System

Abstract

An object of the system according to the embodiment is to automatically optimize the performance and the operation status of the system and efficiently perform troubleshooting.SOLUTION: A system according to an embodiment includes a metrics collection unit, an analysis unit, a knowledge utilization unit, and an automatic tuning unit. The metric collection unit collects a metric related to the performance or operation status of the system using a tool such as a generation AI. The analysis unit analyzes the metrics collected by the metrics collection unit. The knowledge application unit utilizes in-house and a plurality of pieces of technical knowledge as a database, and provides a system optimization method and a troubleshooting method. The automatic tuning unit automatically performs tuning and operation of the system based on the metrics analyzed by the analysis unit and the method provided by the knowledge utilization unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies often require manual optimization and troubleshooting of system performance and operational status, leaving room for improvement in efficiency.

[0005] The system according to the embodiment aims to automatically optimize the performance and operating status of the system and to efficiently perform troubleshooting. [Means for solving the problem]

[0006] A system according to an embodiment includes a metrics collection unit, an analysis unit, a knowledge utilization unit, and an automatic tuning unit. The metrics collection unit uses tools such as a generation AI to collect metrics related to the performance or operating status of the system. The analysis unit analyzes the metrics collected by the metrics collection unit. The knowledge utilization unit uses in-house and multiple technical knowledge as a database to provide system optimization methods and troubleshooting techniques. The automatic tuning unit automatically performs tuning and operation of the system based on the metrics analyzed by the analysis unit and the techniques provided by the knowledge utilization unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically optimize the performance and operating status of the system and efficiently perform troubleshooting. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) An automation system according to an embodiment of the present invention is a system that automates the tuning and operation of infrastructure systems and services by utilizing metrics from Generative AI, Google Gemini, Prometheus, and other technologies, as well as in-house and global technical knowledge, thereby enabling the automation system to efficiently and effectively operate and tune infrastructure systems and services.

[0029] An automation system according to an embodiment includes a metrics collection unit, an analysis unit, a knowledge utilization unit, and an auto-tuning unit. The metrics collection unit collects metrics related to system performance and operation status using tools such as a generation AI, Google Gemini, and Prometheus. For example, the metrics collection unit collects data such as CPU usage, memory usage, and network traffic. The metrics collection unit also monitors the system's operation status in real time and can immediately issue alerts if an abnormality occurs. The analysis unit analyzes the collected metrics. For example, the analysis unit uses a generation AI to analyze the metrics and understand the current system status. The analysis unit can also analyze user operation history and access logs to understand system usage patterns. The knowledge utilization unit utilizes a database of technical knowledge from within the company and around the world to provide system optimization methods and troubleshooting techniques. For example, the knowledge utilization unit analyzes past troubleshooting history and quickly proposes countermeasures when a similar problem occurs. The knowledge utilization unit can also periodically update the technical knowledge database and automatically incorporate new technologies and best practices. The automatic tuning unit automatically performs system tuning and operation based on the metrics analyzed by the analysis unit and the methods provided by the knowledge utilization unit. For example, the automatic tuning unit automatically scales servers according to the current load situation. The automatic tuning unit can also optimize not only system performance but also energy consumption. As a result, the automated system according to the embodiment can automatically optimize system performance and operating status and reduce operating costs. For example, the generation AI can automatically monitor the system load and add resources as needed to maintain system stability. Furthermore, utilizing technical knowledge enables rapid response when problems occur.

[0030] The analysis unit detects system anomalies in real time when collecting metrics, and can immediately issue an alert if an abnormality occurs. For example, the analysis unit has the generation AI monitor system metrics in real time, and immediately issues an alert when it detects an abnormal pattern. For example, an alert is issued if there is a sudden increase in CPU usage or an abnormal increase in network traffic. This allows system anomalies to be detected and addressed quickly.

[0031] When collecting metrics, the analysis unit analyzes not only the system's operating status but also user operation history or access logs, allowing it to understand system usage patterns. For example, the generation AI collects user operation history and access logs in addition to the system's operating status, and analyzes system usage patterns. For example, it can identify patterns of concentrated access during specific time periods. This allows it to understand system usage patterns and achieve optimal operation.

[0032] The metrics collection unit expands the scope of metrics collection and can also collect data from IoT devices or edge computing environments. For example, the metrics collection unit uses generative AI to collect metrics from IoT devices and analyze system performance. For example, it collects data from smart sensors and performs environmental monitoring. This allows data collection from IoT devices and edge computing environments to optimize system performance.

[0033] The analysis unit can analyze the correlation of metrics between different systems and provide insights for optimizing the performance of the entire system. For example, the generative AI can analyze the correlation of metrics between different systems and provide insights for optimizing the performance of the entire system. For example, it can analyze metrics from database systems and application servers to identify bottlenecks. This can provide insights for optimizing the performance of the entire system.

[0034] The knowledge utilization department analyzes past troubleshooting history and can quickly propose countermeasures when a similar problem occurs. For example, the knowledge utilization department uses a generation AI to analyze past troubleshooting history and quickly propose countermeasures when a similar problem occurs. For example, based on the history of network failures that have occurred in the past, the department can propose the optimal countermeasure when a similar failure occurs. This makes it possible to utilize past troubleshooting history and quickly propose countermeasures.

[0035] The knowledge utilization department can periodically update the database of technical knowledge and automatically incorporate new technologies or best practices. For example, the generative AI can periodically update the database of technical knowledge and automatically incorporate new technologies and best practices. For example, the latest security measures and performance improvement technologies can be added to the database. This allows the database of technical knowledge to be periodically updated and new technologies and best practices to be automatically incorporated.

[0036] The knowledge utilization department can share the database of technical knowledge with other companies or research institutions and jointly accumulate and utilize knowledge. For example, the generative AI can share the database of technical knowledge with other companies or research institutions and jointly accumulate and utilize knowledge. For example, the latest technical information can be shared through a joint research project. This allows technical knowledge to be shared with other companies or research institutions and jointly accumulate and utilize knowledge.

[0037] The knowledge utilization department can utilize technical knowledge to perform preventive maintenance on the system and implement measures before a failure occurs. For example, the generative AI can utilize technical knowledge to perform preventive maintenance on the system. For example, it can take measures before a failure occurs based on past failure data. Specifically, if a specific component is nearing the end of its lifespan, it can suggest replacing it in advance. This makes it possible to utilize technical knowledge to perform preventive maintenance on the system and implement measures before a failure occurs.

[0038] The auto-tuning unit can optimize not only system performance but also energy consumption. For example, the auto-tuning unit uses a generative AI to simultaneously optimize system performance and energy consumption. For example, it can optimize CPU usage while utilizing low power consumption modes to reduce energy consumption. This makes it possible to simultaneously optimize system performance and energy consumption.

[0039] The automatic tuning unit can provide a dashboard that visualizes the results of the automatic tuning and allows the operator to intuitively understand the results. For example, the automatic tuning unit provides a dashboard in which the generation AI visualizes the results of the automatic tuning, allowing the operator to intuitively understand the results. For example, the effects of tuning can be displayed in graphs or charts. This visualizes the results of the automatic tuning and allows the operator to intuitively understand the results.

[0040] The automatic tuning unit can expand the scope of automatic tuning to support cloud environments or hybrid environments. For example, the generation AI of the automatic tuning unit expands the scope of automatic tuning to cloud environments and optimizes cloud resources. For example, it automatically adjusts the scaling and resource allocation of cloud instances. This allows the scope of automatic tuning to be expanded to cloud environments and hybrid environments.

[0041] The auto-tuning unit can improve the auto-tuning algorithm and dynamically change the tuning parameters depending on the system usage status. For example, the generation AI can improve the auto-tuning algorithm and dynamically change the tuning parameters depending on the system usage status. For example, resources can be increased during peak times and reduced during off-peak times. This makes it possible to improve the auto-tuning algorithm and dynamically change the tuning parameters depending on the system usage status.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The analysis unit can take external factors such as natural disasters and power outages into account when analyzing system metrics. For example, it can collect weather data and predict the impact on system performance in the event of a natural disaster such as a typhoon or earthquake. It can also collect power outage information and predict the system's operating status in the event of a power outage. This makes it possible to analyze system performance while taking external factors into account, resulting in more accurate operation.

[0044] When detecting an anomaly in the system, the analysis unit can automatically suggest countermeasures according to the type of anomaly. For example, if a sudden increase in CPU usage is detected, it will suggest stopping unnecessary processes. Similarly, if an abnormal increase in network traffic is detected, it will suggest reallocating network bandwidth. This allows the system to quickly suggest appropriate countermeasures according to the type of anomaly.

[0045] When analyzing user operation history and access logs, the analysis unit can predict user behavior patterns and optimize system performance in advance. For example, it can predict patterns in which access will be concentrated during specific time periods and increase resources during those periods. Also, if a specific operation is performed frequently, it can secure resources related to that operation in advance. This makes it possible to predict user behavior patterns and optimize system performance in advance.

[0046] The metrics collection unit expands the scope of metrics collection and can also collect data from IoT devices or edge computing environments. For example, it can collect data from smart sensors to perform environmental monitoring. It can also collect data from edge devices and perform real-time performance analysis. This allows data collection from IoT devices and edge computing environments to optimize system performance.

[0047] The analysis unit analyzes the correlation of metrics between different systems and can provide insights to optimize overall system performance. For example, it analyzes metrics from database systems and application servers to identify bottlenecks. It also analyzes metrics from network infrastructure and cloud resources to propose optimal resource allocation. This provides insights to optimize overall system performance.

[0048] The Knowledge Utilization Department analyzes past troubleshooting history and can quickly propose countermeasures when similar problems occur. For example, based on the history of past network failures, it can propose the optimal countermeasure when a similar failure occurs. It can also analyze the history of past system crashes and propose measures to prevent recurrence. This allows it to utilize past troubleshooting history and quickly propose countermeasures.

[0049] The Knowledge Utilization Department regularly updates the technical knowledge database, enabling it to automatically incorporate new technologies or best practices. For example, it adds the latest security measures and performance improvement technologies to the database. Also, when new technologies emerge, they are automatically added to the database and notified to operators. This allows it to regularly update the technical knowledge database, enabling it to automatically incorporate new technologies and best practices.

[0050] The Knowledge Utilization Department can share its technical knowledge database with other companies or research institutions, and jointly accumulate and utilize knowledge. For example, it can share the latest technical information through joint research projects. It can also collaborate with other companies and research institutions to jointly update its technical knowledge database. This allows it to share technical knowledge with other companies and research institutions, and jointly accumulate and utilize knowledge.

[0051] The Knowledge Utilization Department can utilize technical knowledge to perform preventive maintenance on systems and implement measures before breakdowns occur. For example, based on past failure data, measures can be taken before breakdowns occur. Specifically, if a specific component is nearing the end of its lifespan, replacement can be suggested in advance. Regular maintenance schedules can also be proposed to maintain stable system operation. This allows the department to utilize technical knowledge to perform preventive maintenance on systems and implement measures before breakdowns occur.

[0052] The automatic tuning unit can optimize not only system performance but also energy consumption. For example, it utilizes low power consumption modes to reduce energy consumption while optimizing CPU usage. It also stops unnecessary processes to reduce energy consumption. This allows system performance and energy consumption to be optimized simultaneously.

[0053] The automatic tuning unit can provide a dashboard that visualizes the results of automatic tuning and allows operators to intuitively understand them. For example, it can display the effects of tuning in graphs and charts. It can also display real-time performance data, allowing operators to immediately grasp the situation. This makes it possible to visualize the results of automatic tuning and allow operators to intuitively understand them.

[0054] The auto-tuning unit can expand the scope of auto-tuning to support cloud or hybrid environments. For example, it automatically adjusts the scaling and resource allocation of cloud instances. It also optimally allocates on-premise and cloud resources in hybrid environments. This allows the scope of auto-tuning to be expanded to cloud and hybrid environments.

[0055] The auto-tuning unit improves the auto-tuning algorithm and can dynamically change tuning parameters according to system usage. For example, it can increase resources during peak times and reduce resources during off-peak times. Also, if a specific application has a high load, it can concentrate resources on that application. This allows the auto-tuning algorithm to be improved and tuning parameters to be dynamically changed according to system usage.

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

[0057] Step 1: The metrics collector uses tools such as Generative AI, Google Gemini, and Prometheus to collect metrics related to the system's performance and operating status. For example, the metrics collector collects data such as CPU usage, memory usage, and network traffic. The metrics collector can also monitor the system's operating status in real time and immediately issue alerts if an abnormality occurs. Step 2: The analysis unit analyzes the collected metrics. For example, the analysis unit uses generation AI to analyze the metrics and understand the current state of the system. The analysis unit can also analyze user operation history and access logs to understand system usage patterns. Step 3: The Knowledge Utilization Department uses a database of technical knowledge from within the company and around the world to provide system optimization and troubleshooting techniques. For example, the Knowledge Utilization Department analyzes past troubleshooting history and quickly proposes countermeasures when similar problems occur. The Knowledge Utilization Department can also regularly update the technical knowledge database to automatically incorporate new technologies and best practices. Step 4: The automatic tuning unit automatically tunes and operates the system based on the metrics analyzed by the analysis unit and the methods provided by the knowledge utilization unit. For example, the automatic tuning unit automatically scales the server according to the current load situation. The automatic tuning unit can also optimize not only system performance but also energy consumption.

[0058] (Example 2) An automation system according to an embodiment of the present invention is a system that automates the tuning and operation of infrastructure systems and services by utilizing metrics from Generative AI, Google Gemini, Prometheus, and other technologies, as well as in-house and global technical knowledge, thereby enabling the automation system to efficiently and effectively operate and tune infrastructure systems and services.

[0059] An automation system according to an embodiment includes a metrics collection unit, an analysis unit, a knowledge utilization unit, and an auto-tuning unit. The metrics collection unit collects metrics related to system performance and operation status using tools such as a generation AI, Google Gemini, and Prometheus. For example, the metrics collection unit collects data such as CPU usage, memory usage, and network traffic. The metrics collection unit also monitors the system's operation status in real time and can immediately issue alerts if an abnormality occurs. The analysis unit analyzes the collected metrics. For example, the analysis unit uses a generation AI to analyze the metrics and understand the current system status. The analysis unit can also analyze user operation history and access logs to understand system usage patterns. The knowledge utilization unit utilizes a database of technical knowledge from within the company and around the world to provide system optimization methods and troubleshooting techniques. For example, the knowledge utilization unit analyzes past troubleshooting history and quickly proposes countermeasures when a similar problem occurs. The knowledge utilization unit can also periodically update the technical knowledge database and automatically incorporate new technologies and best practices. The automatic tuning unit automatically performs system tuning and operation based on the metrics analyzed by the analysis unit and the methods provided by the knowledge utilization unit. For example, the automatic tuning unit automatically scales servers according to the current load situation. The automatic tuning unit can also optimize not only system performance but also energy consumption. As a result, the automated system according to the embodiment can automatically optimize system performance and operating status and reduce operating costs. For example, the generation AI can automatically monitor the system load and add resources as needed to maintain system stability. Furthermore, utilizing technical knowledge enables rapid response when problems occur.

[0060] The analysis unit detects system anomalies in real time when collecting metrics, and can immediately issue an alert if an abnormality occurs. For example, the analysis unit has the generation AI monitor system metrics in real time, and immediately issues an alert when it detects an abnormal pattern. For example, an alert is issued if there is a sudden increase in CPU usage or an abnormal increase in network traffic. This allows system anomalies to be detected and addressed quickly.

[0061] When collecting metrics, the analysis unit analyzes not only the system's operating status but also user operation history or access logs, allowing it to understand system usage patterns. For example, the generation AI collects user operation history and access logs in addition to the system's operating status, and analyzes system usage patterns. For example, it can identify patterns of concentrated access during specific time periods. This allows it to understand system usage patterns and achieve optimal operation.

[0062] The analysis unit uses the emotion estimation function to monitor the stress level of the system operator and can automatically suggest measures to reduce the load if stress increases. For example, the analysis unit uses the generative AI emotion estimation function to monitor the stress level of the system operator. For example, it analyzes the operator's facial expressions and voice to estimate the stress level. This reduces the stress of the system operator and enables efficient operation.

[0063] The metrics collection unit expands the scope of metrics collection and can also collect data from IoT devices or edge computing environments. For example, the metrics collection unit uses generative AI to collect metrics from IoT devices and analyze system performance. For example, it collects data from smart sensors and performs environmental monitoring. This allows data collection from IoT devices and edge computing environments to optimize system performance.

[0064] The analysis unit can analyze the correlation of metrics between different systems and provide insights for optimizing the performance of the entire system. For example, the generative AI can analyze the correlation of metrics between different systems and provide insights for optimizing the performance of the entire system. For example, it can analyze metrics from database systems and application servers to identify bottlenecks. This can provide insights for optimizing the performance of the entire system.

[0065] The analysis unit can use the emotion estimation function to adjust system performance based on the user's emotional state, thereby improving the user experience. For example, the analysis unit uses the emotion estimation function to monitor the user's emotional state and adjust system performance. For example, if the user is feeling stressed, the system response can be made faster. This allows system performance to be adjusted based on the user's emotional state, thereby improving the user experience.

[0066] The knowledge utilization department analyzes past troubleshooting history and can quickly propose countermeasures when a similar problem occurs. For example, the knowledge utilization department uses a generation AI to analyze past troubleshooting history and quickly propose countermeasures when a similar problem occurs. For example, based on the history of network failures that have occurred in the past, the department can propose the optimal countermeasure when a similar failure occurs. This makes it possible to utilize past troubleshooting history and quickly propose countermeasures.

[0067] The knowledge utilization department can periodically update the database of technical knowledge and automatically incorporate new technologies or best practices. For example, the generative AI can periodically update the database of technical knowledge and automatically incorporate new technologies and best practices. For example, the latest security measures and performance improvement technologies can be added to the database. This allows the database of technical knowledge to be periodically updated and new technologies and best practices to be automatically incorporated.

[0068] The knowledge utilization unit uses the emotion estimation function to evaluate the operator's level of understanding when providing technical knowledge, and can provide supplementary information if the understanding is insufficient. For example, the generation AI uses the emotion estimation function to evaluate the operator's level of understanding when providing technical knowledge. For example, it analyzes the operator's facial expressions and voice, and provides supplementary information if the operator's level of understanding is low. In this way, by evaluating the operator's level of understanding and providing supplementary information, it is possible to provide technical knowledge effectively.

[0069] The knowledge utilization department can share the database of technical knowledge with other companies or research institutions and jointly accumulate and utilize knowledge. For example, the generative AI can share the database of technical knowledge with other companies or research institutions and jointly accumulate and utilize knowledge. For example, the latest technical information can be shared through a joint research project. This allows technical knowledge to be shared with other companies or research institutions and jointly accumulate and utilize knowledge.

[0070] The knowledge utilization department can utilize technical knowledge to perform preventive maintenance on the system and implement measures before a failure occurs. For example, the generative AI can utilize technical knowledge to perform preventive maintenance on the system. For example, it can take measures before a failure occurs based on past failure data. Specifically, if a specific component is nearing the end of its lifespan, it can suggest replacing it in advance. This makes it possible to utilize technical knowledge to perform preventive maintenance on the system and implement measures before a failure occurs.

[0071] The knowledge utilization unit can use the emotion estimation function to consider the user's emotional state when providing technical knowledge and suggest methods to reduce stress. For example, the generation AI uses the emotion estimation function to monitor the user's emotional state when providing technical knowledge and suggest approaches to reduce stress. For example, if the user is feeling stressed, a concise and easy-to-understand explanation is provided. This makes it possible to consider the user's emotional state and suggest approaches to reduce stress.

[0072] The auto-tuning unit can optimize not only system performance but also energy consumption. For example, the auto-tuning unit uses a generative AI to simultaneously optimize system performance and energy consumption. For example, it can optimize CPU usage while utilizing low power consumption modes to reduce energy consumption. This makes it possible to simultaneously optimize system performance and energy consumption.

[0073] The automatic tuning unit can provide a dashboard that visualizes the results of the automatic tuning and allows the operator to intuitively understand the results. For example, the automatic tuning unit provides a dashboard in which the generation AI visualizes the results of the automatic tuning, allowing the operator to intuitively understand the results. For example, the effects of tuning can be displayed in graphs or charts. This visualizes the results of the automatic tuning and allows the operator to intuitively understand the results.

[0074] The auto-tuning unit uses the emotion estimation function to adjust the frequency and timing of tuning based on the emotional state of the operator, thereby reducing the burden on the operator. For example, the generation AI uses the emotion estimation function to monitor the emotional state of the operator and adjust the frequency and timing of tuning. For example, if the operator is feeling stressed, the tuning frequency is reduced. This adjusts the frequency and timing of tuning based on the emotional state of the operator, thereby reducing the burden on the operator.

[0075] The automatic tuning unit can expand the scope of automatic tuning to support cloud environments or hybrid environments. For example, the generation AI of the automatic tuning unit expands the scope of automatic tuning to cloud environments and optimizes cloud resources. For example, it automatically adjusts the scaling and resource allocation of cloud instances. This allows the scope of automatic tuning to be expanded to cloud environments and hybrid environments.

[0076] The auto-tuning unit can improve the auto-tuning algorithm and dynamically change the tuning parameters depending on the system usage status. For example, the generation AI can improve the auto-tuning algorithm and dynamically change the tuning parameters depending on the system usage status. For example, resources can be increased during peak times and reduced during off-peak times. This makes it possible to improve the auto-tuning algorithm and dynamically change the tuning parameters depending on the system usage status.

[0077] The auto-tuning unit can use the emotion estimation function to tune the system based on the user's emotional state and optimize the user experience. For example, the generation AI uses the emotion estimation function to monitor the user's emotional state and tune the system. For example, if the user is feeling stressed, the system response can be made faster. This allows the system to be tuned based on the user's emotional state and optimize the user experience.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The analysis unit can take external factors such as natural disasters and power outages into account when analyzing system metrics. For example, it can collect weather data and predict the impact on system performance in the event of a natural disaster such as a typhoon or earthquake. It can also collect power outage information and predict the system's operating status in the event of a power outage. This makes it possible to analyze system performance while taking external factors into account, resulting in more accurate operation.

[0080] When detecting an anomaly in the system, the analysis unit can automatically suggest countermeasures according to the type of anomaly. For example, if a sudden increase in CPU usage is detected, it will suggest stopping unnecessary processes. Similarly, if an abnormal increase in network traffic is detected, it will suggest reallocating network bandwidth. This allows the system to quickly suggest appropriate countermeasures according to the type of anomaly.

[0081] When analyzing user operation history and access logs, the analysis unit can predict user behavior patterns and optimize system performance in advance. For example, it can predict patterns in which access will be concentrated during specific time periods and increase resources during those periods. Also, if a specific operation is performed frequently, it can secure resources related to that operation in advance. This makes it possible to predict user behavior patterns and optimize system performance in advance.

[0082] The analysis unit uses the emotion estimation function to monitor the stress level of the system operator and automatically proposes measures to reduce the load if stress increases. For example, it can analyze the operator's facial expressions and voice to estimate the stress level. If the operator is feeling stressed, it can propose stopping unnecessary processes and reallocating resources to reduce the load on the system. This reduces the stress of the system operator and enables efficient operation.

[0083] The metrics collection unit expands the scope of metrics collection and can also collect data from IoT devices or edge computing environments. For example, it can collect data from smart sensors to perform environmental monitoring. It can also collect data from edge devices and perform real-time performance analysis. This allows data collection from IoT devices and edge computing environments to optimize system performance.

[0084] The analysis unit analyzes the correlation of metrics between different systems and can provide insights to optimize overall system performance. For example, it analyzes metrics from database systems and application servers to identify bottlenecks. It also analyzes metrics from network infrastructure and cloud resources to propose optimal resource allocation. This provides insights to optimize overall system performance.

[0085] The analysis unit can use the emotion estimation function to adjust system performance based on the user's emotional state, thereby improving the user experience. For example, if the user is feeling stressed, the system response can be made faster. On the other hand, if the user is relaxed, the system performance can be returned to normal mode. In this way, the system performance can be adjusted based on the user's emotional state, thereby improving the user experience.

[0086] The Knowledge Utilization Department analyzes past troubleshooting history and can quickly propose countermeasures when similar problems occur. For example, based on the history of past network failures, it can propose the optimal countermeasure when a similar failure occurs. It can also analyze the history of past system crashes and propose measures to prevent recurrence. This allows it to utilize past troubleshooting history and quickly propose countermeasures.

[0087] The Knowledge Utilization Department regularly updates the technical knowledge database, enabling it to automatically incorporate new technologies or best practices. For example, it adds the latest security measures and performance improvement technologies to the database. Also, when new technologies emerge, they are automatically added to the database and notified to operators. This allows it to regularly update the technical knowledge database, enabling it to automatically incorporate new technologies and best practices.

[0088] The knowledge utilization department can use the emotion estimation function to evaluate the operator's level of understanding when providing technical knowledge and provide supplementary information if understanding is insufficient. For example, it can analyze the operator's facial expressions and voice and provide supplementary information if the operator's level of understanding is low. It also uses visual aids and videos to provide information in a format that is easy for operators to understand. This makes it possible to evaluate the operator's level of understanding and provide supplementary information, thereby achieving effective provision of technical knowledge.

[0089] The Knowledge Utilization Department can share its technical knowledge database with other companies or research institutions, and jointly accumulate and utilize knowledge. For example, it can share the latest technical information through joint research projects. It can also collaborate with other companies and research institutions to jointly update its technical knowledge database. This allows it to share technical knowledge with other companies and research institutions, and jointly accumulate and utilize knowledge.

[0090] The Knowledge Utilization Department can utilize technical knowledge to perform preventive maintenance on systems and implement measures before breakdowns occur. For example, based on past failure data, measures can be taken before breakdowns occur. Specifically, if a specific component is nearing the end of its lifespan, replacement can be suggested in advance. Regular maintenance schedules can also be proposed to maintain stable system operation. This allows the department to utilize technical knowledge to perform preventive maintenance on systems and implement measures before breakdowns occur.

[0091] The knowledge utilization unit can use the emotion estimation function to consider the user's emotional state when providing technical knowledge and suggest stress reduction methods. For example, if the user is feeling stressed, it can provide a concise and easy-to-understand explanation. On the other hand, if the user is relaxed, it can provide detailed technical information. This makes it possible to consider the user's emotional state and suggest approaches to reduce stress.

[0092] The automatic tuning unit can optimize not only system performance but also energy consumption. For example, it utilizes low power consumption modes to reduce energy consumption while optimizing CPU usage. It also stops unnecessary processes to reduce energy consumption. This allows system performance and energy consumption to be optimized simultaneously.

[0093] The automatic tuning unit can provide a dashboard that visualizes the results of automatic tuning and allows operators to intuitively understand them. For example, it can display the effects of tuning in graphs and charts. It can also display real-time performance data, allowing operators to immediately grasp the situation. This makes it possible to visualize the results of automatic tuning and allow operators to intuitively understand them.

[0094] The automatic tuning unit uses the emotion estimation function to adjust the frequency and timing of tuning based on the emotional state of the operator, thereby reducing the burden on the operator. For example, if the operator is feeling stressed, the frequency of tuning is reduced. On the other hand, if the operator is relaxed, the frequency of tuning is increased. In this way, the frequency and timing of tuning can be adjusted based on the emotional state of the operator, thereby reducing the burden on the operator.

[0095] The auto-tuning unit can expand the scope of auto-tuning to support cloud or hybrid environments. For example, it automatically adjusts the scaling and resource allocation of cloud instances. It also optimally allocates on-premise and cloud resources in hybrid environments. This allows the scope of auto-tuning to be expanded to cloud and hybrid environments.

[0096] The auto-tuning unit improves the auto-tuning algorithm and can dynamically change tuning parameters according to system usage. For example, it can increase resources during peak times and reduce resources during off-peak times. Also, if a specific application has a high load, it can concentrate resources on that application. This allows the auto-tuning algorithm to be improved and tuning parameters to be dynamically changed according to system usage.

[0097] The automatic tuning unit uses the emotion estimation function to tune the system based on the user's emotional state and optimize the user experience. For example, if the user is feeling stressed, the system response can be made faster. On the other hand, if the user is relaxed, the system performance can be returned to normal mode. In this way, the system can be tuned based on the user's emotional state and the user experience can be optimized.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The metrics collector uses tools such as Generative AI, Google Gemini, and Prometheus to collect metrics related to the system's performance and operating status. For example, the metrics collector collects data such as CPU usage, memory usage, and network traffic. The metrics collector can also monitor the system's operating status in real time and immediately issue alerts if an abnormality occurs. Step 2: The analysis unit analyzes the collected metrics. For example, the analysis unit uses generation AI to analyze the metrics and understand the current state of the system. The analysis unit can also analyze user operation history and access logs to understand system usage patterns. Step 3: The Knowledge Utilization Department uses a database of technical knowledge from within the company and around the world to provide system optimization and troubleshooting techniques. For example, the Knowledge Utilization Department analyzes past troubleshooting history and quickly proposes countermeasures when similar problems occur. The Knowledge Utilization Department can also regularly update the technical knowledge database to automatically incorporate new technologies and best practices. Step 4: The automatic tuning unit automatically tunes and operates the system based on the metrics analyzed by the analysis unit and the methods provided by the knowledge utilization unit. For example, the automatic tuning unit automatically scales the server according to the current load situation. The automatic tuning unit can also optimize not only system performance but also energy consumption.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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, in order to avoid confusion and to 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.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 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 metrics collection unit that uses tools such as generative AI to collect metrics related to the performance or operating status of the system; an analysis unit that analyzes the metrics collected by the metrics collection unit; The Knowledge Utilization Department utilizes the company's internal and multiple technical knowledge as a database to provide system optimization methods and troubleshooting techniques. an automatic tuning unit that automatically performs tuning and operation of the system based on the metrics analyzed by the analysis unit and the method provided by the knowledge utilization unit. A system characterized by:

2. The analysis unit When collecting the metrics, the system detects anomalies in real time and issues an immediate alert if an anomaly occurs.

2. The system of claim 1.

3. The metrics collection unit Expand the collection of these metrics to include data from these IoT devices or edge computing environments.

2. The system of claim 1.

4. The knowledge utilization department Analyze past troubleshooting history and quickly suggest solutions when similar problems occur 2. The system of claim 1.

5. The automatic tuning unit Optimizing not only the performance of the system but also its energy consumption 2. The system of claim 1.

6. The analysis unit Monitors the stress level of system operators and automatically suggests load reduction measures if stress increases 2. The system of claim 1.

7. The knowledge utilization department When providing the technical knowledge, evaluate the operator's level of understanding and provide supplementary information if the operator's understanding is insufficient.

2. The system of claim 1.

8. The automatic tuning unit The frequency and timing of the tuning are adjusted based on the emotional state of the operator, thereby reducing the burden on the operator.

2. The system of claim 1.

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