0031] The collection unit can start data collection based on a specific event or trigger. For example, the collection unit can immediately start collecting log data and performance data when a system error occurs. The collection unit can also collect related data when a specific user action is triggered. The collection unit can also automatically start data collection when it detects abnormal system operation. This makes it possible to start data collection based on a specific event or trigger, thereby achieving rapid error detection.

[0032] The collection unit can evaluate the reliability of the collected data and exclude unreliable data. For example, the collection unit checks the consistency of the collected data and filters out abnormal data. The collection unit can also evaluate the reliability of the data and exclude unreliable data. The collection unit can also use only important data for analysis based on the reliability of the data. In this way, by evaluating the reliability of the data and excluding unreliable data, the accuracy of the analysis can be improved.

[0033] The collection unit can collect data based on the geographical location of the system. For example, if the system is distributed across multiple geographical locations, the collection unit collects data from each location. The collection unit can also prioritize collection of important data based on the geographical location. The collection unit can also adjust the timing of data collection, taking the geographical location into consideration. This allows data to be collected taking the geographical location of the system into consideration, thereby improving the accuracy of error detection.

[0034] The collection unit can acquire communication data between different modules of the system. For example, the collection unit collects communication data between different modules of the system and identifies the cause of an error. The collection unit can also analyze the communication data and detect abnormal communication patterns. The collection unit can also evaluate the overall state of the system based on the communication data between modules. This makes it possible to collect communication data between different modules of the system and identify the cause of an error.

[0035] The collection unit can acquire external environment data of the system. For example, the collection unit can collect network status and identify the cause of a system error. The collection unit can also collect power supply status and evaluate system stability. The collection unit can also optimize system performance based on the external environment data. In this way, the external environment data of the system can be collected and the cause of an error can be identified.

[0036] The analysis unit can dynamically adjust the anomaly detection threshold. The analysis unit dynamically adjusts the anomaly detection threshold, for example, depending on the operating status of the system. The analysis unit can also set an optimal anomaly detection threshold based on past data. The analysis unit can also adjust the threshold in real time to improve the accuracy of anomaly detection. This makes it possible to dynamically adjust the anomaly detection threshold and improve the accuracy of error detection.

[0037] The analysis unit can identify abnormality patterns by referring to the system's operation history. For example, the analysis unit analyzes the system's operation history to identify abnormality patterns. The analysis unit can also identify current abnormalities based on past abnormality data. The analysis unit can also identify the cause of an abnormality by referring to the operation history. This makes it possible to identify abnormality patterns by referring to the system's operation history and quickly identify the cause of an error.

[0038] The analysis unit can assign priorities based on the frequency of anomalies and their impact. The analysis unit assigns priorities based on, for example, the frequency of anomalies. The analysis unit can also evaluate the impact of anomalies and assign priorities. The analysis unit can also comprehensively evaluate the frequency of anomalies and their impact and set optimal priorities. This allows prioritization based on the frequency of anomalies and their impact, making it possible to handle important errors with priority.

[0039] The analysis unit can detect errors by taking into account the interactions between different modules of the system. For example, the analysis unit analyzes communication data between different modules of the system and detects errors. The analysis unit can also identify the cause of an error by taking into account the interactions between modules. The analysis unit can also integrate data between different modules and identify patterns in which errors occur. This makes it possible to detect errors and identify the cause of errors by taking into account the interactions between different modules of the system.

[0040] The analysis unit can identify the cause of the error by referring to the external environment data of the system. The analysis unit can identify the cause of the error by referring to the network status, for example. The analysis unit can also identify the cause of the system error by referring to the power supply status. The analysis unit can also identify the cause of the error based on the external environment data. This makes it possible to identify the cause of the error by referring to the external environment data of the system and achieve rapid error recovery.

[0041] The analysis unit can integrate and analyze the performance data and log data of the system. For example, the analysis unit integrates the performance data and log data to identify the cause of an error. The analysis unit can also identify the pattern of abnormality occurrence based on the integrated data. The analysis unit can also comprehensively analyze the performance data and log data to evaluate the system status. This makes it possible to integrate and analyze the performance data and log data of the system and identify the cause of an error.

[0042] The reference unit can evaluate the reliability of past error data and exclude unreliable data. The reference unit, for example, checks the consistency of past error data and excludes unreliable data. The reference unit can also evaluate the reliability of error data and refer to only important data. The reference unit can also filter unreliable data and accurately identify the cause. In this way, by evaluating the reliability of past error data and excluding unreliable data, accurate cause identification can be achieved.

[0043] The reference unit can prioritize cause identification based on the frequency of error occurrence and the degree of impact. The reference unit prioritizes cause identification based on, for example, the frequency of error occurrence. The reference unit can also evaluate the degree of impact of errors and assign priorities. The reference unit can also comprehensively evaluate the frequency of occurrence and the degree of impact and set optimal priorities. This makes it possible to prioritize cause identification based on the frequency of error occurrence and the degree of impact, and to handle important errors preferentially.

[0044] The reference unit can identify similar errors by referring to the system's operation history. For example, the reference unit analyzes the system's operation history to identify similar errors. The reference unit can also identify cases similar to the current error based on past error data. The reference unit can also identify the cause of the error by referring to the operation history. This makes it possible to identify similar errors by referring to the system's operation history, thereby enabling rapid cause identification.

[0045] The reference unit can identify the cause by taking into account the interactions between different modules of the system. For example, the reference unit analyzes communication data between different modules of the system to identify the cause of the error. The reference unit can also identify the cause of the error by taking into account the interactions between the modules. The reference unit can also integrate data between different modules to identify error occurrence patterns. This makes it possible to identify the cause by taking into account the interactions between different modules of the system, and quickly identify the cause of the error.

[0046] The reference unit can identify the cause by referencing the external environment data of the system. The reference unit can identify the cause of the error by referencing, for example, the network status. The reference unit can also identify the cause of a system error by referencing the power supply status. The reference unit can also identify the cause of the error based on the external environment data. This makes it possible to identify the cause by referencing the external environment data of the system and achieve rapid error recovery.

[0047] The reference unit can integrate the performance data and log data of the system to identify the cause. For example, the reference unit integrates the performance data and log data to identify the cause of an error. The reference unit can also identify the pattern of abnormality occurrence based on the integrated data. The reference unit can also comprehensively analyze the performance data and log data to evaluate the system status. This makes it possible to integrate the performance data and log data of the system to identify the cause and quickly identify the cause of an error.

[0048] The suggestion unit can evaluate the reliability of past countermeasure data and exclude unreliable data. For example, the suggestion unit checks the consistency of past countermeasure data and excludes unreliable data. The suggestion unit can also evaluate the reliability of countermeasure data and refer to only important data. The suggestion unit can also filter unreliable data and propose accurate countermeasures. As a result, accurate countermeasure proposals can be realized by evaluating the reliability of past countermeasure data and excluding unreliable data.

[0049] The proposal unit can prioritize countermeasures based on the frequency of error occurrence and the degree of impact. The proposal unit prioritizes countermeasures based on, for example, the frequency of error occurrence. The proposal unit can also evaluate the degree of impact of errors and prioritize them. The proposal unit can also comprehensively evaluate the frequency of occurrence and the degree of impact and set optimal priorities. This makes it possible to prioritize countermeasures based on the frequency of error occurrence and the degree of impact, and to prioritize the most important countermeasures.

[0050] The proposal unit can propose optimal countermeasures by referring to the system's operation history. The proposal unit, for example, analyzes the system's operation history and proposes optimal countermeasures. The proposal unit can also propose optimal countermeasures for current errors based on past countermeasure data. The proposal unit can also propose countermeasures to prevent recurrence of errors by referring to the operation history. In this way, optimal countermeasures can be proposed by referring to the system's operation history, and error recurrence can be prevented.

[0051] The proposal unit can propose countermeasures taking into consideration the interactions between different modules of the system. For example, the proposal unit analyzes communication data between different modules of the system and proposes optimal countermeasures. The proposal unit can also evaluate the impact of countermeasures taking into consideration the interactions between modules. The proposal unit can also integrate data between different modules and propose optimal countermeasures. This makes it possible to propose countermeasures taking into consideration the interactions between different modules of the system and to implement optimal countermeasures.

[0052] The proposal unit can propose measures by referring to the external environment data of the system. The proposal unit can propose optimal measures by referring to the network status, for example. The proposal unit can also propose measures to improve system stability by referring to the power supply status. The proposal unit can also propose measures to optimize system performance based on the external environment data. In this way, measures can be proposed by referring to the external environment data of the system, and system stability can be improved.

[0053] The proposal unit can integrate the performance data and log data of the system to propose countermeasures. For example, the proposal unit integrates the performance data and log data to propose optimal countermeasures. The proposal unit can also identify abnormality occurrence patterns based on the integrated data and propose countermeasures. The proposal unit can also comprehensively analyze the performance data and log data, evaluate the system status, and propose countermeasures. This makes it possible to integrate the performance data and log data of the system to propose countermeasures and implement optimal countermeasures.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The system can further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit can analyze the user's operation patterns and frequency of use to identify the times and situations in which errors are likely to occur. For example, if errors are likely to occur during times when specific operations are frequently performed, monitoring can be focused on those times. Also, if a specific operation sequence causes an error, the system can detect that sequence and issue a warning in advance. Furthermore, the behavior analysis unit can propose measures to prevent the recurrence of errors based on the user's operation history. This makes it possible to detect errors and propose countermeasures that take into account the user's behavior patterns, thereby improving the reliability of the system.

[0056] The system can further include a prediction unit that predicts the occurrence of errors. The prediction unit can predict future error occurrences based on past error data and the system's operating status. For example, if errors tend to occur when a specific performance indicator exceeds a certain threshold, a warning can be issued when that threshold is approached. Also, if errors are more likely to occur during certain times of the day or on certain days of the week, monitoring can be focused on those times or days of the week. Furthermore, the prediction unit can evaluate the risk of error occurrence and take measures in advance if the risk is high. This makes it possible to prevent errors and improve system stability.

[0057] The system can further include an impact range identification unit that identifies the impact range of an error. When an error occurs, the impact range identification unit can evaluate the impact of the error on the entire system. For example, if an error occurs in a specific module, it can evaluate the impact on other modules that depend on that module. It can also evaluate the degradation of the overall system performance when an error occurs. Furthermore, the impact range identification unit can visualize the impact range of the error and provide it to the system administrator. This allows the impact range of the error to be quickly identified and appropriate measures to be taken.

[0058] The system can further include a recurrence prevention proposal unit that proposes measures to prevent the recurrence of errors. The recurrence prevention proposal unit can propose measures to prevent the recurrence of errors based on past error data and countermeasure data. For example, if a specific error occurs frequently, it can identify the root cause of the error and propose permanent countermeasures. It can also propose countermeasures that have been effective in the past. Furthermore, the recurrence prevention proposal unit can analyze error occurrence patterns and optimize recurrence prevention measures. This makes it possible to prevent errors from recurring and improve system reliability.

[0059] The system can further include an impact minimization proposal unit that proposes measures to minimize the impact of an error. The impact minimization proposal unit can propose measures to minimize the impact of an error when it occurs. For example, if an error occurs in a specific module, that module can be temporarily stopped to prevent its impact on other modules. It can also propose measures to minimize the degradation of overall system performance when an error occurs. Furthermore, the impact minimization proposal unit can evaluate the extent of the impact of the error and propose the optimal measure. This makes it possible to minimize the impact of an error and improve system stability.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects system log data and performance data. For example, the collection unit can collect data such as system error logs, access logs, CPU usage, and memory usage. The collection unit can also use AI to adjust the timing of data collection. Step 2: The analysis unit analyzes the data collected by the collection unit to detect errors. For example, the analysis unit may use AI to analyze the collected data and detect errors by detecting specific error messages or abnormal performance indicators. The analysis unit may also dynamically adjust its error detection algorithms. Step 3: The reference section identifies the cause of the error detected by the analysis section. For example, the reference section uses AI to refer to past error data and similar cases to identify that a specific code section or misconfiguration is the cause. The reference section can also prioritize cause identification based on the frequency and impact of the error. Step 4: The proposal section proposes optimal countermeasures based on the causes identified by the reference section. For example, the proposal section uses AI to suggest countermeasures such as modifying settings, modifying code, or restarting the system. The proposal section can also suggest effective countermeasures by referencing past countermeasure data and best practices.

[0062] (Example 2) A system according to an embodiment of the present invention automates the process from detecting system errors to analyzing their causes and developing temporary and permanent solutions. This system detects system errors, analyzes the causes of the detected errors, and develops temporary and permanent solutions based on the analysis results. Automating this process significantly reduces the workload of system personnel and improves error recovery speed. For example, the system collects system log data and performance data and uses AI to analyze them to detect errors. Next, the AI ​​identifies the cause of the error based on the collected data. Furthermore, the AI ​​proposes optimal solutions for the identified cause. This significantly reduces the workload of system personnel and improves error recovery speed. This system automates the process from detecting system errors to analyzing their causes and proposing solutions, thereby reducing workload and improving error recovery speed. For example, identifying the cause of an error and developing solutions, which previously took several hours, can now be completed in just a few minutes. This system also contributes to preventing error recurrence.

[0063] A system according to an embodiment includes a collection unit, an analysis unit, a reference unit, and a proposal unit. The collection unit collects system log data and performance data. For example, the collection unit can collect data such as system error logs, access logs, CPU usage, and memory usage. The collection unit can also adjust the timing of data collection using AI. The analysis unit analyzes the data collected by the collection unit to detect errors. For example, the analysis unit uses AI to analyze the collected data and detect errors by detecting specific error messages or abnormal performance indicators. The analysis unit can also dynamically adjust the error detection algorithm. The reference unit identifies the cause of the error detected by the analysis unit. For example, the reference unit uses AI to refer to past error data and similar cases to identify that a specific code section or configuration error is the cause. The reference unit can also prioritize cause identification based on the frequency and impact of the error. The proposal unit proposes optimal countermeasures based on the cause identified by the reference unit. For example, the proposal unit uses AI to propose countermeasures such as modifying settings, modifying code, or restarting the system. The proposing unit can also propose effective countermeasures by referring to past countermeasure data and best practices. As a result, the system according to the embodiment automates the process from detecting system errors to analyzing their causes and proposing countermeasures, thereby reducing the number of steps and improving the speed of error recovery.

[0064] The collection unit can analyze the user's emotions and adjust the timing of collecting log data and performance data based on the analyzed user's emotions. For example, if the user is feeling stressed, the collection unit can increase the collection timing to aim for early error detection. Furthermore, if the user is relaxed, the collection unit can set the collection timing to normal to reduce the load on the system. Furthermore, if the user is in a hurry, the collection unit can shorten the collection timing to detect errors quickly. This allows the data collection timing to be adjusted according to the user's emotions, improving the accuracy of error detection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0065] The collection unit can dynamically change the type and amount of data to be collected based on the operating status of the system. For example, when the system is under high load, the collection unit collects only important log data and suspends the collection of performance data. In addition, when the system is under low load, the collection unit can also collect detailed log data and performance data. The collection unit can also dynamically change the type of data to be collected based on the operating status of the system and efficiently collect necessary information. This makes it possible to optimize data collection according to the operating status of the system and achieve efficient error detection.

[0066] The collection unit can start data collection based on a specific event or trigger. For example, the collection unit can immediately start collecting log data and performance data when a system error occurs. The collection unit can also collect related data when a specific user action is triggered. The collection unit can also automatically start data collection when it detects abnormal system operation. This makes it possible to start data collection based on a specific event or trigger, thereby achieving rapid error detection.

[0067] The collection unit can evaluate the reliability of the collected data and exclude unreliable data. For example, the collection unit checks the consistency of the collected data and filters out abnormal data. The collection unit can also evaluate the reliability of the data and exclude unreliable data. The collection unit can also use only important data for analysis based on the reliability of the data. In this way, by evaluating the reliability of the data and excluding unreliable data, the accuracy of the analysis can be improved.

[0068] The collection unit can analyze the user's emotions and determine the priority of data to be collected based on the analyzed user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting important log data. The collection unit can also perform normal data collection if the user is relaxed. The collection unit can also prioritize collecting error-related data if the user is in a hurry. This allows data to be prioritized according to the user's emotions and important data to be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0069] The collection unit can collect data based on the geographical location of the system. For example, if the system is distributed across multiple geographical locations, the collection unit collects data from each location. The collection unit can also prioritize collection of important data based on the geographical location. The collection unit can also adjust the timing of data collection, taking the geographical location into consideration. This allows data to be collected taking the geographical location of the system into consideration, thereby improving the accuracy of error detection.

[0070] The collection unit can acquire communication data between different modules of the system. For example, the collection unit collects communication data between different modules of the system and identifies the cause of an error. The collection unit can also analyze the communication data and detect abnormal communication patterns. The collection unit can also evaluate the overall state of the system based on the communication data between modules. This makes it possible to collect communication data between different modules of the system and identify the cause of an error.

[0071] The collection unit can acquire external environment data of the system. For example, the collection unit can collect network status and identify the cause of a system error. The collection unit can also collect power supply status and evaluate system stability. The collection unit can also optimize system performance based on the external environment data. In this way, the external environment data of the system can be collected and the cause of an error can be identified.

[0072] The analysis unit can analyze the user's emotions and adjust the error detection algorithm based on the analyzed user emotions. For example, if the user is feeling stressed, the analysis unit can set a low error detection threshold to detect errors early. The analysis unit can also perform error detection with a normal threshold if the user is relaxed. The analysis unit can also speed up the error detection algorithm if the user is in a hurry. This allows the error detection algorithm to be adjusted according to the user's emotions, improving the accuracy of error detection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The analysis unit can dynamically adjust the anomaly detection threshold. The analysis unit dynamically adjusts the anomaly detection threshold, for example, depending on the operating status of the system. The analysis unit can also set an optimal anomaly detection threshold based on past data. The analysis unit can also adjust the threshold in real time to improve the accuracy of anomaly detection. This makes it possible to dynamically adjust the anomaly detection threshold and improve the accuracy of error detection.

[0074] The analysis unit can identify abnormality patterns by referring to the system's operation history. For example, the analysis unit analyzes the system's operation history to identify abnormality patterns. The analysis unit can also identify current abnormalities based on past abnormality data. The analysis unit can also identify the cause of an abnormality by referring to the operation history. This makes it possible to identify abnormality patterns by referring to the system's operation history and quickly identify the cause of an error.

[0075] The analysis unit can assign priorities based on the frequency of anomalies and their impact. The analysis unit assigns priorities based on, for example, the frequency of anomalies. The analysis unit can also evaluate the impact of anomalies and assign priorities. The analysis unit can also comprehensively evaluate the frequency of anomalies and their impact and set optimal priorities. This allows prioritization based on the frequency of anomalies and their impact, making it possible to handle important errors with priority.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the error detection results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This allows the display method of the error detection results to be adjusted according to the user's emotions and improves visibility. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] The analysis unit can detect errors by taking into account the interactions between different modules of the system. For example, the analysis unit analyzes communication data between different modules of the system and detects errors. The analysis unit can also identify the cause of an error by taking into account the interactions between modules. The analysis unit can also integrate data between different modules and identify patterns in which errors occur. This makes it possible to detect errors and identify the cause of errors by taking into account the interactions between different modules of the system.

[0078] The analysis unit can identify the cause of the error by referring to the external environment data of the system. The analysis unit can identify the cause of the error by referring to the network status, for example. The analysis unit can also identify the cause of the system error by referring to the power supply status. The analysis unit can also identify the cause of the error based on the external environment data. This makes it possible to identify the cause of the error by referring to the external environment data of the system and achieve rapid error recovery.

[0079] The analysis unit can integrate and analyze the performance data and log data of the system. For example, the analysis unit integrates the performance data and log data to identify the cause of an error. The analysis unit can also identify the pattern of abnormality occurrence based on the integrated data. The analysis unit can also comprehensively analyze the performance data and log data to evaluate the system status. This makes it possible to integrate and analyze the performance data and log data of the system and identify the cause of an error.

[0080] The reference unit can estimate the user's emotions and adjust the algorithm for identifying the cause of the error based on the estimated user emotions. For example, if the user is feeling stressed, the reference unit can quickly execute the algorithm for identifying the cause of the error. Furthermore, if the user is relaxed, the reference unit can perform a detailed analysis and accurately identify the cause. Furthermore, if the user is in a hurry, the reference unit can speed up the algorithm for identifying the cause of the error. This allows the algorithm for identifying the cause of the error to be adjusted according to the user's emotions, thereby achieving rapid and accurate cause identification. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] The reference unit can evaluate the reliability of past error data and exclude unreliable data. The reference unit, for example, checks the consistency of past error data and excludes unreliable data. The reference unit can also evaluate the reliability of error data and refer to only important data. The reference unit can also filter unreliable data and accurately identify the cause. In this way, by evaluating the reliability of past error data and excluding unreliable data, accurate cause identification can be achieved.

[0082] The reference unit can prioritize cause identification based on the frequency of error occurrence and the degree of impact. The reference unit prioritizes cause identification based on, for example, the frequency of error occurrence. The reference unit can also evaluate the degree of impact of errors and assign priorities. The reference unit can also comprehensively evaluate the frequency of occurrence and the degree of impact and set optimal priorities. This makes it possible to prioritize cause identification based on the frequency of error occurrence and the degree of impact, and to handle important errors preferentially.

[0083] The reference unit can identify similar errors by referring to the system's operation history. For example, the reference unit analyzes the system's operation history to identify similar errors. The reference unit can also identify cases similar to the current error based on past error data. The reference unit can also identify the cause of the error by referring to the operation history. This makes it possible to identify similar errors by referring to the system's operation history, thereby enabling rapid cause identification.

[0084] The reference unit can estimate the user's emotions and adjust the display method of the error cause identification results based on the estimated user emotions. For example, if the user is nervous, the reference unit can provide a simple, highly visible display method. If the user is relaxed, the reference unit can also provide a display method that includes detailed information. If the user is in a hurry, the reference unit can also provide a display method that focuses on the main points. This allows the display method of the error cause identification results to be adjusted according to the user's emotions, improving visibility. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The reference unit can identify the cause by taking into account the interactions between different modules of the system. For example, the reference unit analyzes communication data between different modules of the system to identify the cause of the error. The reference unit can also identify the cause of the error by taking into account the interactions between the modules. The reference unit can also integrate data between different modules to identify error occurrence patterns. This makes it possible to identify the cause by taking into account the interactions between different modules of the system, and quickly identify the cause of the error.

[0086] The reference unit can identify the cause by referencing the external environment data of the system. The reference unit can identify the cause of the error by referencing, for example, the network status. The reference unit can also identify the cause of a system error by referencing the power supply status. The reference unit can also identify the cause of the error based on the external environment data. This makes it possible to identify the cause by referencing the external environment data of the system and achieve rapid error recovery.

[0087] The reference unit can integrate the performance data and log data of the system to identify the cause. For example, the reference unit integrates the performance data and log data to identify the cause of an error. The reference unit can also identify the pattern of abnormality occurrence based on the integrated data. The reference unit can also comprehensively analyze the performance data and log data to evaluate the system status. This makes it possible to integrate the performance data and log data of the system to identify the cause and quickly identify the cause of an error.

[0088] The suggestion unit can estimate the user's emotions and adjust the way in which the proposed countermeasures are presented based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible proposed countermeasures. If the user is relaxed, the suggestion unit can also provide detailed proposed countermeasures. If the user is in a hurry, the suggestion unit can also provide a proposed countermeasure that focuses on the main points. This allows the way in which the proposed countermeasures are presented to be adjusted according to the user's emotions, improving visibility. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The suggestion unit can evaluate the reliability of past countermeasure data and exclude unreliable data. For example, the suggestion unit checks the consistency of past countermeasure data and excludes unreliable data. The suggestion unit can also evaluate the reliability of countermeasure data and refer to only important data. The suggestion unit can also filter unreliable data and propose accurate countermeasures. As a result, accurate countermeasure proposals can be realized by evaluating the reliability of past countermeasure data and excluding unreliable data.

[0090] The proposal unit can prioritize countermeasures based on the frequency of error occurrence and the degree of impact. The proposal unit prioritizes countermeasures based on, for example, the frequency of error occurrence. The proposal unit can also evaluate the degree of impact of errors and prioritize them. The proposal unit can also comprehensively evaluate the frequency of occurrence and the degree of impact and set optimal priorities. This makes it possible to prioritize countermeasures based on the frequency of error occurrence and the degree of impact, and to prioritize the most important countermeasures.

[0091] The proposal unit can propose optimal countermeasures by referring to the system's operation history. The proposal unit, for example, analyzes the system's operation history and proposes optimal countermeasures. The proposal unit can also propose optimal countermeasures for current errors based on past countermeasure data. The proposal unit can also propose countermeasures to prevent recurrence of errors by referring to the operation history. In this way, optimal countermeasures can be proposed by referring to the system's operation history, and error recurrence can be prevented.

[0092] The suggestion unit can estimate the user's emotions and determine the priority of proposed countermeasures based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggesting important countermeasures. Furthermore, if the user is relaxed, the suggestion unit can also prioritize suggesting normal countermeasures. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggesting countermeasures that can be implemented quickly. This allows the priority of proposed countermeasures to be determined according to the user's emotions, and important countermeasures to be implemented preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] The proposal unit can propose countermeasures taking into consideration the interactions between different modules of the system. For example, the proposal unit analyzes communication data between different modules of the system and proposes optimal countermeasures. The proposal unit can also evaluate the impact of countermeasures taking into consideration the interactions between modules. The proposal unit can also integrate data between different modules and propose optimal countermeasures. This makes it possible to propose countermeasures taking into consideration the interactions between different modules of the system and to implement optimal countermeasures.

[0094] The proposal unit can propose measures by referring to the external environment data of the system. The proposal unit can propose optimal measures by referring to the network status, for example. The proposal unit can also propose measures to improve system stability by referring to the power supply status. The proposal unit can also propose measures to optimize system performance based on the external environment data. In this way, measures can be proposed by referring to the external environment data of the system, and system stability can be improved.

[0095] The proposal unit can integrate the performance data and log data of the system to propose countermeasures. For example, the proposal unit integrates the performance data and log data to propose optimal countermeasures. The proposal unit can also identify abnormality occurrence patterns based on the integrated data and propose countermeasures. The proposal unit can also comprehensively analyze the performance data and log data, evaluate the system status, and propose countermeasures. This makes it possible to integrate the performance data and log data of the system to propose countermeasures and implement optimal countermeasures. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, reference unit, and suggestion unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect system log data and performance data using the camera 42 and microphone 38B of the smart device 14. For example, the collection unit can be implemented by the specific processing unit 290 of the data processing device 12 and can collect data such as system error logs, access logs, CPU usage, and memory usage. For example, the analysis unit can be implemented by the control unit 46A of the smart device 14 and can analyze the collected data to detect errors. For example, the analysis unit can be implemented by the specific processing unit 290 of the data processing device 12 and can detect errors by analyzing the collected data and detecting specific error messages or abnormal performance indicators. For example, the reference unit can be implemented by the control unit 46A of the smart device 14 and can identify the cause of an error detected by the analysis unit. For example, the reference unit can be implemented by the specific processing unit 290 of the data processing device 12 and can refer to past error data or similar cases to identify that a specific code section or misconfiguration is the cause. For example, the suggestion unit may be realized by the control unit 46A of the smart device 14 and may propose optimal measures based on the cause identified by the reference unit. For example, the suggestion unit may be realized by the specific processing unit 290 of the data processing device 12 and may propose measures such as modifying settings, modifying code, or rebooting the system. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, reference unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect system log data and performance data using the camera 42 and microphone 238 of the smart glasses 214. For example, the collection unit can be realized by the specific processing unit 290 of the data processing device 12 and can collect data such as system error logs, access logs, CPU usage, and memory usage. For example, the analysis unit can be realized by the control unit 46A of the smart glasses 214 and can analyze the collected data to detect errors. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and can analyze the collected data to detect specific error messages or abnormal performance indicators to detect errors. For example, the reference unit can be realized by the control unit 46A of the smart glasses 214 and can identify the cause of the error detected by the analysis unit. For example, the reference unit may be realized by the specific processing unit 290 of the data processing device 12, and may refer to past error data or similar cases to identify that a specific code section or misconfiguration is the cause. For example, the suggestion unit may be realized by the control unit 46A of the smart glasses 214, and may propose optimal measures based on the cause identified by the reference unit. For example, the suggestion unit may be realized by the specific processing unit 290 of the data processing device 12, and may propose measures such as modifying settings, modifying code, or restarting the system. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, reference unit, and suggestion unit, described above, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect system log data and performance data using the camera 42 and microphone 238 of the headset type terminal 314. For example, the collection unit can be realized by the specific processing unit 290 of the data processing device 12 and can collect data such as system error logs, access logs, CPU usage rates, and memory usage rates. For example, the analysis unit can be realized by the control unit 46A of the headset type terminal 314 and can analyze the collected data to detect errors. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and can analyze the collected data to detect specific error messages or abnormal performance indicators to detect errors. For example, the reference unit can be realized by the control unit 46A of the headset type terminal 314 and can identify the cause of an error detected by the analysis unit. For example, the reference unit may be realized by the specific processing unit 290 of the data processing device 12, and may refer to past error data or similar cases to identify that a specific code section or setting error is the cause. For example, the suggestion unit may be realized by the control unit 46A of the headset type terminal 314, and may propose optimal measures based on the cause identified by the reference unit. For example, the suggestion unit may be realized by the specific processing unit 290 of the data processing device 12, and may propose measures such as modifying settings, modifying code, or restarting the system. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, reference unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect system log data and performance data using the camera 42 and microphone 238 of the robot 414. For example, the collection unit can be realized by the specific processing unit 290 of the data processing device 12 and can collect data such as system error logs, access logs, CPU usage rates, and memory usage rates. For example, the analysis unit can be realized by the control unit 46A of the robot 414 and can detect errors by analyzing the collected data. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and can detect errors by analyzing the collected data and detecting specific error messages or abnormal performance indicators. For example, the reference unit can be realized by the control unit 46A of the robot 414 and can identify the cause of an error detected by the analysis unit. For example, the reference unit can be realized by the specific processing unit 290 of the data processing device 12 and can identify that a specific code section or setting error is the cause by referring to past error data or similar cases. For example, the suggestion unit may be realized by the control unit 46A of the robot 414 and may propose optimal measures based on the cause identified by the reference unit. For example, the suggestion unit may be realized by the specific processing unit 290 of the data processing device 12 and may propose measures such as modifying settings, modifying code, or rebooting the system.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The system can further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit can analyze the user's operation patterns and frequency of use to identify the times and situations in which errors are likely to occur. For example, if errors are likely to occur during times when specific operations are frequently performed, monitoring can be focused on those times. Also, if a specific operation sequence causes an error, the system can detect that sequence and issue a warning in advance. Furthermore, the behavior analysis unit can propose measures to prevent the recurrence of errors based on the user's operation history. This makes it possible to detect errors and propose countermeasures that take into account the user's behavior patterns, thereby improving the reliability of the system.

[0098] The system may further include an impact assessment unit that estimates the user's emotions and assesses the impact of an error based on the estimated emotions. For example, if the user is feeling stressed, the impact assessment unit may assess the impact of the error highly and suggest a quick solution. Alternatively, if the user is relaxed, the impact assessment unit may perform a normal impact assessment. Furthermore, if the user is in a hurry, the impact of the error may be assessed quickly and errors requiring immediate attention may be prioritized. This allows for impact assessment based on the user's emotions, enabling appropriate solutions to be implemented quickly.

[0099] The system can further include a prediction unit that predicts the occurrence of errors. The prediction unit can predict future error occurrences based on past error data and the system's operating status. For example, if errors tend to occur when a specific performance indicator exceeds a certain threshold, a warning can be issued when that threshold is approached. Also, if errors are more likely to occur during certain times of the day or on certain days of the week, monitoring can be focused on those times or days of the week. Furthermore, the prediction unit can evaluate the risk of error occurrence and take measures in advance if the risk is high. This makes it possible to prevent errors and improve system stability.

[0100] The system may further include a notification adjustment unit that estimates the user's emotions and adjusts the error notification method based on the estimated emotions. For example, if the user is nervous, the notification adjustment unit may provide a simple, highly visible notification. Alternatively, if the user is relaxed, the notification adjustment unit may provide a notification with detailed information. Furthermore, if the user is in a hurry, the notification adjustment unit may provide a concise notification that focuses on the main points. This makes it possible to provide an appropriate notification method according to the user's emotions and improve the efficiency of error response.

[0101] The system can further include an impact range identification unit that identifies the impact range of an error. When an error occurs, the impact range identification unit can evaluate the impact of the error on the entire system. For example, if an error occurs in a specific module, it can evaluate the impact on other modules that depend on that module. It can also evaluate the degradation of the overall system performance when an error occurs. Furthermore, the impact range identification unit can visualize the impact range of the error and provide it to the system administrator. This allows the impact range of the error to be quickly identified and appropriate measures to be taken.

[0102] The system may further include a priority determination unit that estimates the user's emotions and determines the priority of errors based on the estimated emotions. For example, the priority determination unit may prioritize processing important errors when the user is stressed. Alternatively, the priority determination unit may process errors with normal priority when the user is relaxed. Furthermore, if the user is in a hurry, the priority determination unit may prioritize errors that require a quick response. This makes it possible to determine the priority of errors according to the user's emotions, and to quickly process important errors.

[0103] The system can further include a recurrence prevention proposal unit that proposes measures to prevent the recurrence of errors. The recurrence prevention proposal unit can propose measures to prevent the recurrence of errors based on past error data and countermeasure data. For example, if a specific error occurs frequently, it can identify the root cause of the error and propose permanent countermeasures. It can also propose countermeasures that have been effective in the past. Furthermore, the recurrence prevention proposal unit can analyze error occurrence patterns and optimize recurrence prevention measures. This makes it possible to prevent errors from recurring and improve system reliability.

[0104] The system may further include an accuracy adjustment unit that estimates the user's emotions and adjusts the accuracy of identifying the cause of the error based on the estimated emotions. For example, the accuracy adjustment unit increases the accuracy to quickly identify the cause when the user is stressed. Alternatively, the accuracy adjustment unit may perform cause identification with normal accuracy when the user is relaxed. Furthermore, the accuracy adjustment unit may adjust the accuracy to quickly identify the cause when the user is in a hurry. This makes it possible to adjust the accuracy of cause identification according to the user's emotions, thereby achieving fast and accurate cause identification.

[0105] The system can further include an impact minimization proposal unit that proposes measures to minimize the impact of an error. The impact minimization proposal unit can propose measures to minimize the impact of an error when it occurs. For example, if an error occurs in a specific module, that module can be temporarily stopped to prevent its impact on other modules. It can also propose measures to minimize the degradation of overall system performance when an error occurs. Furthermore, the impact minimization proposal unit can evaluate the extent of the impact of the error and propose the optimal measure. This makes it possible to minimize the impact of an error and improve system stability.

[0106] The system may further include a notification frequency adjustment unit that estimates the user's emotions and adjusts the frequency of error notifications based on the estimated emotions. For example, if the user is feeling stressed, the notification frequency adjustment unit may set the notification frequency low to avoid excessive notifications. Alternatively, if the user is relaxed, the notification frequency adjustment unit may provide notifications at a normal frequency. Furthermore, if the user is in a hurry, the system may provide only important notifications. This allows the system to provide an appropriate notification frequency according to the user's emotions and improve the efficiency of error response.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The collection unit collects system log data and performance data. For example, the collection unit can collect data such as system error logs, access logs, CPU usage, and memory usage. The collection unit can also use AI to adjust the timing of data collection. Step 2: The analysis unit analyzes the data collected by the collection unit to detect errors. For example, the analysis unit may use AI to analyze the collected data and detect errors by detecting specific error messages or abnormal performance indicators. The analysis unit may also dynamically adjust its error detection algorithms. Step 3: The reference section identifies the cause of the error detected by the analysis section. For example, the reference section uses AI to refer to past error data and similar cases to identify that a specific code section or misconfiguration is the cause. The reference section can also prioritize cause identification based on the frequency and impact of the error. Step 4: The proposal section proposes optimal countermeasures based on the causes identified by the reference section. For example, the proposal section uses AI to suggest countermeasures such as modifying settings, modifying code, or restarting the system. The proposal section can also suggest effective countermeasures by referencing past countermeasure data and best practices.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 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 identification processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 collection unit that collects system log data and performance data; an analysis unit that analyzes the data collected by the collection unit to detect errors; a reference unit that identifies the cause of the error detected by the analysis unit; a suggestion unit that proposes appropriate measures based on the cause identified by the reference unit; Equipped with A system characterized by:

2. The collecting unit Analyze user sentiment and adjust the timing of collecting log data and performance data based on the analyzed user sentiment.

2. The system of claim 1.

3. The collecting unit Dynamically change the type and amount of data collected based on system activity 2. The system of claim 1.

4. The collecting unit Start data collection based on a specific event or trigger 2. The system of claim 1.

5. The collecting unit Evaluate the reliability of collected data and filter out unreliable data 2. The system of claim 1.

6. The collecting unit Analyze user sentiment and prioritize data collection based on the analyzed user sentiment.

2. The system of claim 1.

7. The collecting unit Collect data based on the geographic location of your systems 2. The system of claim 1.

8. The collecting unit Capture communication data between different modules of the system 2. The system of claim 1.

Images